The Semantic Web changes the way we deal with data, because assumptions about the nature of the data that we deal with differ substantially from the ones in established database approaches. Semantic Web data is (i) provided by different people in an ad-hoc manner, (ii) distributed, (iii) semi-structured, (iv) (more or less) typed, (v) supposed to be used serendipitously. In fact, these are highly relevant assumptions and challenges, because they are frequently encountered in all kind of data-centric challenges – also in cases where Semantic Web standards are not in use. However, they are only partially accounted for in existing programming approaches for Semantic Web data including (i) semantic search, (ii) graph programming, and (iii) traditional database programming approaches. The main hypothesis of this talk is that we have not yet developed the right kind of programming paradigms to deal with the proper nature of Semantic Web data, because none of the mentioned approaches fully considers its characteristics. Thus, I want to outline empirical investigations of Semantic Web data and recent developments towards Semantic Web programming that target the reduction of the impedance mismatch between data engineering and programming approaches.

The lecture is divided into four parts. In the first part, I offer a brief and simple introduction to four well-known senses in which different scientific fields speak of complexity, namely state complexity, Kolmogorov complexity, computational complexity, and programming complexity. I then suggest an intuitive way in which they can all be linked in a conceptual, unified view. Against this background, in the second part, I outline a new concept of complexity, which I shall call coordination complexity. This completes the unified view. I then argue, in the third part, that the semantic web helps us dealing with problems with increasingly high degree of coordination complexity, which require the mobilisation of whole systems to be tackled. In the last and concluding part, I highlight one of the consequences of the resolution of problems with high degree of coordination complexity: the predictability and manipulability of autonomous choices.

Combining Statistics and Semantics to Turn Data into Knowledge

Local Organization and Conference Administration

Graphium Chrysalis: Exploiting Graph Database Engines to Analyze RDF Graphs

Collaborative Semantic Management and Automated Analysis of Scientific Literature

SemLAV: Querying Deep Web and Linked Open Data with SPARQL

Olap4LD - A Framework for Building Analysis Applications over Governmental Statistics

VideoLecturesMashup: using media fragments and semantic annotations to enable topic-centred e-learning

A Rule-Based System for Monitoring of Microblogging Disease Reports

SparqlFilterFlow: SPARQL Query Composition for Everyone

The ProtegeVOWL Plugin: Ontology Visualization for Everyone

Visualizing RDF Data Cubes using the Linked Data Visualization Model

SmarT INsiGhts (STING) - An Intelligence Application for Querying Heterogeneous Databases

Durchblick - A Conference Assistance System for Augmented Reality Devices

FAGI-tr: A tool for aligning geospatial RDF vocabularies

Painless URI dereferencing using The DataTank

Aether - Generating and Viewing Extended VoID Statistical Descriptions of RDF Datasets

Combining a REST Lexical Analysis Web Service with SPARQL for Mashup Semantic Annotation from Text

Geographic Summaries from Crowdsourced Data

CORNER: A Completeness Reasoner for SPARQL Queries over RDF Data Sources
CORNER: A Completeness Reasoner for the Semantic Web

SPARQL SAHA, a Configurable Linked Data Editor and Browser as a Service

Towards a Semantic Web Platform for Finite Element Simulations

dime: A Context-Aware Information System

Boosting QAKiS with multimedia answer visualization

Dendro: collaborative research data management built on linked open data

TRTML - A Tripleset Recommendation Tool based on Supervised Learning Algorithm

LinkLion: A Link Repository for the Web of Data

Distant Supervision for Relation Extraction using Ontology Class Hierarchy-Based Features

IDE Integrated RDF Exploration, Access and RDF-based Code Typing with LITEQ

AnnoMarket - Multilingual Text Analytics at Scale on the Cloud

Kuphi - An Investigation Tool for Searching for and via Semantic Relations

Making use of Linked Data for Generating Enhanced Snippets

LinkZoo: A linked data platform for collaborative management of heterogeneous resources

http://www.w3.org/1999/02/22-rdf-syntax-ns#SynopsViz - A Framework for Hierarchical Linked Data Visual Exploration and Analysis
rdf:SynopsViz - A Framework for Hierarchical Linked Data Visual Exploration and Analysis

Augmenting TV Newscasts via Entity Expansion

Analyzing Linked Data Quality with LiQuate

Big, Linked and Open Data: Applications in the German Aerospace Center

morph-LDP: An R2RML-based Linked Data Platform implementation

Prod-Trees: Semantic Search for Earth Observation Products

ODCleanStore: An ETL Framework for Sustainable RDF Data Processing

Using semantic technologies for scalable multi-channel communication

SCS Connector - Quantifying and Visualising Semantic Paths between Entity Pairs

Browsing DBpedia Entities with Summaries

A Demonstration of a Natural Language Query Interface to an Event-Based Semantic Web Triplestore

Matware: Constructing and Exploiting Domain Specific Warehouses by Aggregating Semantic Data
In many applications one has to fetch and assemble pieces of information coming from more than one web sources such as SPARQL endpoints. In this paper we describe the corresponding requirements and challenges, and then we present a process and a tool that we have developed (called MatWare) for constructing such semantic warehouses. We focus on domain-specific warehouses, where the focus is given on the aspects of quality, value and freshness. Subsequently, we present MatWare (MATerialized WAreshouse), a tool that we have developed for automating the construction of such warehouses, for assessing their value and for automating their reconstruction. Finally we report our experiences from using it for building, maintaining and evolving an operational semantic warehouse for the marine domain, that is currently in use by several applications ranging from e-infrastructure services to smart phone applications.

How to Best Find a Partner? An Evaluation of Editing Approaches to Construct R2RML Mappings
R2RML defines a language to express mappings from relational data to RDF. That way, applications built on top of the W3C Semantic Technology stack can seamlessly integrate relational data using those mappings. A major obstacle to using R2RML, though, is the effort for manually curating the mappings. In particular in scenarios that aim to map data from huge and complex relational schemata to more abstract ontologies efficient ways to support the mapping creation are needed. In previous work we presented a mapping editor that aims to reduce the effort that users need to invest for mapping creation. While assisting users in mapping construction the editor also imposed a fixed editing approach, which turned out to be not optimal for all users and all kinds of mapping tasks. Most prominently, it is unclear on which of the two data models users should best start with the mapping construction. In this paper, we present the results of a comprehensive user study that evaluates different alternative editing approaches for constructing R2RML mapping rules. In the user study we measure the efficiency and quality of the mapping construction to find out which approach works better for users with different background knowledge and for different types of tasks in order to extend our editor.

Towards Portable Shopping Histories: Using GoodRelations to Expose Ownership Information to E-Commerce Sites
Recommender systems are an important technology component for many e-commerce applications. In short, they are technical means that suggest potentially relevant products and services to the users of a Web site, typically a shop. The recommendations are computed in advance or during the actual visit and use various types of data as input, in particular past purchases and the purchasing behavior of other users with similar preferences. One major problem with recommender systems is that the quality of recommendations depends on the amount, quality, and representativeness of the in-formation about items already owned by the visitor, e.g. from past purchases at that particular shop. For first-time visitors and customers migrating from other merchants, the amount of available information is often too small to generate good recommendations. In other words, shopping history data for a single user is fragmented and spread over multiple sites, and cannot be actively exposed by the user himself to additional shops. This creates strong lock-in effects for consumers, because they cannot migrate from one merchant to another without losing access to high-quality recommendations, and it creates strong market entry barriers for new merchants, since they do not have access to the customers’ shopping history with other shops. In this paper, we propose to use Semantic Web technology, namely GoodRelations and schema.org, to empower e-commerce customers to (1) collect and manage ownership information about products, (2) advertise interest in sharing this information with shop sites in exchange for better recommendations or oth-er incentives, and (3) expose the information to such shop sites directly from their browser. We then sketch how a shop site could use the ownership infor-mation to propose relevant products.

Predicting Severity of Road Traffic Congestion using Semantic Web Technologies
Predictive reasoning, or the problem of estimating future observations given some historical information, is an important inference task for obtaining insight on cities and supporting efficient urban planning. This paper, focusing on transportation, presents how severity of road traffic congestion can be predicted using semanticWeb technologies. In particular we present a system which integrates numerous sensors (exposing heterogenous, exogenous and raw data streams such as weather information, road works, city events or incidents) to improve accuracy and consistency of traffic congestion prediction. Our prototype of semantics-aware prediction, being used and experimented currently by traffic controllers in Dublin City Ireland, works efficiently with real, live and heterogeneous stream data. The experiments have shown accurate and consistent prediction of road traffic conditions, main benefits of the semantic encoding.

Accepting the XBRL Challenge with Linked Data for Financial Data Integration
Analysts spend a disproportionate amount of time with financial data curation before they are able to compare company performances in an analysis. The Extensible Business Reporting Language (XBRL) for annotating financial facts is suited for automatic processing to increase information quality in financial analytics. Still, XBRL does not solve the problem of data integration as required for a holistic view on companies. Semantic Web technologies promise benefits for financial data integration, yet, existing literature lacks concrete case studies. In this paper, we present the Financial Information Observation System (FIOS) that uses Linked Data and multidimensional modelling based on the RDF Data Cube Vocabulary for accessing and representing relevant financial data. FIOS fulfils the information seeking mantra of ``overview first, zoom and filter, then details on demand'', integrates yearly and quarterly balance sheets, daily NASDAQ stock quotes as well as company and industry background information and helps analysts creating their own analyses with Excel-like functionality.

PCS2OWL: A Generic Approach for Deriving Web Ontologies from Product Classification Systems
The classification of products and services enables reliable and efficient electronic exchanges of product data across organizations. Many companies classify products (a) according to generic or industry-specific product classification standards, or (b) by using proprietary category systems. Such classification systems often contain thousands of product classes that are updated at regular intervals. This implies a large quantity of useful information which the e-commerce Web of Data could immediately benefit from. Thus, instead of building up product ontologies from scratch, which is costly, tedious, error-prone, and high-maintenance, it is generally easier to derive them from existing classifications. In this paper, we (1) describe a generic, semi-automated method for deriving OWL ontologies from product classification standards and proprietary category systems. Moreover, we (2) show that our approach generates logically and semantically correct vocabularies, and (3) present the practical benefit of our approach. The resulting product ontologies are compatible with the GoodRelations vocabulary for e-commerce and with schema.org and can be used to enrich product and offer descriptions on the Semantic Web, allowing for better product searches.

Seeding Structured Data by Default via Open Source Library Systems
Most libraries use the machine-readable cataloguing (MARC) format to encode and exchange metadata about the items they make available to their patrons. Traditional library systems have not published this data on the Semantic Web. However, some agile open source library systems have begun closing this gap by publishing structured data that uses the schema.org vocabulary to describe the bibliographic data, make offers for items available for loan, and link the items to their owning libraries. This article distills the lessons learned from implementing structured data in Evergreen, Koha, and VuFind; highlights emerging design patterns for publishing structured data in other library systems; and traces the influence these implementation experiences have had on the evolution of the schema.org vocabulary. Finally, we discuss the impact that "the power of the default" publishing of structured data could have on discoverability of library offerings on the Semantic Web.

conTEXT -- Lightweight Text Analytics using Linked Data
The Web democratized publishing -- everybody can easily publish information on a Website, Blog, in social networks or microblogging systems. The more the amount of published information grows, the more important are technologies for accessing, analysing, summarising and visualising information. While substantial progress has been made in the last years in each of these areas individually, we argue, that only the intelligent combination of approaches will make this progress truly useful and leverage further synergies between techniques. In this paper we develop a text analytics architecture of participation, which allows ordinary people to use sophisticated NLP techniques for analysing and visualizing their content, be it a Blog, Twitter feed, Website or article collection. The architecture comprises interfaces for information access, natural language processing and visualization. Different exchangeable components can be plugged into this architecture, making it easy to tailor for individual needs. We evaluate the usefulness of our approach by comparing both the effectiveness and efficiency of end users within a task-solving setting. Moreover, we evaluate the usability of our approach using a questionnaire-driven approach. Both evaluations suggest that ordinary Web users are empowered to analyse their data and perform tasks, which were previously out of reach.

Best practices and lessons learnt from using the Semantic Web and Linked
Data

Ontology-driven applications and ontologies for semantic technologies

Semantic data mining (data mining approaches that use ontologies as background
knowledge)

Semantic discovery and composition planning of services
"Semantics Inside!" But let's not tell the Data Miners: Intelligent Support for Data Mining
Knowledge Discovery in Databases (KDD) has evolved significantly over the past years and reached a mature stage offering plenty of operators to solve complex data analysis tasks. User support for building data analysis workflows, however, has not progressed sufficiently: the large number of operators currently available in KDD systems and interactions between these operators complicates successful data analysis. To help Data Miners we enhanced one of the most used open source data mining tools---RapidMiner---with semantic technologies. Specifically, we first annotated all elements involved in the Data Mining (DM) process---the data, the operators, models, data mining tasks, and KDD workflows---semantically using our eProPlan modelling tool that allows to describe operators and build a task/method decomposition grammar to specify the desired workflows embedded in an ontology. Second, we enhanced RapidMiner to employ these semantic annotations to actively support data analysts. Third, we built an Intelligent Discovery Assistant, eIDA, that leverages the semantic annotation as well as HTN planning to automatically support KDD process generation. We found that the use of Semantic Web approaches and technologies in the KDD domain helped us to lower the barrier to data analysis. We also found that using a generic ontology editor overwhelmed KDD-centric users. We, therefore, provided them with problem-centric extensions to protege. Last and most surprising, we found that our semantic modeling of the KDD domain served as a rapid prototyping approach for several hard-coded improvements of RapidMiner, namely correctness checking of workflows and quick-fixes, reinforcing the finding that even a little semantic modeling can go a long way in improving the understanding of a domain even for domain experts.

Solvonauts : an open educational search engine

TuvaLabs – Data Literacy Skills for a Brighter Future

DBLPXplorer: Interactive Graphical Interfaces for the Computer Science Bibliography

The electronic Discharge Letter (eDL) mobile app

LODStories: Learning About Art by Building Multimedia Stories

AGRIS – exploiting bibliographic records to create rich Linked Open Data page

Konnektid – Social Learning Anytime, Anywhere

Visualization of Labour Conflicts in the Netherlands for last 700 years

An Approach for Incremental Entity Resolution at the Example of Social Media Data

SEMashup - Making Use of Linked Data for Generating Enhanced Snippets

Augmented Participation to Live Events through Social Network Content Enrichment

conTEXT - A Mashup platform for Lightweight Text Analytics

Designing an integrated semantic framework for structured opinion summarization

Predicting SPARQL Query Performance and Explaining Linked Data

Semantic Information Extraction on Domain Specific Data Sheets

On The Discovery of Relational Patterns in Semantically Similar Annotated Linked Data

Searching Linked Data and Services with a Single Query

Metrics-driven Framework for LOD Quality Assessment

Automatic Detection and Semantic Formalisation of Business Rules

Towards a Data Warehouse fed with Web Services

Combining Linked Data and Statistical Information Retrieval

Harvesting and Structuring Social Data in Music Information Retrieval

Ranking Entities in a Large Semantic Network

Improving the online visibility of touristic service providers by using semantic annotations

A companion screen application for TV broadcasts annotated with Linked Open Data

A Semantic Web Based Core Engine to Efficiently Perform Sentiment Analysis

Linked Data Finland: A 7-star Model and Platform for Publishing and Re-using Linked Datasets

A Semantic Approach to Support Cross Border e-Justice

Balloon Synopsis: A Modern Node-Centric RDF Viewer and Browser for the Web

Annotating Ontologies with Descriptions of Vagueness

Rapid Deployment of a RESTful Service for Data Collected by Oceanographic Research Cruises

SPARQL-MM - Extending SPARQL to Media Fragments

RuQAR : Reasoning Framework for OWL 2 RL Ontologies

An Investigation of HTTP Header Information for Detecting Changes of Linked Open Data Sources

TMR: A Semantic Recommender System using Topic Maps on the Items' Descriptions

Data Cleansing Consolidation with PatchR

What are the Important Properties of an Entity? Comparing Users and Knowledge Graph Point of View

Trusty URIs: Verifiable, Immutable, and Permanent Digital Artifacts for Linked Data
To make digital resources on the web verifiable, immutable, and permanent, we propose a technique to include cryptographic hash values in URIs. We call them trusty URIs and we show how they can be used for approaches like nanopublications to make not only specific resources but their entire reference trees verifiable. Digital artifacts can be identified not only on the byte level but on more abstract levels such as RDF graphs, which means that resources keep their hash values even when presented in a different format. Our approach sticks to the core principles of the web, namely openness and decentralized architecture, is fully compatible with existing standards and protocols, and can therefore be used right away. Evaluation of our reference implementations shows that these desired properties are indeed accomplished by our approach, and that it remains practical even for very large files.

Leveraging Distributed Human Computation and Consensus Partition for Entity Coreference
Entity coreference is important to Linked Data integration. User involvement is considered as a valuable source of human knowledge that helps identify coreferent entities. However, the quality of user involvement is not always satisfying, which significantly diminishes the coreference accuracy. In this paper, we propose a new approach called coCoref, which leverages distributed human computation and consensus partition for entity coreference. Consensus partition is used to aggregate all distributed user-judged coreference results and resolve their disagreements. To alleviate user involvement, ensemble learning is performed on the consensus partition to automatically identify coreferent entities that users have not judged. We integrate coCoref into an online Linked Data browsing system, so that users can participate in entity coreference with their daily Web activities. Our empirical evaluation shows that coCoref largely improves the accuracy of user-judged coreference results, and reduces user involvement by automatically identifying a large number of coreferent entities.

The CLOCK Data-Aware Eviction Approach: Towards Processing Linked Data Streams with Limited Resources
Processing streams rather than static files of Linked Data has gained increasing importance in the web of data. When processing data streams system builders are faced with the conundrum of guaranteeing a constant maximum response time with limited resources and, possibly, no prior information on the data arrival frequency. One approach to address this issue is to delete data from a cache during processing – a process we call eviction. The goal of this paper is to show that data-driven eviction outperforms today’s dominant data-agnostic approaches such as first-in-first-out or random deletion. Specifically, we first introduce a method called Clock that evicts data from a join cache based on the likelihood estimate of contributing to a join in the future. Second, using the well-established SR-Bench benchmark as well as a data set from the IPTV domain, we show that Clock outperforms data-agnostic approaches indicating its usefulness for resource limited linked data stream processing.

The usability of Description Logics: Understanding the cognitive difficulties presented by Description Logics
Description Logics have been extensively studied from the viewpoint of decidability and computational tractability. Less attention has been given to their usability and the cognitive difficulties they present, in particular for those who are not specialists in logic. This paper reports on a study into the difficulties associated with the most commonly used Description Logic features. Psychological theories are used to take account of these. Whilst most of the features presented no difficulty to participants, the comprehension of some was affected by commonly occurring misconceptions. The paper proposes explanations and remedies for some of these difficulties. In addition, the time to confirm stated inferences was found to depend both on the maximum complexity of the relations involved and the number of steps in the argument.

Dedalo: looking for Clusters Explanations in a Labyrinth of Linked Data
We present Dedalo, a framework which is able to exploit Linked Data to generate explanations for clusters. In general, any result of a Knowledge Discovery process, including clusters, is interpreted by human experts who use their background knowledge to explain them. However, for someone without such expert knowledge, those results may be difficult to understand. Obtaining a complete and satisfactory explanation becomes a laborious and time-consuming process, involving expertise in possibly different domains. Having said so, not only does the Web of Data contain vast amounts of such background knowledge, but it also natively connects those domains. While the efforts put in the interpretation process can be reduced with the support of Linked Data, how to automatically access the right piece of knowledge in such a big space remains an issue. Dedalo is a framework that dynamically traverses Linked Data to find commonalities that form explanations for items of a cluster. We have developed different strategies (or heuristics) to guide this traversal, reducing the time to get the best explanation. In our experiments, we compare those strategies and demonstrate that Dedalo finds relevant and sophisticated Linked Data explanations from different areas.

Distributed Keyword Search over RDF via MapReduce
Non expert users need support to access linked data available on the Web. To this aim, keyword-based search is considered an essential feature of database systems. The distributed nature of the Semantic Web demands query processing techniques to evolve towards a scenario where data is scattered on distributed data stores. Existing approaches to keyword search cannot guarantee scalability in a distributed environment, because, at runtime, they are unaware of the location of the relevant data to the query and thus, they cannot optimize join tasks. In this paper, we illustrate a novel distributed approach to keyword search over RDF data that exploits the MapReduce paradigm by switching the problem from graph-parallel to data-parallel processing. Moreover, our framework is able to consider ranking during the building phase to return directly the best (top-k) answers in the first (k) generated results, reducing greatly the overall computational load and complexity. Finally, a comprehensive evaluation demonstrates that our approach exhibits very good efficiency guaranteeing high level of accuracy, especially with respect to state-of-the-art competitors.

A Probabilistic Approach for Integrating Heterogeneous Knowledge Sources
In the past years, open-domain Information Extraction (IE) systems like Nell and ReVerb have achieved impressive results by harvesting massive amounts of machine-readable knowledge with minimal supervision. However, the knowledge bases they produce still lack a clean, explicit semantic data model. This, on the other hand, could be provided by full-fledged semantic networks like DBpedia or Yago, which, in turn, would benefit from the additional coverage provided by Web-scale IE. In this paper, we bring these two strains of research together, and present a method to align terms from Nell with instances in DBpedia. Our approach is unsupervised in nature and relies on two key components. First, we automatically acquire probabilistic type information for Nell terms given a set of matching hypotheses. Second, we view the mapping task as the statistical inference problem of finding the most likely coherent mapping – i.e., the maximum a posteriori (MAP) mapping – based on the outcome of the first component used as soft constraint. These two steps are highly intertwined: accordingly, we propose an approach that iteratively refines type acquisition based on the output of the mapping generator, and vice versa. Experimental results on gold-standard data indicate that our approach outperforms a strong baseline, and is able to produce ever-improving mappings consistently across iterations.

Generating and Summarizing Explanations for Linked Data
Linked Data consumers may need explanations for debugging or understanding the reasoning behind producing the data. They may need the possibility to transform long explanations into more understandable short explanations. In this paper, we discuss an approach to explain reasoning over Linked Data. We introduce a vocabulary to describe explanation related metadata and we discuss how publishing these metadata as Linked Data enables explaining reasoning over Linked Data. Finally, we present an approach to summarize these explanations taking into account user specified explanation filtering criteria.

Using Semantic and Domain-based Information in CLIR Systems
Cross-Language Information Retrieval (CLIR) systems extend classic information retrieval mechanisms for allowing users to query across languages, i.e., to retrieve documents written in languages different from the language used for query formulation. In this paper, we present a CLIR system exploiting multilingual ontologies for enriching documents representation with multilingual semantic information during the indexing phase and for mapping query fragments to concepts during the retrieval phase. This system has been applied on a domain-specific document collection and the contribution of the ontologies to the CLIR system has been evaluated in conjunction with the use of both Microsoft Bing and Google Translate translation services. Results demonstrate that the use of domain-specific resources leads to a significant improvement of CLIR system performance.

RDSZ: An approach for lossless RDF stream compression
In many applications (like social or sensor networks) the information generated can be represented as a continuous stream of RDF items, where each item describes an application event (social network post, sensor measurement, etc). In this paper we focus on compressing RDF streams. In particular, we propose an approach for lossless RDF stream compression, named RDSZ (RDF Differential Stream compressor based on Zlib). This approach takes advantage of the structural similarities among items in a stream by combining a differential item encoding mechanism with the general purpose stream compressor Zlib. Empirical evaluation using several RDF stream datasets shows that this combination produces gains in compression ratios with respect to using Zlib alone.

Towards Competency Question-driven Ontology Authoring
Ontology authoring is a non-trivial task for novice authors who are not proficient in logics. It is difﬁcult to either specify the requirements to an ontology, or test the satisfaction of them. In this paper, we propose a novel approach to addressing this problem by leveraging the ideas of competency questions and test-driven software development. We ﬁrst analyse real-world competency questions collected from two different domains. Analysis shows that many of them can be categorised into several frequent patterns that differ along a set of features. Then we employ the notion of presupposition from linguistics to describe the ontology requirements implied by competency questions, and show that these presuppositions can be tested automatically.

Ontology-Based Data Access Using Rewriting, OWL 2 RL Systems and Repairing
In previous work it has been shown how an OWL 2 DL ontology O can be ‘repaired’ for an OWL 2 RL system ans—that is, how we can compute a set of axioms R that is independent from the data and such that ans that is generally incomplete for O becomes complete for all SPARQL queries when used with O\cup R. However, the initial implementation and experiments were very preliminary and hence it is currently unclear whether the approach can be applied to large and complex ontologies. Moreover, the approach so far can only support instance queries. In the current paper we thoroughly investigate repairing as an approach to scalable (and complete) ontology-based data access. First, we present several non-trivial optimisations to the first prototype. Second, we show how (arbitrary) conjunctive queries can be supported by integrating well-known query rewriting techniques with OWL 2 RL systems via repairing. Third, we perform an extensive experimental evaluation obtaining encouraging results. In more detail, our results show that we can compute repairs even for very large real-world ontologies in a reasonable amount of time, that the performance overhead introduced by repairing is negligible in small to medium sized ontologies and noticeable but manageable in large and complex one, and that the hybrid reasoning approach can very efficiently compute the correct answers for real-world challenging scenarios.

Identifying diachronic topic-based research communities by clustering shared research trajectories
Communities of academic authors are usually identified by means of standard community detection algorithms, which exploit ‘static’ relations, such as co-authorship or citation networks. In contrast with these approaches, here we focus on diachronic topic-based communities –i.e., communities of people who appear to work on semantically related topics at the same time. These communities are interesting because their analysis allows us to make sense of the dynamics of the research world –e.g., migration of researchers from one topic to another, new communities being spawn by older ones, communities splitting, merging, ceasing to exist, etc. To this purpose, we are interested in developing clustering methods that are able to handle correctly the dynamic aspects of topic-based community formation, prioritizing the relationship between researchers who appear to follow the same research trajectories. We thus present a novel approach called Temporal Semantic Topic-Based Clustering (TST), which exploits a novel metric for clustering researchers according to their research trajectories, defined as distributions of semantic topics over time. The approach has been evaluated through an empirical study involving 25 experts from the Semantic Web and Human-Computer Interaction areas. The evaluation shows that TST exhibits a performance comparable to the one achieved by human experts.

These are your rights: A Natural Language Processing Approach to Automated RDF Licenses Generation
In the latest years, the Web has seen an increasing interest in legal issues, concerning the use and re-use of online published material. In particular, several open issues affect the terms and conditions under which the data published on the Web is released to the users, and the users rights over such data. Though the number of licensed material on the Web is considerably increasing, the problem of generating machine readable licenses information is still unsolved. In this paper, we propose to adopt Natural Language Processing techniques to extract in an auto-mated way the rights and conditions granted by a license, and we return the license in a machine readable format using RDF and adopting two well known vocabularies to model licenses. Experiments over a set of widely adopted licenses show the feasibility of the proposed approach.

Evaluating citation functions in CiTO: cognitive issues
Networks of citations are a key tool for referencing, disseminating and evaluating research results. The task of characterising the functional role of citations in scientific literature is very difficult, not only for software agents but for humans, too. The main problem is that the mental models of different annotators hardly ever converge to a single shared opinion. The goal of this paper is to investigate how an existing reference model for classifying citations, namely CiTO (Citation Typing Ontology), is interpreted and used by annotators of scientific literature. We present some experiments capturing the cognitive processes behind users' decisions in annotating papers with CITO, and provide initial ideas to refine future releases of CiTO and to simulate readers' behaviour within CiTaLO, a tool for automatic classification of citations.

SPARQL Query Verbalization for Explaining Semantic Search Engine Queries
In this paper we introduce Spartiqulation, a system that translates SPARQL queries into English text. Our aim is to allow casual end users of semantic applications with limited to no expertise in the SPARQL query language to interact with these applications in a more intuitive way. The verbalization approach exploits domain-independent template-based natural language generation techniques, as well as linguistic cues in labels and URIs.

NLP data cleansing based on Linguistic Ontology constraints
Linked Data comprises of an unprecedented volume of structured data on the Web and is adopted from an increasing number of domains. However, the varying quality of published data forms a barrier for further adoption, especially for Linked Data consumers. In this paper, we extend a previously developed methodology of Linked Data quality assessment, which is inspired by test-driven software development. Specifically, we enrich it with ontological support and different levels of result reporting and describe how the method is applied in the Natural Language Processing (NLP) area. NLP is -- compared to other domains, such as biology -- a late Linked Data adopter. However, it has seen a steep rise of activity in the creation of data and ontologies. NLP data quality assessment has become an important need for NLP datasets. In our study, we analysed 11 datasets using the Lemon and NIF vocabularies in 277 test cases and point out common quality issues.

Optimising Linked Data Queries Using Runtime Statistics in the Presence of Co-reference
Due to the distributed nature of Linked Data, many resources are referred to by more than one URI. This phenomenon, known as co-reference, increases the probability of leaving out implicit semantically related results when querying Linked Data. The probability of co-reference increases further when considering distributed SPARQL queries over a larger set of distributed datasets. Addressing co-reference in Linked Data queries, on one hand, increases complexity of query processing. On the other hand, it requires changes in how statistics of datasets are taken into consideration. We investigate these two challenges of addressing co-reference in distributed SPARQL queries, and propose two methods to improve query efficiency: 1) a model named Virtual Graph, that transforms a query with co-reference into a normal query with pre-existing bindings; 2) an algorithm named $\Psi$, that intensively exploits parallelism, and dynamically optimises queries using runtime statistics. We deploy both methods in an distributed engine called LHD-d. To evaluate LHD-d, we investigate the distribution of co-reference in the real world, based on which we simulate an experimental RDF network. In this environment we demonstrate the advantages of LHD-d for distributed SPARQL queries in environments with co-reference.

Hybrid Acquisition of Temporal Scopes for RDF Data
Information on the temporal interval of validity for facts described by RDF triples plays an important role in a large number of applications. Yet, most of the knowledge bases available on the Web of Data do not provide such information in an explicit manner. In this paper, we present a generic approach which addresses this drawback by inserting temporal information into knowledge bases. Our approach combines two types of information to associate RDF triples with time intervals. First, it relies on temporal information gathered from the document Web by an extension of the fact validation framework DeFacto. Second, it harnesses the time information contained in knowledge bases. This knowledge is combined within a three-step approach which comprises the steps matching, selection and merging. We evaluate our approach against a corpus of facts gathered from Yago2 by using DBpedia and Freebase as input and different parameter settings for the underlying algorithms. Our results suggest that we can detect temporal information for facts from DBpedia with an F-measure of up to 70%.

NL-Graphs: A Hybrid Approach toward Interactively Querying Semantic Data
A variety of query approaches have been proposed by the semantic web community to explore and query semantic data. Each was developed for a specific task and employed its own interaction mechanism; each query mechanism has its own set of advantages and drawbacks. Most semantic web search systems employ only one approach, thus being unable to exploit the benefits of alternative approaches. Motivated by a usability and interactivity perspective, we propose to combine two query approaches (graph-based and natural language) as a hybrid query approach. In this paper, we present NL-Graphs which aims to exploit the strengths of both approaches, while ameliorating their weaknesses. NL-Graphs was conceptualised and developed from observations, and lessons learned, in several evaluations with expert and casual users. The results of evaluating our approach with expert and casual users on a large semantic dataset are very encouraging; both types of users were highly satisfied and could effortlessly use the hybrid approach to formulate and answer queries. Indeed, success rates showed they were able to successfully answer all the evaluation questions.

User Interests Identification on Twitter Using a Hierarchical Knowledge Base
Twitter, due to its massive growth as a social networking platform has been in focus to analyze its user generated content for personalization and recommendation tasks. A common challenge across these tasks is identifying user interests from tweets. Lately, semantic enrichment of Twitter posts to determine (entity-based) user interests has been an active area of research. The advantages of these approaches include interoperability, information reuse and the availability of knowledge-bases to be exploited. However, exploiting these knowledge bases for identifying user interests still remains a challenge. In this work, we focus on exploiting hierarchical relationships present in knowledge-bases to infer richer user interests expressed as \textit{Hierarchical Interest Graph}. We argue that the hierarchical semantics of concepts can enhance the existing systems to personalize or recommend items based on varied level of conceptual abstractness. We demonstrate the effectiveness of our approach through a user study which shows an average of approximately eight of the top ten weighted hierarchical interests in the graph being relevant to a given user's interests.

Facilitating Human Intervention in Coreference Resolution with Comparative Entity Summaries
A primary challenge to Web data integration is coreference resolution, namely identifying entity descriptions from different data sources that refer to the same real-world entity. Increasingly, solutions to coreference resolution have humans in the loop. For instance, many active learning, crowdsourcing, and pay-as-you-go approaches solicit user feedback for verifying candidate coreferent entities computed by automatic methods. Whereas reducing the number of verification tasks is a major consideration for these approaches, very little attention has been paid to the efficiency of performing each single verification task. To address this issue, in this paper, instead of showing the entire descriptions of two entities for verification which are possibly lengthy, we propose to extract and present a compact summary of them, and expect that such length-limited comparative entity summaries can help human users verify more efficiently without significantly hurting the accuracy of their verification. Our approach exploits the common and different features of two entities that best help indicate (non-)coreference, and also considers the diverse information on their identities. Experimental results show that verification is 2.7--2.9 times faster when using our comparative entity summaries, and its accuracy is not notably affected.

Linked USDL: a Vocabulary for Web-scale Service Trading
An increasing number of services and marketplaces are flourishing in the Internet. From human services advertised in Google Helpouts to cloud services available at Amazon AWS marketplace. All services are, however, solely described in unstructured Web pages, which are suitable for manual browsing, but are difficult to exploit for creating advanced software applications which can foster service trading by supporting automated search, selection, service level negotiation, and contracting. The Unified Service Description Language (USDL) aimed at formalising service descriptions to take the current Internet to a Web of services. Nonetheless, its rigid technological foundations, its high complexity, and the reduced extensibility of the model limited its adoption. Informed from past experience on USDL, we present in this paper Linked USDL, the next evolution of USDL that adopts and exploits Linked Data to be scalable for the Web, to promote and simplify its adoption by reusing vocabularies and datasets, and to benefit from a high level of genericity and adaptability for domain specific modelling.

Blank nodes and equivalence testing and comparison (diff)
Generation of synthetic RDF data for benchmarking
Generating Synthetic RDF Data with Connected Blank Nodes for Benchmarking
Generators for synthetic RDF datasets are very important for testing and benchmarking various semantic data management tasks (e.g. querying, storage, update, compare, integrate). However, the current generators, like the Univ-Bench Artificial data generator (UBA), do not support sufficiently (or totally ignore) blank node connectivity issues. Blank nodes are used for various purposes (e.g. for describing complex attributes), and a significant percentage of resources is currently represented with blank nodes. Moreover, several semantic data management tasks, like isomorphism checking (useful for checking equivalence), and blank node matching (useful in comparison/versioning/synchronization, and in semantic similarity functions), not only have to deal with blank nodes, but their complexity and optimality depends on the connectivity of blank nodes. To enable the comparative evaluation of the various techniques for carrying out these tasks, in this paper we present the design and implementation of a generator, called BGEN, which allows building data sets containing blank nodes with the desired complexity, controllable through various features (morphology, size, diameter, clustering coefficient). Finally, the paper reports experimental results concerning the efficiency of the generator, as well as results from using the generated datasets, that demonstrate the value of the generator.

A Knowledge Based Approach for Tackling Mislabeled Multi-class Big Social Data
The performance of classification models extremely relies on the quality of training data. However, label imperfection is an inherent fault of training data, which cannot be manually handled in big data environment. Various methods have been proposed to remove label noises in order to improve classification quality, with the side effect of cutting down data bulk. In this paper, we propose a knowledge based approach for tackling mislabeled multi-class big data, in which knowledge graph technique is combined with other data correction method to perceive and correct the error labels in big data. Experiments on a medical Q&A social data set show our knowledge graph based approach can effectively improve data quality and classification accuracy. Furthermore, this approach can be applied in other data mining tasks requiring deep understanding.

Using semantic technologies the materialization of implicit given facts that can be derived from a dataset is an important task performed by a reasoner. With respect to the answering time for queries and the growing amount of available data, scaleable solutions that are able to process large datasets are needed. In previous work we described a rule-based reasoner implementation that uses massively parallel hardware to derive new facts based on a given set of rules. This implementation was limited by the size of processable input data as well as on the number of used parallel hardware devices. In this paper we introduce further concepts for a workload partitioning and distribution to overcome this limitations. Based on the introduced concepts, additional levels of parallelization can be proposed that benefit from the use of multiple parallel devices. Furthermore, we introduce a concept to reduce the amount of invalid triple derivations like duplicates. We evaluate our concepts by applying different rulesets to the real-world DBPedia dataset as well as to the synthetic Lehigh University benchmark ontology (LUBM) with up to 1.1 billion triples. The evaluation shows that our implementation scales in a linear way and outperforms current state of the art reasoner with respect to the throughput achieved on a single computing node.

Detecting Incorrect Numerical Data in DBpedia
DBpedia is a central hub of Linked Open Data (LOD). Being based on crowd-sourced contents and heuristic extraction methods, it is not free of errors. In this paper, we study the application of numerical outlier detection methods to DBpedia, using Interquantile Range (IQR), Kernel Density Estimation (KDE), and various dispersion estimators, combined with different semantic grouping methods. Our approach reaches 87% precision, and has lead to the identification of 11 systematic errors in the DBpedia extraction framework.

Perplexity of Index Models over Evolving Linked Data
In this paper we analyse the sensitivity of twelve prototypical linked data index models towards evolving data. Thus, we consider the reliability and accuracy of results obtained from an index in scenarios where the original data has changed after having been indexed. Our analysis is based on empirical ob- servations over real world data covering a time span of more than one year. The quality of the index models is evaluated w.r.t. their ability to give reliable esti- mations of the distribution of the indexed data. To this end we use metrics such as perplexity, cross-entropy and Kullback-Leibler divergence. Our experiments show that all considered index models are affected by the evolution of data, but to different degrees and in different ways. We also make the interesting observa- tion that index models based on schema information seem to be more stable than index models based on triples or context information.

HiBISCuS: Hypergraph-Based Source Selection for SPARQL Endpoint Federation
Efficient federated query processing is of significant importance to tame the large amount of data available on the Web of Data. Previous works have focused on generating optimized query execution plans for fast result retrieval. However, devising source selection approaches beyond triple pattern-wise source selection has not received much attention. This work presents HiBISCuS, a novel hypergraph-based source selection approach to federated SPARQL querying. Our approach can be directly combined with existing SPARQL query federation engines to achieve the same recall while querying fewer data sources. We extend three well-known SPARQL query federation engines -- DARQ, SPLENDID, and FedX -- with HiBISCus and compare our extensions with the original approaches on FedBench. Our evaluation shows that HiBISCuS can efficiently reduce the total number of sources selected without losing the recall. Moreover, our approach significantly reduces the execution time of the selected engines on most of the benchmark queries.

Pay-as-you-go Approximate Join Top-k Processing for the Web of Data
For effectively searching the Web of data, ranking results is a crucial. Top-k processing strategies have been proposed to allow an efficient processing of such ranked queries. These strategies aim at computing k top-ranked results without complete result materialization. However, for many applications result computation time is much more important than result accuracy and completeness. Thus, there is a strong need for approximated ranked results. Unfortunately, previous work on approximate top-k processing is not well-suited for the Web of data. In this paper, we propose the first approximate top-k join framework for Web data and queries. Our approach is very lightweight – neces- sary statistics are learned at runtime in a pay-as-you-go manner. We conducted extensive experiments on state-of-art RDF benchmarks. Our results are very promising: we could achieve up to 65% time savings, while maintaining a high precision/recall.

Object Property Matching utilizing the Overlap between Imported Ontologies
Large scale Linked Data is often based on relational databases and thereby tends to be modeled with rich object properties, specifying the exact relationship between two objects, rather than a generic is-a or part-of relationship. We study this phenomenon on government issued statistical data, where a vested interest exists in matching such object properties for data integration. We leverage the fact that while the labeling of the properties is often heterogeneous, e.g. ex1:geo and ex2:location, they link to individuals of semantically similar code lists, e.g. country lists. State-of-the-art ontology matching tools do not use this effect and therefore tend to miss the possible correspondences. We enhance the state-of-the-art matching process by aligning the individuals of such imported ontologies separately and computing the overlap between them to improve the matching of the object properties. The matchers themselves are used as black boxes and are thus interchangeable. The new correspondences found with this method lead to an increase of recall up to 2.5 times on real world data, with only a minor loss in precision.

Survey on Common Strategies of Vocabulary Reuse in Linked Open Data Modeling
The choice of which vocabulary to reuse when modeling and publishing Linked Open Data (LOD) is far from trivial. Until today, there is no study that investigates the different strategies of reusing vocabularies for LOD modeling.In this paper, we present the results of a survey with 79 participants that examines the most preferred strategies of vocabulary reuse of LOD modeling. Participants of our survey are LOD publishers and practitioners. Their task was to assess different vocabulary reuse strategies and explain their ranking decision. We found significant differences between the modeling strategies that range from reusing popular vocabularies, minimizing the number of vocabularies, and staying within one domain vocabulary. A very interesting insight is that the popularity in terms of how many data sources use a vocabulary is more important than total occurrence of individual classes and properties in the LOD cloud. Overall, the results of this survey help in better understanding the strategies how data engineers reuse vocabularies and may be used to develop future vocabulary engineering tools.

knowledge-based complex event processing
Plan-Based Semantic Enrichment of Event Streams
Background knowledge about the application domain can be used in event processing in order to improve processing quality. The idea of semantic enrichment is to incorporate background knowledge into events, thereby generating enriched events which in the next processing step can be better understood by event processing engines. In this paper, we present an efficient technique for event stream enrichment by planning multi-step event enrichment and processing. Our optimization goal is to minimize event enrichment costs while meeting application-specific service expectations. The event enrichment is optimized to avoid unnecessary event stream enrichment without missing any complex events of interest. Our experimental results shows that by using this approach it is possible to reduce the knowledge acquisition costs.

Providing Alternative Declarative Descriptions for Entity Sets using Parallel Concept Lattices
Abstract We propose an approach for modifying a declarative description of a set of entities (e.g., a SPARQL query) for the purpose of finding alternative declarative descriptions of the entities. Such a shift in representation can help to get new insights into the data, to discover related attributes, or to find a more con- cise description of the entities of interest. Allowing the alternative descriptions furthermore to be close approximations of the original entity set leads to more flexibility in finding such insights. Our approach is based on the construction of parallel formal concept lattices over different sets of attributes for the same en- tities. Between the formal concepts in the parallel lattices, we define mappings which constitute approximations of the extent of the concepts. In this paper, we formalize the idea of two types of mappings between formal concept lattices, provide an implementation of these mappings and evaluate their ability of finding alternative descriptions on several real-world RDF data sets for one concrete set- ting of finding alternative descriptions for entities described by RDF class types which are based on properties.

WaterFowl: a Compact, Self-indexed and Inference-enabled RDF Store
In this paper, we present a novel approach – called WaterFowl – for the storage of RDF triples that addresses some key issues in the contexts of big data and the Semantic Web. The architecture of our prototype, largely based on the use of succinct data structures, enables the representation of triples in a self-indexed, compact manner without requiring decompression at query answering time. Moreover, it is adapted to efficiently support RDF and RDFS entailment regimes thanks to an optimized encoding of ontology concepts and properties that does not require a complete inference materialization or extensive query rewriting algorithms. This approach implies to make a distinction between the ter- minological and the assertional components of the knowledge base early in the process of data preparation, i.e., preprocessing the data before storing it in our structures. The paper describes the complete architec- ture of this system and presents some preliminary results obtained from evaluations conducted on our first prototype.

A Framework for Iterative Signing of Graph Data on the Web
Existing algorithms for signing graph data typically do not cover the whole signing process. In addition, they lack distinctive features such as signing graph data at different levels of granularity, iterative signing of graph data, and signing multiple graphs. In this paper, we introduce a novel framework for signing arbitrary graph data provided, e.g., as RDF(S), Named Graphs, or OWL. We conduct an extensive theoretical and empirical analysis of the runtime and space complexity of different framework configurations. The experiments are performed on synthetic and real-world graph data of different size and different number of blank nodes. We investigate security issues, present a trust model, and discuss practical considerations for using our signing framework.

Error-Tolerant RDF Subgraph Matching for Adaptive Presentation of Linked Data on Mobile
We present PRISSMA, a context-aware presentation layer for Linked Data. PRISSMA extends the Fresnel vocabulary with the notion of mobile context. Besides, it includes an algorithm that determines whether the sensed context is compatible with some context declarations. The algorithm finds optimal error-tolerant subgraph isomorphisms between RDF graphs using the notion of graph edit distance and is sublinear in the number of context declarations in the system.

Unsupervised Link Discovery Through Knowledge Base Repair
Over the last few years, devising efficient approaches to compute links between datasets has been regarded as central to achieve the vision behind the Data Web. Several unsupervised approaches have been developed to achieve this goal. Yet, so far, none of these approaches make use of replication of resources across several knowledge bases. In this paper, we present Colibri, an unsupervised approach that allows discovering links between datasets while improving the quality of the instance data in these datasets. A Colibri iteration begins by generating links between knowledge bases. Then, our approach makes use of these links to detect resources with probably erroneous or missing information. The erroneous or missing information detected by the approach is finally corrected or added. We evaluate our approach on benchmark datasets with respect to the F-score it achieves. Our results suggest that Colibri can improve the results of unsupervised machine-learning approaches for link discovery by up to 12% while correctly detecting and repairing erroneous resources.

A Scalable Approach for Efficiently Generating Structured Dataset Topic Profiles
The increasing adoption of Linked Data principles has led to an abundance of datasets on the Web. However, take-up and reuse is hindered by the lack of descriptive information about the nature of the data, such as their topic coverage, dynamics or evolution. To address this issue, we propose an approach for creating linked dataset profiles. A profile consists of structured dataset metadata describing topics and their relevance. Profiles are generated through the configuration of techniques for resource sampling from datasets, topic extraction from knowledge bases and their ranking based on graphical models. To enable a good trade-off between scalability and representatives of generated data, appropriate parameters are determined experimentally. Our evaluation considers topic profiles of all accessible datasets from the Linked Open Data cloud and shows that our approach generates representative profiles even with comparably small sample sizes (10%) outperforms established topic modelling approaches.

Identifying change patterns of concept attributes in ontology evolution
Ontology versions are periodically released to ensure their usefulness and reliability over time. This potentially impacts dependent artefacts such as mappings and annotations. To deal with this issue re- quires to ﬁnely characterize ontology entities’ changes between ontology versions. This article proposes to identify change patterns at attribute values when an ontology evolves, to track textual statements describing concepts. We empirically evaluate our approach by using biomedical on- tologies, for which new ontology versions are frequently released. Our achieved results suggest the feasibility of the proposed techniques.

SentiCircles for Contextual and Conceptual Semantic Sentiment analysis of Twitter
Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words' sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending on context and targeted entities. In this paper we present SentiCircle; a novel lexicon-based approach that takes into account the contextual and conceptual semantics of words when calculating their sentiment orientation and strength in Twitter. We evaluate our approach on three Twitter datasets using three different sentiment lexicons. Results show that our approach significantly outperforms two lexicon baselines. Results are competitive but inconclusive when comparing to state-of-art SentiStrength, and vary from one dataset to another. SentiCircle outperforms SentiStrength in accuracy on average, but falls marginally behind in F-measure.

Lechuzo: Weakly-Supervised System for Fine-Grained Sentiment Analysis

A Hybrid Multi-Strategy Recommender System Using Linked Open Data

Exploring Semantic Features for Producing Top-N Recommendation Lists from Binary User Feedback

Aggregation Strategies for Linked Open Data-enabled Recommender Systems

SemStim at the LOD-RecSys 2014 challenge

LODify: A Hybrid Recommender System based on Linked Open Data

Popular Books and Linked Data: Some Results for the ESWC'14 RecSys Challenge
Popular Books and Linked Data: Some Results for the ESWC’14 RecSys Challenge

Hybrid Approach for the Semantic Processing of Scientific Papers

Semantify CEUR-WS Proceedings: towards the automatic generation of highly descriptive scholarly publishing Linked Datasets

Increasing Top-20 diversity through recommendation post-processing

Hybrid model rating prediction with Linked Open Data for Recommender Systems

Unstable markup: A template-based information extraction from web sites with unstable markup

Deep Learning of Semantic Word Representations to Implement a Content-based Recommender for the RecSys Challenge'14

Semantic Lexicon Induction for Concept-level Sentiment Analysis

Linkitup: Semantic Publishing of Research Data

Extraction of workshop proceedings in HTML using the RML mapping language

Sentic Demo: A Hybrid Concept Level Aspect Based Sentiment Analysis Toolkit

Sinica Semantic Parser for ESWC'14 Concept-Level Semantic Analysis Challenge
Sinica Semantic Parser for ESWC'14 Concept-Levle Semantic Analysis Challenge

Improve Polarity Detection of Online Reviews with Bag-of-Sentimental-Concepts

ROHub – A digital library of Research Objects supporting scientists towards reproducible science

Faceted Semantic Search for Scientific Publications

A Fuzzy System For Semantic Sentiment Analysis

Hybrid Recommending Exploiting Multiple DBPedia Language Editions

The role of annotation in reproducibility

An Experience on Empirical Research about RDF Stream Processing

A Linked Data Approach to Sentiment and Emotion Analysis of Twitter in the Financial Domain

Analyzing Stock Market Fraud Cases Using a Linguistics-Based Text Mining Approach

Predicting the impact of central bank communications on financial market investors’ interest rate expectations

Predicting financial statements based on unstructured data sources

ArtFinder: A Faceted Browser for Cross-Cultural Art Discovery

Supporting Visual Exploration of Inter-organization Collaboration

Supporting Web Vocabulary Development by Automated Quality Assessment: Results of a Case Study in a Teaching Context

Harvesting Ontology Authoring Data with Protégé

Parallel Search Through Statistical Semantic Spaces Leveraging Linked Open Data

Interlinking English and Chinese RDF Data Sets Using Machine Translation

Simplifying RDF Data for Graph-Based Machine Learning

Link Prediction in Very Large Directed Graphs: Exploiting Hierarchical Properties in Parallel

Enriching Electronic Programming Guides with Web Data.

The Media Ecology Project: Using Linked Data to Support Distributed Analysis of Visual Culture.

Media Fragments Indexing using Social Media

Survey of Semantic Media Annotation Tools for the Web: Towards new Media Applications with Linked Media

Na de bevrijding XL: Expanding a Historical Television Series with Archival Sources

Extending VoID for Expressing Connectivity Metrics of a Semantic Warehouse

What's all the Data about? - Creating structured Profiles of Linked Data on the Web

Entity-based Data Source Contextualization for Searching the Web of Data

Towards Analytics and Collaborative Exploration of Social and linked Media for Technology-Enchanced Learning Scenarios

From Changes to Dynamics: Dynamics Analysis of Linked Open Data Sources

Towards a Framework for Managing Evolving Information Resources on the Data Web

Of Sampling and Smoothing: Approximating Distributions over Linked Open Data

LODOP - Multi-Query Optimization for Linked Data Profiling Queries

Ricochet:Context and Complementarity-Aware, Ontology-based POIs Recommender System

RESTful or RESTless - Current State of Today's Top Web APIs

Semantic RESTful APIs for Dynamic Data Sources

A Data API With Security and Graph-Level Access Control

What's in the proceedings? Combining publisher's and researcher's perspectives

Ornithology Based on Linking Bird Observations with Weather Data

Supporting Researchers with Semantic Literature Management

SMART Research using Linked Data - Sharing Research Data for Integrated Water Resources Management in the Lower Jordan Valley

Describing bibliographic references in RDF

Supporting Information Sharing for Re-Use and Analysis of Scientific Research Publication Data

A review of ontologies for describing scholarly and scientific documents

Emergency vehicle routing using Geolinked Open Data for the Municipality of Catania

Visualisation of User-Generated Event Information: Towards Geospatial Situation Awareness Using Hierarchical Granularity Levels

An Ontology-Based Approach to Social Media Mining for Crisis Management

Visual Analysis of Real-time Social Media for Emergency Response

Adapting Sentiment Lexicons using Contextual Semantics for Twitter Sentiment Analysis

Usage of Abstract Features in Semantic Sentiment Analysis

Sentiment Analysis for Dynamic User Preference Inference in Spoken Dialogue Systems

Get All, Filter Details - On the Use of Regular Expressions in SPARQL Queries

Man vs. Machine: Differences in SPARQL Queries

Ontology Design Patterns: Adoption Challenges and Solutions

CROCUS: Cluster-based ontology data cleansing

IRIS: A Protege Plug-in to Extract and Serialize Product Attribute Name-Value Pairs

Mapping Representation based on Meta-data and SPIN for Localization Workflows

Knowledge Linkage across Knowledge Domains in Collaborative Environments

First experiments in cultural alignment repair.

Interactive Ontology Debugging using Direct Diagnosis.

Identifying Wrong Links between Datasets by Multi-dimensional Outlier Detection.

A System for Debugging Missing Is-a Structure in EL Ontologies.

B-Annot: Supplying Background Model Annotations for Ontology Coherence Testing.

Proceedings of 11th Extended Semantic Web Conference (ESWC2014), May 25 - May 29, 2014

Aalto University and University of Helsinki

Aston Business School, Aston University, Birmingham, UK

Baden-Wuerttemberg Cooperative State University, Mannheim

Carnegie Mellon University, Department of Psychology

Cognitive Interaction Technology, Center of Excellence

Data Archiving and Networked Services (DANS)

Department of Computer Science and Engineering, Shanghai Jiao Tong University

Department of Computer Science, University of Leipzig

Department of Computer Science, University of Oxford

Department of Computer Science, University of Turin

Dept. of Archive and Library Science, Ionian University

DERI - National University of Ireland, Galway
DERI Galway, National University of Ireland, Galway
Digital Enterprise Research Institute, NUI Galway

Digital Enterprise Research Institute (DERI), National University of Ireland,
Galway

Digital Enterprise Research Institute, NUIG

Dipartimento di Informatica - University of Bari

Faculty Information Systems and Applied Computer Science, University of Bamberg

GESIS - Leibniz Institute for the Social Sciences

Goethe-University Frankfurt am Main, Text Technology Group

Hasso-Plattner-Institute for IT Systems Engineering, University of Potsdam

INRIA Saclay--Île-de-France and LRI, Université Paris Sud-11

INSERM UMRS 872, éq.20, 15, rue de l’école de médecine, 75006 Paris, France

Institute for Educational Technologies, CNR

Institute for the Management of Information Systems

Institute of Computer Science, FORTH-ICS, GREECE

Institute of Law and Technology - Universitat Autònoma de Barcelona

Intelligent Software Components (iSOCO) S.A.

Kiel University and Leibniz Information Center for Economics, Kiel, Germany

Knowledge Media Institute, The Open University

Knowledge Media Institute, Open University, U.K.

Laboratory for Applied Ontology, ISTC-CNR, Trento

Laboratory for Semantic Information Processing, Chair for Computing in the Cultural
Sciences

Limos, Blaise Pascal University, Clermont-Ferrand

Linguistic Modelling Laboratory, IPP, Bulgarian Academy of Sciences

LIPN UMR CNRS 7030 - Université Paris 13

Los Alamos National Laboratory, Research Library

National and Kapodistrian University of Athens

National Technical University of Athens (NTUA)

Ontology and Conceptual Modeling Research Group (NEMO)/Federal University of Espirito
Santo (UFES)

Ontology Engineering Group. Departamento de Inteligencia Artificial. Facultad de
Informática. Universidad Politécnica de Madrid

Ontology Engineering Group, Facultad de Informática, Univesidad Politécnica de
Madrid

Ontology Engineering Group. Laboratorio de Inteligencia Artificial. Facultad de
Informática. Universidad Politécnica de Madrid.

Ontology Engineering Group - Universidad Politécnica de Madrid

Ontology Engineering Group. Universidad Politécnica de Madrid

Salzburg Research Forschungsgesellschaft

SANTEC, CRP Henri Tudor and LRI, University of Paris XI

School of Computer Science & School of Software, Nanjing University of Posts and
Telecommunications

School of ITEE, The University of Queensland

School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal,
South Africa

Technical University of Dortmund, Fraunhofer IAIS

The Open University - Knowledge Media Institute

Universidad Simon Bolivar, Dept. Computer Science

Universität Leipzig, Institut für Informatik, AKSW

University of Amsterdam/Leibniz Center for Law

University of Applied Sciences St. Pölten

Dipartimento di Informatica, Università di Bari

University of Maryland, Baltimore County

Institute for Geoinformatics, University of Muenster

University of Sheffield, Department of Computer Science

Department of Computer Sciences, The University of Texas at Austin

University Panthéon - Sorbonne (Paris I)

Vienna University of Economics and Business - WU Wien

University of Amsterdam/Vrije Universiteit Amsterdam

Web Science Program, Mathematics Department, Aristotle University of Thessaloniki,
Greece

Kno.e.sis Center, Wright State University
Kno.e.sis Center, Wright State University, Dayton, Ohio

Luis SÃ¡nchez FernÃ¡ndez
SÃ¡nchez FernÃ¡ndez

Norberto FernÃ¡ndez GarcÃa
FernÃ¡ndez GarcÃa

Martin Peters, Christopher Brink, Sabine Sachweh and Albert Zündorf.
Research papers Reasoning: Scaling Parallel Rule-based Reasoning

Christoph Pinkel, Carsten Binnig, Peter Haase, Clemens Martin, Kunal Sengupta and Johannes Trame.
In-Use papers In-Use & Industrial II: How to Best Find a Partner? An Evaluation of Editing Approaches to Construct R2RML Mappings

Hassan Saif, Miriam Fernandez, Yulan He and Harith Alani.
Research papers Social Web and Web Science: SentiCircles for Contextual and Conceptual Semantic Sentiment analysis of Twitter

Session Chairs: Steffen Staab, Mathieu d'Aquin
Closing Wrap-up session: PhD Symposium Summary

Christina Lantzaki, Thanos Yannakis, Yannis Tzitzikas and Anastasia Analyti.
Research papers Data Management II: Generating Synthetic RDF Data with Connected Blank Nodes for Benchmarking

Jörg-Uwe Kietz, Floarea Serban, Simon Fischer and Abraham Bernstein.
In-Use papers In-Use & Industrial III: "Semantics Inside!'' But let's not tell the Data Miners: Intelligent Support for Data Mining

Research papers Linked Open Data I: Trusty URIs: Verifiable, Immutable, and Permanent Digital Artifacts for Linked Data

Mauro Dragoni, Alessio Bosca, Matteo Casu and Chiara Di Francescomarino.
Research papers Natural Language Processing: Using Semantic and Domain-based Information in CLIR Systems

Benedikt Kämpgen, Tobias Weller, Sean O'Riain, Craig Weber and Andreas Harth.
In-Use papers In-Use and Industrial I: Accepting the XBRL Challenge with Linked Data for Financial Data Integration

Besnik Fetahu, Stefan Dietze, Bernardo Pereira Nunes, Marco Antonio Casanova, Davide Taibi and Wolfgang Nejdl.
Research papers Linked Open Data IV: A Scalable Approach for Efficiently Generating Structured Dataset Topic Profiles

Research papers Mobile, Sensor and Semantic Streams: Error-Tolerant RDF Subgraph Matching for Adaptive Presentation of Linked Data on Mobile

Gala Gala Dinner: Conference Gala Dinner

Challenges ESWC Challenges: Concept-Level Sentiment Analysis Challenge: presentation of tasks.

Closing Best Workshop papers: Best Workshop papers

Andreas Wagner, Veli Bicer and Thanh Tran.
Research papers Data Management I: Pay-as-you-go Approximate Join Top-k Processing for the Web of Data

Thomas Gottron, Ansgar Scherp and Stefan Scheglmann.
Research papers Machine Learning: Providing Alternative Declarative Descriptions for Entity Sets using Parallel Concept Lattices

Research papers Data Management I: Perplexity of Index Models over Evolving Linked Data

Closing Closing Ceremony: Closing and Awards Ceremony

Duy Dinh, Julio Cesar Dos Reis, Cedric Pruski, Marcos Da Silveira and Chantal Reynaud.
Research papers Vocabularies, Schemas, Ontologies: Identifying change patterns of concept attributes in ontology evolution

Roberto De Virgilio and Antonio Maccioni.
Research papers Data Management II: Distributed Keyword Search over RDF via MapReduce

Khadija Elbedweihy, Suvodeep Mazumdar, Stuart N. Wrigley and Fabio Ciravegna.
Research papers Cognition and Semantic Web: NL-Graphs: A Hybrid Approach toward Interactively Querying Semantic Data

Ali Khalili, Sören Auer and Axel-Cyrille Ngonga Ngomo.
In-Use papers In-Use and Industrial I: conTEXT -- Lightweight Text Analytics using Linked Data

Session Chairs: Harald Sack and Nathalie Aussenac-Gilles
Closing Wrap-up session: Workshops and Tutorials Wrap-up

Research papers Mobile, Sensor and Semantic Streams: Plan-Based Semantic Enrichment of Event Streams

Anisa Rula, Matteo Palmonari, Axel-Cyrille Ngonga Ngomo, Daniel Gerber, Jens Lehmann and Lorenz Bühmann.
Research papers Linked Open Data III: Hybrid Acquisition of Temporal Scopes for RDF Data

Elena Cabrio, Alessio Palmero Aprosio and Serena Villata.
Research papers Semantic Web Policies, Rights and Governance: These are your rights: A Natural Language Processing Approach to Automated RDF Licenses Generation

Research papers Linked Open Data IV: Facilitating Human Intervention in Coreference Resolution with Comparative Entity Summaries

Carlos Pedrinaci, Jorge Cardoso and Torsten Leidig.
Research papers Services, Processes and Cloud Computing: Linked USDL: a Vocabulary for Web-scale Service Trading

Session Chair: Valentina Presutti. Luciano Floridi : Coordination, Semantics, and Autonomy
Keynote Keynote: Keynote Speech: Luciano Floridi

Session Chair: Claudia d'Amato. Lise Getoor: Combining Statistics and Semantics to Turn Data into Knowledge
Keynote Keynote: Keynote Speech - Lise Getoo

In-Use papers In-Use & Industrial II: Seeding Structured Data by Default via Open Source Library Systems

Andreas Kasten, Ansgar Scherp and Peter Schauß.
Research papers Data Management I: A Framework for Iterative Signing of Graph Data on the Web

Research papers Vocabularies, Schemas, Ontologies: Object Property Matching utilizing the Overlap between Imported Ontologies

Challenges ESWC Challenges: Semantic Publishing Challenge: presentation of tasks

Research papers Reasoning: Ontology-Based Data Access Using Rewriting, OWL 2 RL Systems and Repairing

Paul Warren, Paul Mulholland, Trevor Collins and Enrico Motta.
Research papers Cognition and Semantic Web: The usability of Description Logics: Understanding the cognitive difficulties presented by Description Logics

Alex Stolz, Bene Rodriguez-Castro, Andreas Radinger and Martin Hepp.
In-Use papers In-Use and Industrial I: PCS2OWL: A Generic Approach for Deriving Web Ontologies from Product Classification Systems

Research papers Vocabularies, Schemas, Ontologies: Vocabularies, Schemas, Ontologies

Dimitris Kontokostas, Martin Brümmer, Sebastian Hellmann, Jens Lehmann and Lazaros Ioannidis.
Research papers Natural Language Processing: NLP data cleansing based on Linguistic Ontology constraints

Muhammad Saleem and Axel-Cyrille Ngonga Ngomo.
Research papers Data Management II: HiBISCuS: Hypergraph-Based Source Selection for SPARQL Endpoint Federation

Basil Ell, Andreas Harth and Elena Simperl.
Research papers Linked Open Data II: SPARQL Query Verbalization for Explaining Semantic Search Engine Queries

EU Project Networking Session Session Chairs: Mari Carmen Suarez de Figueroa Baonza, Sergio Consoli, Alessio Iabichella
EU Project EU Project Networking Session: EU Project Networking Session

Panel session Panel session: Panel: Data protection and security on the Web

Yuan Ren, Artemis Parvizi, Chris Mellish, Jeff Z. Pan, Kees Van Deemter and Robert Stevens.
Research papers Vocabularies, Schemas, Ontologies: Towards Competency Question-driven Ontology Authoring

Announcements Announcements: Announcements

Norberto Fernández García, Jesús Arias Fisteus, Luis Sánchez Fernández, Damaris Fuentes-Lorenzo and Óscar Corcho.
Research papers Mobile, Sensor and Semantic Streams: RDSZ: An approach for lossless RDF stream compression

Xin Wang, Thanassis Tiropanis and Hugh C. Davis.
Research papers Linked Open Data II: Optimising Linked Data Queries Using Runtime Statistics in the Presence of Co-reference

Freddy Lecue, Robert Tucker, Veli Bicer, Pierpaolo Tommasi, Simone Tallevi-Diotallevi and Marco Luca Sbodio.
In-Use papers In-Use and Industrial I: Predicting Severity of Road Traffic Congestion using Semantic Web Technologies

Shen Gao, Thomas Scharrenbach and Abraham Bernstein.
Research papers Mobile, Sensor and Semantic Streams: The CLOCK Data-Aware Eviction Approach: Towards Processing Linked Data Streams with Limited Resources

In-Use papers In-Use & Industrial II: Towards Portable Shopping Histories: Using GoodRelations to Expose Ownership Information to E-Commerce Sites

Research papers Linked Open Data I: Leveraging Distributed Human Computation and Consensus Partition for Entity Coreference

Challenges ESWC Challenges: Linked Open Data-enabled Recommender Systems Challenge: presentation of tasks

Session Chair: Fabien Gandon Steffen Staab: Programming the Semantic Web
Keynote Keynote: Keynote Speech: Steffen Staab

Research papers Linked Open Data III: Detecting Incorrect Numerical Data in DBpedia

EU Project posters, ESWC Challenges, AI Mashup Challenge, LinkedUp Challenge
Challenges Posters and Challenges: EU Project posters, ESWC Challenges, AI Mashup Challenge, LinkedUp Challenge

Huakang Li, Yi Liu, Mingyi Guo and Bei Xu.
Research papers Machine Learning: A Knowledge Based Approach for Tackling Mislabeled Multi-class Big Social Data

Pavan Kapanipathi, Prateek Jain, Chitra Venkataramani and Amit Sheth.
Research papers Social Web and Web Science: User Interests Identification on Twitter Using a Hierarchical Knowledge Base

Ilaria Tiddi, Mathieu D'Aquin and Enrico Motta.
Research papers Machine Learning: Dedalo: looking for Clusters Explanations in a Labyrinth of Linked Data

Axel-Cyrille Ngonga Ngomo, Mohamed Sherif and Klaus Lyko.
Research papers Machine Learning: Unsupervised Link Discovery Through Knowledge Base Repair

Johann Schaible, Thomas Gottron and Ansgar Scherp.
Research papers Linked Open Data II: Survey on Common Strategies of Vocabulary Reuse in Linked Open Data Modeling

Olivier Curé, Guillaume Blin, Dominique Revuz and David Célestin Faye.
Research papers Reasoning: WaterFowl: a Compact, Self-indexed and Inference-enabled RDF Store

Paolo Ciancarini, Angelo Di Iorio, Andrea Giovanni Nuzzolese, Silvio Peroni and Fabio Vitali.
Research papers Cognition and Semantic Web: Evaluating citation functions in CiTO: cognitive issues

Francesco Osborne, Giuseppe Scavo and Enrico Motta.
Research papers Social Web and Web Science: Identifying diachronic topic-based research communities by clustering shared research trajectories

Yannis Tzitzikas, Nikos Minadakis, Yannis Marketakis, Pavlos Fafalios, Carlo Allocca, Michalis Mountantonakis and Ioanna Zidianaki.
In-Use papers In-Use & Industrial III: Matware: Constructing and Exploiting Domain Specific Warehouses by Aggregating Semantic Data

Research papers Linked Open Data III: Generating and Summarizing Explanations for Linked Data

Arnab Dutta, Christian Meilicke and Simone Ponzetto.
Research papers Reasoning: A Probabilistic Approach for Integrating Heterogeneous Knowledge Sources

Posters and Demos Posters and Demos: Posters and Demos

-//Google Inc//Google Calendar 70.9054//EN

Benedikt Forchhammer, Anja Jentzsch and Felix Naumann.
PROFILES2014 Full papers: PROFILING and DYNAMICS: LODOP - Multi-Query Optimization for Linked Data Profiling Queries

Harshil Parikh, Jaimin Patel, Benjamin Farahmand and Rachana Pandey –
LinkedUp Paper presentation: TuvaLabs – Data Literacy Skills for a Brighter Future

Didier Cherix, Ricardo Usbeck, Andreas Both and Jens Lehmann
WaSABi Research papers: CROCUS: Cluster-based ontology data cleansing

Matt Thompson, Steve Battle, Julian Padget and Hideaki Takeda
HSWI Paper: ArtFinder: A Faceted Browser for Cross-Cultural Art Discovery

USEWOOD 2014 introduction by workshop chairs: Research Data Forensics: Introduction to the USEWOD Crowdsourcing Activity

USEWOOD 2014 Hands-on session: USEWOD Crowdsourcing Activity I: Participants Set-Up

USEWOOD 2014 Paper presentation: Man vs. Machine: Differences in SPARQL Queries

J. Fernando Śanchez-Rada: Research paper presentation + discussion
FEOSW2014 Research papers: A Linked Data Approach to Sentiment and Emotion Analysis of Twitter in the Financial Domain

Saud Aljaloud, Markus Luczak-Roesch, Tim Chown and Nicholas Gibbins
USEWOOD 2014 Paper presentation: Get All, Filter Details - On the Use of Regular Expressions in SPARQL Queries

Know@LOD Demo: Link Prediction in Very Large Directed Graphs: Exploiting Hierarchical Properties in Parallel

SePublica Regular Paper: Supporting Researchers with Semantic Literature Management

Benedikt Kämpgen, David Riepl and Jochen Klinger
SePublica Regular Paper: SMART Research using Linked Data - Sharing Research Data for Integrated Water Resources Management in the Lower Jordan Valley

WoDOOM 2014 Research paper: Repairing Learned Ontologies

LinkedUp Session 1, chair: Stefan Dietze
LinkedUp Welcome, keynote, paper presentations: LinkedUp Session 1

Pedro Macedo, Jorge Cardoso and Alexandre Miguel Pinto
LiME 2014 Research Papers: Enriching Electronic Programming Guides with Web Data

moderation: Bettina Berendt and Markus Luczak-Roesch
USEWOOD 2014 Hands-on session: Discussion, wrap-up, and closing remarks

Marios Meimaris, George Papastefanatos, Christos Pateritsas, Theodora Galani and Yannis Stavrakas.
PROFILES2014 Poster papers: PROFILING and EVOLUTION: Towards a Framework for Managing Evolving Information Resources on the Data Web

SMILE2014: Keynote Speech: Bart van Leeuwen

LinkedUp Session 2, chair: Mathieu d'Aquin
LinkedUp Paper presentations: LinkedUp Session 2

Mohamed Zaki: Research paper presentation + discussion
FEOSW2014 Research papers: Analyzing Stock Market Fraud Cases Using a Lin- guistics-Based Text Mining Approach

Sebastian Schaffert: Linking Media and Data using Apache Marmotta
LiME 2014 Keynote: Keynote Talk: Sebastian Schaffert

Zlatan Dragisic, Patrick Lambrix and Fang Wei-Kleiner.
WoDOOM 2014 Demonstration: A System for Debugging Missing Is-a Structure in EL Ontologies

Sergej Zerr, Mathieu D'Aquin, Ivana Marenzi, Davide Taibi, Adamou Alessandro and Stefan Dietze.
PROFILES2014 Full papers: SEARCH AND EXPLORATION: Towards Analytics and Collaborative Exploration of Social and linked Media for Technology-Enchanced Learning Scenarios

Angelo Di Iorio, Andrea Giovanni Nuzzolese, Silvio Peroni, David Shotton and Fabio Vitali
SePublica Regular Paper: Describing bibliographic references in RDF

Breakout session group reports and workshop closing

USEWOOD 2014 Hands-on session: USEWOD Crowdsourcing Activity III: Data collection via crowdsourcing (interlinking)

Renata Dividino, Thomas Gottron, Ansgar Scherp and Gerd Gröner.
PROFILES2014 Full papers: PROFILING and DYNAMICS: From Changes to Dynamics: Dynamics Analysis of Linked Open Data Sources

SW & Sentiment Analysis 2014 Discussion: Wrap-up Panel: the mutual dependencies between NLP and SW for SA

SALADS 2014 papers: A Data API With Security and Graph-Level Access Control

LinkedUp Paper presentation: AGRIS – exploiting bibliographic records to create rich Linked Open Data page (remote presentation)

Lotte Baltussen, Erwin Verbruggen, Brigitte Jansen, Johan Oomen, Bauke Freiburg and Liesbeth van de Kar
LiME 2014 Demo Papers: Na de bevrijding XL: Expanding a Historical Television Series with Archival Sources

WoDOOM 2014 Invited talk: Foundations for revising networks of ontologies

Diego Reforgiato, Aldo Gangemi, Andrea Nuzzolese, Sergio Consoli, Daria Spampinato and Valentina Presutti
SW & Sentiment Analysis 2014 Research Papers: Semantic Web-based Sentiment Analysis

Jacco van Ossenbruggen and Maria-Esther Vidal

EMPIRICAL2014: Evaluation in Semantic Web Research

Breakout brainstorming session on enterprise adoption and best practice challenges
WaSABi Breakout groups: Breakout session

Yves Vanrompay, Mario Cataldi, Marine Le Glouanec, Marie-Aude Aufaure and Myriam Lamolle
SW & Sentiment Analysis 2014 Research Papers: Sentiment Analysis for Dynamic User Preference Inference in Spoken Dialogue Systems

Sergio Consoli, Aldo Gangemi, Andrea Giovanni Nuzzolese, Silvio Peroni, Valentina Presutti, Diego Reforgiato Recupero, and Daria Spampinato
SMILE2014: Towards emergency vehicle routing using Geolinked Open Data: the case study of the Municipality of Catania

Vojtech Svatek, Simone Serra, Miroslav Vacura, Martin Homola and Jan Kluka.
WoDOOM 2014 Demonstration: B-Annot: Supplying Background Model Annotations for Ontology Coherence Testing

LinkedUp Paper presentation: Visualization of Labour Conflicts in the Netherlands for last 700 years

PROFILES2014 Full papers: PROFILING and DYNAMICS: Of Sampling and Smoothing: Approximating Distributions over Linked Open Data

FEOSW2014 Keynote: Can Data Science prevent a new global economic crisis?

Kostyantyn Shchekotykhin, Gerhard Friedrich, Patrick Rodler and Philipp Fleiss.
WoDOOM 2014 Research paper: Interactive Ontology Debugging using Direct Diagnosis

Bettina Berendt and Markus Luczak-Roesch
USEWOOD 2014 Opening session: USEWOD 2014 - Welcome address

Know@LOD Paper: Parallel Search Through Statistical Semantic Spaces Leveraging Linked Open Data

Leonardo Lezcano, Michel Triana, Bridget Maher, Pat Henn, Carola Orrego, Lina Stieger, Hanna Schroeder, Susanne Druener, Sasa Sopka and Marcus Specht
LinkedUp Paper presentation: The electronic Discharge Letter (eDL) mobile app

Interactive Discussion Session Chair: Maria Maleshkova
SALADS 2014 closing: Interactive Discussion

SALADS 2014 papers: RESTful or RESTless - Current State of Today's Top Web APIs

LiME 2014 Opening: LiME 2014 Workshop Opening

SALADS 2014 papers: Linked Personal Dataspaces

Vanni Zavarella, Hristo Tanev, Ralf Steinberger, and Erik Van der Goot
SMILE2014: An Ontology-Based Approach to Social Media Mining for Crisis Management

Know@LOD Paper: Amending RDF Entities with New Facts

Mateusz Radzimski: Research paper presentation + discussion
FEOSW2014 Research papers: Predicting stocks returns correlations based on unstructured data sources

Invited Position Paper Daniele Dell'Agilio
EMPIRICAL2014: An Experience on Empirical Research about RDF Stream Processing

Felix Leif Keppmann and Steffen Stadtmüller
SALADS 2014 papers: Semantic RESTful APIs for Dynamic Data Sources

PROFILES2014 Poster papers: PROFILING and EVOLUTION: What's all the Data about? - Creating structured Profiles of Linked Data on the Web

Ricardo Kawase, Ujwal Gadiraju and Patrick Siehndel
LinkedUp Paper presentation: DBLPXplorer: Interactive Graphical Interfaces for the Computer Science Bibliography

Fajar J. Ekaputra, Marta Sabou, Estefania Serral and Stefan Biffl
SePublica Regular Paper: Supporting Information Sharing for Re-Use and Analysis of Scientific Research Publication Data

Heidelinde Hobel, Lisa Madlberger, Andreas Thoni, and Stefan Fenz
SMILE2014: Visualisation of User-Generated Event Information: Towards Geospatial Situation Awareness Using Hierarchical Granularity Levels

WaSABi Research papers: IRIS: A Protege Plug-in to Extract and Serialize Product Attribute Name-Value Pairs

Suvodeep Mazumdar, Vitaveska Lanfranchi, Neil Ireson, and Fabio Ciravegna
SMILE2014: Visual Analysis of Real-time Social Media for Emergency Response

Alan Meehan, Rob Brennan, Dave Lewis and Declan O’Sullivan
WaSABi Research papers: Mapping Representation based on Meta-data and SPIN for Localization Workflows

Michalis Mountantonakis, Carlo Allocca, Pavlos Fafalios, Nikos Minadakis, Yannis Marketakis, Cristina Lantzaki and Yannis Tzitzikas.
PROFILES2014 Full papers: PROFILING and DYNAMICS: Extending VoID for Expressing Connectivity Metrics of a Semantic Warehouse

Know@LOD Challenge: Linked Data Mining Challenge Session

Markel Vigo, Caroline Jay and Robert Stevens
HSWI Paper: Protégé4US: Harvesting Ontology Authoring Data with Protégé

SW & Sentiment Analysis 2014 Keynote: Invited Talk and/or Challenge report (TBC)

Lionel Médini, Pierre-Antoine Champin, Michaël Mrissa and Amélie Cordier
SALADS 2014 papers: Towards semantic resource mashups

USEWOOD 2014 Invited talk: The Lonesome LOD Cloud

Hassan Saif, Yulan He, Mirim Fernandez and Harith Alani
SW & Sentiment Analysis 2014 Research Papers: Adapting Sentiment Lexicons using Contextual Semantics for Twitter Sentiment Analysis

WoDOOM 2014 Research paper: Identifying Wrong Links between Datasets by Multi-dimensional Outlier Detection

SePublica Regular Paper: A review of ontologies for describing scholarly and scientific documents

Mohamed Ben Ellefi, Zohra Bellahsene, François Scharffe and Konstantin Todorov.
PROFILES2014 Poster papers: PROFILING and EVOLUTION: Towards Semantic Dataset Profiling

Marin Dimitrov, CTO Ontotext: Crossing the Chasm with Semantic Technologies
WaSABi Keynote: Keynote speech: Marin Dimitrov

USEWOOD 2014 Hands-on session: USEWOD Crowdsourcing Activity II: Data collection via crowdsourcing (research datasets and publications)

Mohammed Almashraee, Kia Teymourian, Dagmar Monett-Diaz and Adrian Paschke
SW & Sentiment Analysis 2014 Research Papers: Usage of Abstract Features in Semantic Sentiment Analysis

LinkedUp Introduction: Welcome & LinkedUp intro

Chun Lu, Philippe Laublet and Milan Stankovic
SALADS 2014 papers: Ricochet:Context and Complementarity-Aware, Ontology-based POIs Recommender System

SW & Sentiment Analysis 2014 Workshop Opening
SW & Sentiment Analysis 2014 Opening: SW & Sentiment Analysis 2014 Workshop Opening

LiME 2014 Demo Papers: The Media Ecology Project: Using Linked Data to Support Distributed Analysis of Visual Culture

HSWI Paper: Supporting Web Vocabulary Development by Automated Quality Assessment: Results of a Case Study in a Teaching Context

Mikko Koho, Eero Hyvönen and Aleksi Lehikoinen
SePublica Regular Paper: Ornithology Based on Linking Bird Observations with Weather Data

SALADS 2014 Keynote: Keynote (Steve Klabnik)

USEWOOD 2014 Invited talk: Crowdsourcing and the Web of Data

FEOSW2014 Discussion: Frontiers in Financial Data Analysis: new directions, trends & ideas

Dor Garbash, Eyal Rotbart, William Zeng, Brendan Fong, Erik Edstrom, Jacob Cole, Calvin Fong, Yael Ben Dov

WaSABi Research papers: Ontology Design Patterns: Adoption Challenges and Solutions

LinkedUp Paper presentation: Solvonauts : an open educational search engine (remote presentation)

Andy Moniz: Research paper presentation + discussion
FEOSW2014 Research papers: Predicting the impact of central bank communications on financial market investors interest rate expectations

Volha Bryl, Aliaksandr Birukou, Kai Eckert and Mirjam Kessler
SePublica Regular Paper: What's in the proceedings? Combining publisher's and researcher's perspectives

EMPIRICAL2014: The role of annotation in reproducibility

HSWI Paper: Supporting Visual Exploration of Inter-organization Collaboration

Michel Visser, Simone Potenza and Romee Houben – Konnektid –
LinkedUp Paper presentation: Social Learning Anytime, Anywhere

Peter Bloem, Adianto Wibisono and Gerben De Vries
Know@LOD Paper: Simplifying RDF Data for Graph-Based Machine Learning

LiME 2014 Research Papers: Survey of Semantic Media Annotation Tools for the Web: Towards new Media Applications with Linked Media

Frank van Harmelen, Jacco van Ossenbruggen, and Maria-Esther Vidal
EMPIRICAL2014: Benchmarking is standing on trial for being unscientific

Yunjia Li, Raphaël Troncy, Mike Wald and Gary Wills
LiME 2014 Research Papers: Media Fragments Indexing using Social Media

Andreas Wagner, Peter Haase and Achim Rettinger.
PROFILES2014 Full papers: SEARCH AND EXPLORATION: Entity-based Data Source Contextualization for Searching the Web of Data

SePublica Discussion: Discussion / Polemics

Tatiana Lesnikova, Jérôme David and Jérôme Euzenat
Know@LOD Paper: Interlinking English and Chinese RDF Data Sets Using Machine Translation

Thanassis Tiropanis: Linked Data Affordances and Challenges for Web Observatories
PROFILES2014 Keynote: Keynote: Thanassis Tiropanis

WoDOOM 2014 Research paper: First experiments in cultural alignment repair

Jianliang Chen, Yuting Liu, Dipanwita Maulik, Linda Xu, Hao Zhang, Craig A. Knoblock, Pedro Szekely and Miel Vander Sande
LinkedUp Paper presentation: LODStories: Learning About Art by Building Multimedia Stories