Uncertainty Reasoning for the Semantic Web II

International Workshops URSW 2008-2010 Held at ISWC and UniDL 2010 Held at FLoC, Revised Selected Papers

  • Fernando Bobillo
  • Paulo C. G. Costa
  • Claudia d’Amato
  • Nicola Fanizzi
  • Kathryn B. Laskey
  • Kenneth J. Laskey
  • Thomas Lukasiewicz
  • Matthias Nickles
  • Michael Pool
Conference proceedings

DOI: 10.1007/978-3-642-35975-0

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7123)

Table of contents (17 papers)

  1. Front Matter
  2. Probabilistic and Dempster-Shafer Models

    1. PR-OWL 2.0 – Bridging the Gap to OWL Semantics
      Rommel N. Carvalho, Kathryn B. Laskey, Paulo C. G. Costa
      Pages 1-18
    2. Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil
      Rommel N. Carvalho, Shou Matsumoto, Kathryn B. Laskey, Paulo C. G. Costa, Marcelo Ladeira, Laécio L. Santos
      Pages 19-40
    3. Pronto: A Practical Probabilistic Description Logic Reasoner
      Pavel Klinov, Bijan Parsia
      Pages 59-79
  3. Fuzzy and Possibilistic Models

  4. Inductive Reasoning and Machine Learning

    1. PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation Using Probabilistic Methods
      Saminda Abeyruwan, Ubbo Visser, Vance Lemmon, Stephan Schürer
      Pages 217-236
    2. Semantic Web Search and Inductive Reasoning
      Claudia d’Amato, Nicola Fanizzi, Bettina Fazzinga, Georg Gottlob, Thomas Lukasiewicz
      Pages 237-261
    3. Ontology Enhancement through Inductive Decision Trees
      Bart Gajderowicz, Alireza Sadeghian, Mikhail Soutchanski
      Pages 262-281
    4. Assertion Prediction with Ontologies through Evidence Combination
      Giuseppe Rizzo, Claudia d’Amato, Nicola Fanizzi, Floriana Esposito
      Pages 282-299
  5. Hybrid Approaches

    1. Representing Uncertain Concepts in Rough Description Logics via Contextual Indiscernibility Relations
      Claudia d’Amato, Nicola Fanizzi, Floriana Esposito, Thomas Lukasiewicz
      Pages 300-314
    2. Efficient Trust-Based Approximate SPARQL Querying of the Web of Linked Data
      Kuldeep B. R. Reddy, P. Sreenivasa Kumar
      Pages 315-330
  6. Back Matter

About these proceedings

Introduction

This book contains revised and significantly extended versions of selected papers from three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2008, 2009, and 2010 or presented at the first international Workshop on Uncertainty in Description Logics (UniDL), held at the Federated Logic Conference (FLoC) in 2010. The 17 papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on probabilistic and Dempster-Shafer models, fuzzy and possibilistic models, inductive reasoning and machine learning, and hybrid approaches.

Keywords

fuzzy logic knowledge management linked data ontology learning probability theory

Editors and affiliations

  • Fernando Bobillo
    • 1
  • Paulo C. G. Costa
    • 2
  • Claudia d’Amato
    • 3
  • Nicola Fanizzi
    • 4
  • Kathryn B. Laskey
    • 5
  • Kenneth J. Laskey
    • 6
  • Thomas Lukasiewicz
    • 7
  • Matthias Nickles
    • 8
  • Michael Pool
    • 9
  1. 1.Department of Computer Science and Systems EngineeringUniversity of ZaragozaZaragozaSpain
  2. 2.Department of Systems Engineering and Operations ResearchGeorge Mason UniversityFairfaxUSA
  3. 3.Dipatimento di InformaticaUniversità degli Studi di BariBariItaly
  4. 4.Dipatimento di Informatica and CILAUniversità degli Studi di BariBariItaly
  5. 5.Department of Systems Engineering and Operations ResearchGeorge Mason UniversityFairfaxUSA
  6. 6.MIRTE CorporationMcLeanUSA
  7. 7.Department of Computer ScienceUniversity of OxfordOxfordUK
  8. 8.Technische Universität MünchenGarchingGermany
  9. 9.Goldman SachsJersey CityUSA

Bibliographic information

  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-35974-3
  • Online ISBN 978-3-642-35975-0
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349