editors) (DFKI Saarbrücken, University of Karlsruhe, and ITC-irst) Amsterdam: IOS Press (Frontiers in artificial intelligence and applications, edited by J. Breuker et al., volume 123), 2005, v+171 pp; hardbound, ISBN 1-58603-523-1, $102.00, €85.00, £59.00 This volume is a collection of extended versions of papers first presented at workshops held at the European Conference on Artificial Intelligence and the International Conference on Knowledge Engineering and Management in 2004. The editors have all made significant contributions to the field of ontology learning and have organized some of the important workshops in the area. Ontology learning has become a major area of research within the wider area of artificial intelligence and natural language processing. This is largely due to the adoption of ontologies (especially formal ontology expressed in OWL) as the standard form of knowledge representation in the Semantic Web. The vast majority of researchers approach the challenge as one of learning ontologies from texts, rather than from other possible sources. Thus, this book is timely and representative of many of the core methodologies applied. Researchers dealing with the challenge of building ontologies from text are essentially building on the considerable array of methodologies developed in computational linguistics and natural language processing. By a judicious selection of techniques ranging from part-of-speech tagging, chunking, and parsing to clustering and IR methodologies, they attempt to deal with the three fundamental issues involved in constructing ontologies: associating terms, building hierarchies of terms and concepts, and identifying and labeling ontological relations. In many ways, ontology learning is a specialization of core computational linguistic ambitions such as automatic lexicon construction and semantic labeling of texts. The editors begin the volume with a short overview of the ontology-learning landscape and a brief account of the papers in the volume. They present an "ontology-learning layer cake," clearly influenced by Tim Berners-Lee's Semantic Web layer cake, which starts with terms as the foundation and works up through synonyms, concepts, concept hierarchies, and relations to rules at the top. The book is then divided into three sections dealing respectively with methods, evaluation, and applications. In the first chapter, "An information theoretic approach to taxonomy extraction for ontology learning," Pum-Mo Ryu and Key-Sun Choi extract taxonomies by using the relative specificity of a term to a domain to determine IS-A relations. They use a combination of internal NP structure ("inside information") and syntactic modifiers ("outside information") to calculate in information-theoretic terms the respective entropy of different terms. Their approach is interesting in the combination of techniques it uses. Using the MeSH thesaurus as a gold standard and Medline abstracts as a corpus, they present figures showing up to 87% precision. While their approach may be capable of confirming hierarchical relations, it is not apparent how easily it can be used to discover such relations.
An overview on ontology learning from text can be found in REF .
Ontology Learning from Text : Methods , Evaluation and Applications
Computational Linguistics
Computational Linguistics
Engineering Computer Science