Use of Lexico-Syntactic Patterns for the Evaluation of Taxonomic Relations

In this paper we present an approach for the evaluation of taxonomic relations of restricted domain ontologies. We use the evidence found in corpora associated to the ontology domain for determining the validity of the taxonomic relations. Our approach employs lexico-syntactic patterns for evaluating taxonomic relations in which the concepts are totally different, and it uses a particular technique based on subsumption for those relations in which one concept is completely included in the other one. The integration of these two techniques has allowed to automatically evaluate taxonomic relations for two ontologies of restricted domain. The performance obtained was about 70% for one ontology of the e-learning domain, whereas we obtained around 88% for the ontology associated to the artificial intelligence domain.

[1]  Peter D. Turney Similarity of Semantic Relations , 2006, CL.

[2]  Nicola Guarino,et al.  Formal ontology, conceptual analysis and knowledge representation , 1995, Int. J. Hum. Comput. Stud..

[3]  Desislava Zhekova,et al.  Lexico-Syntactic Patterns for Automatic Ontology Building , 2011, RANLP Student Research Workshop.

[4]  Jian Su,et al.  ECNU: Effective Semantic Relations Classification without Complicated Features or Multiple External Corpora , 2010, SemEval@ACL.

[5]  Pushpak Bhattacharyya,et al.  Domain Specific Ontology Extractor For Indian Languages , 2012, ALR@COLING.

[6]  Guadalupe Aguado de Cea,et al.  Using Natural Language Patterns for the Development of Ontologies , 2008 .

[7]  Azucena Montes Rendón,et al.  BUAP: A First Approximation to Relational Similarity Measuring , 2012, *SEMEVAL.

[8]  Elena Montiel-Ponsoda,et al.  From Linguistic Patterns to Ontology Structures , 2009, TIA.

[9]  Daniel Jurafsky,et al.  Learning Syntactic Patterns for Automatic Hypernym Discovery , 2004, NIPS.

[10]  Matteo Negri,et al.  FBK_NK: A WordNet-Based System for Multi-Way Classification of Semantic Relations , 2010, SemEval@ACL.

[11]  Saif Mohammad,et al.  SemEval-2012 Task 2: Measuring Degrees of Relational Similarity , 2012, *SEMEVAL.

[12]  M. Kendall A NEW MEASURE OF RANK CORRELATION , 1938 .

[13]  Marek Hatala,et al.  Linguistic Patterns for Information Extraction in OntoCmaps , 2012, WOP.

[14]  Lluís Codina,et al.  La Web semántica , 2006 .

[15]  Azucena Montes Rendón,et al.  Determining the Degree of Semantic Similarity Using Prototype Vectors , 2013, MCPR.

[16]  Pablo Castells,et al.  A collaborative recommendation framework for ontology evaluation and reuse , 2006 .

[17]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[18]  Marti A. Hearst Automatic Acquisition of Hyponyms from Large Text Corpora , 1992, COLING.

[19]  Simonetta Montemagni,et al.  Combining Statistical Techniques and Lexico-syntactic Patterns for Semantic Relations Extraction from Text , 2008, SWAP.

[20]  Fabio Celli UNITN: Part-Of-Speech Counting in Relation Extraction , 2010, SemEval@ACL.

[21]  Svitlana Volkova,et al.  Boosting Biomedical Entity Extraction by Using Syntactic Patterns for Semantic Relation Discovery , 2010 .

[22]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .