http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#1
date: 2008-10-26
tzid: GMT-5

Evidential Nearest-Neighbors Classification for Inductive ABox Reasoning
year: 2009
month: October
maker: Claudia d'Amato
maker: Nicola Fanizzi
type: InProceedings
author: Claudia d'Amato
author: Floriana Esposito
creator: Claudia d'Amato
abstract: In the line of our investigation on inductive methods for Semantic Web reasoning, we propose an alternative way for approximate ABox reasoning based on the analogical principle of the nearest-neighbors. Once neighbors of a test individual are selected, a combination rule descending from the Dempster-Shafer theory can join together the evidence provided by the neighbor individuals. We show how to exploit the procedure for determining unknown class- and role-memberships or fillers for datatype properties which may be the basis for many further ABox inductive reasoning algorithms.
author list: http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#4
url: http://ceur-ws.org/Vol-527/paper3.pdf
creator: Floriana Esposito
maker: Floriana Esposito
creator: Nicola Fanizzi
author: Nicola Fanizzi
is part of: proceedings

5th International Workshop on Uncertainty Reasoning for the Semantic Web
type: WorkshopEvent
has related document: proceedings
dtend: http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#1
dtstart: http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#2
url: http://c4i.gmu.edu/ursw/2009/
has acronym: URSW2009
has role: pcmember
has role: organisingcommitteemember
complete graph: complete

Sergej Sizov
holds role: pcmember
type: Person

Paulo C. G. Costa
holds role: organisingcommitteemember
holds role: pcmember
type: Person
made: Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil

Pavel Smrz
holds role: organisingcommitteemember
holds role: pcmember
type: Person

Yung Peng
holds role: pcmember
type: Person

Peter Vojtas
holds role: pcmember
type: Person

Position paper: Uncertainty reasoning for linked data
creator: Dave Reynolds
month: October
year: 2009
is part of: proceedings
type: InProceedings
maker: Dave Reynolds
url: http://ceur-ws.org/Vol-527/pospaper3.pdf
abstract: Linked open data offers a set of design patterns and conventions for sharing data across the semantic web. In this position paper we enumerate some key uncertainty representation issues which apply to linked data and suggest approaches to tackling them. We suggest the need for vocabularies to enable representation of link certainty, to handle ambiguous or imprecise values and to express sets of assumptions based on named graph combinators.
subject: Linked open data
subject: uncertainty reasoning
subject: Semantic Web
author: Dave Reynolds
author list: http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#9

Dave Robertson
holds role: pcmember
type: Person

Ontology Granulation Through Inductive Decision Trees
type: InProceedings
subject: ontology granulation
maker: Bart Gajderowicz
author: Bart Gajderowicz
author list: http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#3
creator: Alireza Sadeghian
abstract: The popularity of ontologies for representing the semantics behind many real-world domains has created a growing pool of ontologies on various topics. While different ontologists, experts, and organizations create the vast majority of ontologies, often for closed world systems, their domains frequently overlap in an open world system, such as the Semantic Web. These overlapping ontologies sometimes model similar or matching theories, that may be inconsistent. To assist in the reuse of these ontologies, this paper describes a technique for enriching manually created ontologies by supplementing them with inductively derived rules, and reducing the number of inconsistencies. The derived rules are translated from decision trees created by executing a tree based data mining algorithm with probability measures over the data being modeled. These rules can be used to revise the ontology adding a higher level of granularity, in order to identify possible similarities missed by the original ontologists. We then discuss how this may be applied to ontology matching. We demonstrate the application of our technique by presenting an example, and discuss how various data types may be treated to generalize the semantics of an ontology for an open world system.
subject: decision trees
subject: probabilistic ontology
subject: ontology matching
is part of: proceedings
year: 2009
maker: Alireza Sadeghian
author: Alireza Sadeghian
creator: Bart Gajderowicz
month: October
url: http://ceur-ws.org/Vol-527/paper4.pdf

proceedings
has part: Fuzzy Taxonomies for Creative Knowledge Discovery
editor: Michael Pool
related to event: 5th International Workshop on Uncertainty Reasoning for the Semantic Web
has part: BeliefOWL: an Evidential Representation in OWL Ontology
has part: Position paper: Uncertainty reasoning for linked data
editor: Kathryn B. Laskey
month: October
has part: Evidential Nearest-Neighbors Classification for Inductive ABox Reasoning
year: 2009
editor: Trevor Martin
has part: Ontology Granulation Through Inductive Decision Trees
editor list: http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#12
editor: Matthias Nickles
editor: Thomas Lukasiewicz
editor: Paulo C. G. Costa
booktitle: Proceedings of 5th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2009)
type: Proceedings
editor: Pavel Smrz
has part: Axiomatic First-Order Probability
editor: Fernando Bobillo
editor: Nicola Fanizzi
editor: Claudia d'Amato
has part: Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil
has part: An Algorithm for Learning with Probabilistic Description Logics
has part: Combining Semantic Web Search with the Power of Inductive Reasoning
editor: Kenneth J. Laskey

Claudia d'Amato
holds role: organisingcommitteemember
holds role: pcmember
type: Person
made: Evidential Nearest-Neighbors Classification for Inductive ABox Reasoning
made: Combining Semantic Web Search with the Power of Inductive Reasoning

Marcelo Ladeira
holds role: pcmember
type: Person
made: Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil

Amira Essaid
type: Person
made: BeliefOWL: an Evidential Representation in OWL Ontology

Carlos Henrique Ribeiro
holds role: pcmember
type: Person

Bart Gajderowicz
type: Person
made: Ontology Granulation Through Inductive Decision Trees

Axiomatic First-Order Probability
url: http://ceur-ws.org/Vol-527/paper5.pdf
abstract: Most languages for the Semantic Web have their logical basis in some fragment of first-order logic. Thus, integrating first-order logic with probability is fundamental for representing and reasoning with uncertainty in the semantic web. Defining semantics for probability logics presents a dilemma: a logic that assigns a real-valued probability to any first-order sentence cannot be axiomatized and lacks a complete proof theory. This paper develops a first-order axiomatic theory of probability in which probability is formalized as a function mapping Gödel numbers to elements of a real closed field. The resulting logic is fully first-order and recursively axiomatizable, and therefore has a complete proof theory. This gives rise to a plausible reasoning logic with a number of desirable properties: the logic can represent arbitrarily fine-grained degrees of plausibility intermediate between proof and disproof; all mathematical and logical assumptions can be explicitly represented as finite computational structures accessible to automated reasoners; contradictions can be discovered in finite time; and the logic supports learning from observation.
maker: Kathryn B. Laskey
subject: probability
author list: http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#8
creator: Kathryn B. Laskey
year: 2009
subject: first-order logic
type: InProceedings
is part of: proceedings
author: Kathryn B. Laskey
month: October

Siyao Zheng
type: Person
made: Fuzzy Taxonomies for Creative Knowledge Discovery

Trevor Martin
holds role: organisingcommitteemember
holds role: pcmember
type: Person
made: Fuzzy Taxonomies for Creative Knowledge Discovery

Kathryn B. Laskey
holds role: organisingcommitteemember
holds role: pcmember
type: Person
made: Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil
made: Axiomatic First-Order Probability

Floriana Esposito
type: Person
made: Evidential Nearest-Neighbors Classification for Inductive ABox Reasoning

Anders L. Madsen
holds role: pcmember
type: Person

Laécio Santos
type: Person
made: Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil

Rommel Carvalho
holds role: pcmember
type: Person
made: Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil

Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil
maker: Paulo C. G. Costa
maker: Shou Matsumoto
subject: ontology
author list: http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#7
url: http://ceur-ws.org/Vol-527/paper1.pdf
creator: Paulo C. G. Costa
maker: Rommel Carvalho
author: Marcelo Ladeira
creator: Laécio Santos
creator: Kathryn B. Laskey
subject: probabilistic ontology
year: 2009
maker: Kathryn B. Laskey
author: Laécio Santos
author: Paulo C. G. Costa
creator: Rommel Carvalho
subject: knowledge fusion
author: Shou Matsumoto
type: InProceedings
maker: Marcelo Ladeira
subject: UnBBayes
is part of: proceedings
author: Kathryn B. Laskey
creator: Marcelo Ladeira
month: October
subject: PR-OWL
creator: Shou Matsumoto
subject: MEBN
abstract: To cope with society’s demand for transparency and corruption prevention, the Brazilian Federal General Comptroller (CGU) has carried out a number of actions, including: awareness campaigns aimed at the private sector; campaigns to educate the public; research initiatives; and regular inspections and audits of municipalities and states. Although CGU has collected information from hundreds of different sources - Revenue Agency, Federal Police, and others - the process of fusing all this data has not been efficient enough to meet the needs of CGU’s decision makers. Therefore, it is natural to change the focus from data fusion to knowledge fusion. As a consequence, traditional syntactic methods must be augmented with techniques that represent and reason with the semantics of databases. However, commonly used approaches fail to deal with uncertainty, a dominant characteristic in corruption prevention. This paper presents the use of Probabilistic OWL (PR-OWL) to design and test a model that performs information fusion to detect possible frauds in procurements involving Federal money. To design this model, a recently developed tool for creating PR-OWL ontologies was used with support from PR-OWL specialists and careful guidance from a fraud detection specialist from CGU.
subject: procurement fraud detection
maker: Laécio Santos
author: Rommel Carvalho

Umberto Straccia
holds role: pcmember
type: Person

Daniel Sánchez
holds role: pcmember
type: Person

Fernando Bobillo
holds role: organisingcommitteemember
holds role: pcmember
type: Person

An Algorithm for Learning with Probabilistic Description Logics
author: Fabio G. Cozman
url: http://ceur-ws.org/Vol-527/paper6.pdf
type: InProceedings
abstract: Probabilistic Description Logics are the basis of ontologies in the Semantic Web. Knowledge representation and reasoning for these logics have been extensively explored in the last years; less attention has been paid to techniques that learn ontologies from data. In this paper we report on algorithms that learn probabilistic concepts and roles. We present an initial effort towards semi-automated learning using probabilistic methods. We combine ILP (Inductive Logic Programming) methods and a probabilistic classifier algorithm (search for candidate hypotheses is conducted by a Noisy-OR classifier). Preliminary results on a real world dataset are presented.
creator: Fabio G. Cozman
creator: José Eduardo Ochoa Luna
is part of: proceedings
maker: José Eduardo Ochoa Luna
maker: Fabio G. Cozman
author: José Eduardo Ochoa Luna
author list: http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#6
month: October
year: 2009

José Eduardo Ochoa Luna
type: Person
made: An Algorithm for Learning with Probabilistic Description Logics

Andreas Tolk
holds role: pcmember
type: Person

Kenneth J. Laskey
holds role: organisingcommitteemember
holds role: pcmember
type: Person

Jeff Z. Pan
holds role: pcmember
type: Person

Michael Pool
holds role: organisingcommitteemember
holds role: pcmember
type: Person

Giorgos Stoilos
holds role: pcmember
type: Person

Thomas Lukasiewicz
holds role: organisingcommitteemember
holds role: pcmember
type: Person
made: Combining Semantic Web Search with the Power of Inductive Reasoning

Fuzzy Taxonomies for Creative Knowledge Discovery
year: 2009
subject: uncertainty in Semantic Web
abstract: A systematic form of creative knowledge discovery is outlined, requiring taxonomies to generalise knowledge structures and mappings between taxonomies to find parallels between knowledge structures from different domains. These share many of the features needed to handle uncertainty in the semantic web, and results will be relevant to the URSW community.
creator: Andrei Majidian
creator: Siyao Zheng
author: Siyao Zheng
month: October
url: http://ceur-ws.org/Vol-527/pospaper2.pdf
author: Andrei Majidian
type: InProceedings
maker: Trevor Martin
author: Trevor Martin
is part of: proceedings
subject: fuzzy association rules
subject: fuzzy taxonomy
creator: Trevor Martin
author list: http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#11
subject: creative knowledge discovery
maker: Andrei Majidian
maker: Siyao Zheng

Georg Gottlob
type: Person
made: Combining Semantic Web Search with the Power of Inductive Reasoning

http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#2
date: 2008-10-26
tzid: GMT-5

Combining Semantic Web Search with the Power of Inductive Reasoning
url: http://ceur-ws.org/Vol-527/paper2.pdf
creator: Nicola Fanizzi
maker: Nicola Fanizzi
creator: Claudia d'Amato
is part of: proceedings
author: Thomas Lukasiewicz
creator: Bettina Fazzinga
maker: Thomas Lukasiewicz
month: October
creator: Georg Gottlob
year: 2009
maker: Claudia d'Amato
author: Bettina Fazzinga
maker: Georg Gottlob
maker: Bettina Fazzinga
author list: http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#5
author: Nicola Fanizzi
type: InProceedings
author: Claudia d'Amato
creator: Thomas Lukasiewicz
abstract: Extensive research activities are recently directed towards the Semantic Web as a future form of the Web. Consequently, Web search as the key technology of the Web is evolving towards some novel form of Semantic Web search. A very promising recent approach to such SemanticWeb search is based on combining standard Web search with ontological background knowledge and using standard Web search engines as the main inference motor of Semantic Web search. In this paper, we propose to further enhance this approach to Semantic Web search by the use of inductive reasoning techniques. This adds especially the important ability to handle inconsistencies, noise, and incompleteness, which are very likely to occur in distributed and heterogeneous environments, such as the Web. We report on a prototype implementation of the new approach and extensive experimental results.
author: Georg Gottlob

Andrei Majidian
type: Person
made: Fuzzy Taxonomies for Creative Knowledge Discovery

Matthias Nickles
holds role: organisingcommitteemember
holds role: pcmember
type: Person

Ben Yaghlane
type: Person
made: BeliefOWL: an Evidential Representation in OWL Ontology

Shou Matsumoto
type: Person
made: Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil

Bettina Fazzinga
type: Person
made: Combining Semantic Web Search with the Power of Inductive Reasoning

Livia Predoiu
holds role: pcmember
type: Person

Nicola Fanizzi
holds role: organisingcommitteemember
holds role: pcmember
type: Person
made: Evidential Nearest-Neighbors Classification for Inductive ABox Reasoning
made: Combining Semantic Web Search with the Power of Inductive Reasoning

Guilin Qi
holds role: pcmember
type: Person

Johanna Voelker
holds role: pcmember
type: Person

Fabio G. Cozman
holds role: pcmember
type: Person
made: An Algorithm for Learning with Probabilistic Description Logics

Silvia Calegari
holds role: pcmember
type: Person

BeliefOWL: an Evidential Representation in OWL Ontology
author: Amira Essaid
subject: Semantic Web
url: http://ceur-ws.org/Vol-527/pospaper1.pdf
maker: Ben Yaghlane
subject: Dempster-Shafer theory
author list: http://lodlaundromat.org/.well-known/genid/908a3c909d397832b3009c50743e6ca4#10
creator: Ben Yaghlane
month: October
creator: Amira Essaid
subject: directed evidential network
year: 2009
is part of: proceedings
maker: Amira Essaid
type: InProceedings
author: Ben Yaghlane
subject: OWL
abstract: The OWL is a language for representing ontologies but it is unable to capture the uncertainty about the concepts for a domain. To address the problem of representing uncertainty, we propose in this paper, the theoretical aspects of our tool BeliefOWL which is based on evidential approach. It focuses on translating an ontology into a directed evidential network by applying a set of structural translation rules. Once the network is constructed, belief masses will be assigned to the different nodes in order to propagate uncertainties later.
subject: ontology representation
subject: ontology

Dave Reynolds
type: Person
made: Position paper: Uncertainty reasoning for linked data

Alireza Sadeghian
type: Person
made: Ontology Granulation Through Inductive Decision Trees