Distributional semantics for ontology verification

As they grow in size, OWL ontologies tend to comprise intuitively incompatible statements,even when they remain logically consistent. This is true in particular of lightweight ontologies, especially the ones which aggregate knowledge from different sources. The article investigates how distributional semantics can help detect and repair violation of common sense in consistent ontologies, based on the identification of consequences which are unlikely to hold if the rest of the ontology does. A score evaluating the plausibility for a consequence to hold with regard to distributional evidence is defined, as well as several methods in order to decide which statements should be preferably amended or discarded. A conclusive evaluation is also provided, which consists in extending an input ontology with randomly generated statements, before trying to discard them automatically.

[1]  Asunción Gómez-Pérez,et al.  Did You Validate Your Ontology? OOPS! , 2012, ESWC.

[2]  Giancarlo Guizzardi,et al.  Validating Modal Aspects of OntoUML Conceptual Models Using Automatically Generated Visual World Structures , 2010, J. Univers. Comput. Sci..

[3]  Ondrej Sváb-Zamazal,et al.  Antipattern detection: how to debug an ontology without a reasoner , 2013, WoDOOM.

[4]  Bernhard Nebel,et al.  Belief Revision: Syntax based approaches to belief revision , 1992 .

[5]  Gerhard Friedrich,et al.  A General Diagnosis Method for Ontologies , 2005, SEMWEB.

[6]  Claudio Giuliano,et al.  Instance-Based Ontology Population Exploiting Named-Entity Substitution , 2008, COLING.

[7]  Bijan Parsia,et al.  Finding All Justifications of OWL DL Entailments , 2007, ISWC/ASWC.

[8]  Dan Klein,et al.  Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network , 2003, NAACL.

[9]  Matthew Horridge,et al.  Justification based explanation in ontologies , 2012 .

[10]  Stefan Schlobach,et al.  Non-Standard Reasoning Services for the Debugging of Description Logic Terminologies , 2003, IJCAI.

[11]  Gerhard Weikum,et al.  SOFIE: a self-organizing framework for information extraction , 2009, WWW '09.

[12]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[13]  Johanna Völker,et al.  A Kernel Revision Operator for Terminologies - Algorithms and Evaluation , 2008, International Semantic Web Conference.

[14]  Bernardo Magnini,et al.  Weakly Supervised Approaches for Ontology Population , 2008, EACL.

[15]  Philipp Cimiano,et al.  Ontology learning and population from text - algorithms, evaluation and applications , 2006 .

[16]  Silvia Bernardini,et al.  BootCaT: Bootstrapping Corpora and Terms from the Web , 2004, LREC.

[17]  Sebastian Rudolph,et al.  Advocatus Diaboli - Exploratory Enrichment of Ontologies with Negative Constraints , 2012, EKAW.

[18]  Renata Wassermann,et al.  Base Revision for Ontology Debugging , 2009, J. Log. Comput..

[19]  Stefanie N. Lindstaedt,et al.  Automatic Support for Formative Ontology Evaluation , 2010, EKAW.

[20]  Christian Bizer,et al.  DBpedia: A Multilingual Cross-domain Knowledge Base , 2012, LREC.

[21]  Stefan Schlobach.  Diagnosing Terminologies , 2005, AAAI.

[22]  Bijan Parsia,et al.  Repairing Unsatisfiable Concepts in OWL Ontologies , 2006, ESWC.

[23]  Johanna Völker,et al.  Towards large-scale , open-domain and ontology-based named entity classification 1 , 2005 .

[24]  Nathalie Aussenac-Gilles,et al.  Trimming a consistent OWL knowledge base, relying on linguistic evidence , 2015 .

[25]  Philipp Cimiano,et al.  Ontology Learning from Text: Methods, Evaluation and Applications , 2005 .