Lexically Evaluating Ontology Triples Generated Automatically from Texts

Our purpose is to present a method to lexically evaluate the results of extracting in an unsupervised way material from text corpora to build ontologies. We have worked on a legal corpus (EU VAT directive) consisting of 43K words. The unsupervised text miner has produced a set of triples. These are to be used as preprocessed material for the construction of ontologies from scratch. A quantitative scoring method (coverage, accuracy, recall and precision metrics resulting in a 38.68%, 52.1%, 9.84% and 75.81% scores respectively) has been defined and applied.

[1]  Yorick Wilks,et al.  Data Driven Ontology Evaluation , 2004, LREC.

[2]  George Kingsley Zipf,et al.  Human Behaviour and the Principle of Least Effort: an Introduction to Human Ecology , 2012 .

[3]  Robert Meersman,et al.  On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE , 2003, Lecture Notes in Computer Science.

[4]  Marta Sabou,et al.  Extracting ontologies from software documentation: a semi-automatic method and its evaluation , 2004 .

[5]  Peter Spyns,et al.  Evaluating DOGMA-lexons Generated Automatically from a Text Corpus , 2004, LSTKM@EKAW.

[6]  Paola Velardi,et al.  Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites , 2004, CL.

[7]  Nicola Guarino,et al.  Ontologies and Knowledge Bases. Towards a Terminological Clarification , 1995 .

[8]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993 .

[9]  Lee Gillam,et al.  Ontology Via Terminology ? , 2003 .

[10]  Roberto Basili,et al.  Identification of Relevant Terms to Support the Construction of Domain Ontologies , 2001, HTLKM@ACL.

[11]  Walter Daelemans,et al.  Mining for Lexons: Applying Unsupervised Learning Methods to Create Ontology Bases , 2003, CoopIS/DOA/ODBASE.

[12]  Nicolaas J. I. Mars,et al.  Towards very large knowledge bases, knowledge building and knowledge sharing 1995 , 1995 .

[13]  Peter Spyns,et al.  Unsupervised Text Mining for the Learning of DOGMA-inspired Ontologies , 2005 .

[14]  Ted Pedersen,et al.  WordNet::Similarity - Measuring the Relatedness of Concepts , 2004, NAACL.

[15]  Walter Daelemans,et al.  Cascaded Grammatical Relation Assignment , 1999, EMNLP.

[16]  Michael Uschold.  Where Are the Semantics in the Semantic Web? , 2003, AI Mag..

[17]  Walter Daelemans,et al.  Automatic Initiation of an Ontology , 2004, CoopIS/DOA/ODBASE.

[18]  Michael Uschold,et al.  Ontologies: principles, methods and applications , 1996, The Knowledge Engineering Review.

[19]  Hans Peter Luhn,et al.  The Automatic Creation of Literature Abstracts , 1958, IBM J. Res. Dev..

[20]  Robert Meersman,et al.  Ontologies and Databases: More than a Fleeting Resemblance , 2002 .

[21]  Robert Meersman,et al.  Data modelling versus ontology engineering , 2002, SGMD.

[22]  Peter Spyns,et al.  Ontologies: a revamped cross-disciplinary buzzword or a truly promising interdisciplinary research topic? , 2021, Linguistica Antverpiensia, New Series – Themes in Translation Studies.

[23]  Tim Berners-Lee,et al.  Weaving The Web: The Original Design And Ultimate Destiny of the World Wide Web , 1999 .

[24]  Christopher Brewster,et al.  Book Review: Ontology Learning from Text: Methods, Evaluation and Applications, edited by Paul Buitelaar, Philipp Cimiano and Bernado Magnini , 2005, CL.

[25]  Robert Meersman,et al.  On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE , 2004, Lecture Notes in Computer Science.

[26]  Steffen Staab,et al.  Measuring Similarity between Ontologies , 2002, EKAW.