Catálogo de publicaciones - libros

Determining semantic similarity between words, concepts and phrases is important in many areas within Artificial Intelligence. This includes the general areas of information retrieval, data mining, and natural language processing. Existing approaches have primarily focused on noun to noun synonym comparison. We propose a new technique for the comparison of general expressions that combines web searching with Latent Semantic Analysis. This technique is more general than previous approaches, as it is able to match similarities between multi-word expressions, abbreviations, and alpha-numeric phrases. Consequently, it can be applied to more complex comparison problems such as ontology alignment.

Given that pairwise similarity computations are essential in ontology learning and data mining, we propose a similarity framework that is based on a conventional Web search engine. There are two main aspects that we can benefit from utilizing a Web search engine. First, we can obtain the freshest content for each term that represents the up-to-date knowledge on the term. This is particularly useful for dynamic ontology management in that ontologies must evolve with time as new concepts or terms appear. Second, in comparison with the approaches that use the certain amount of crawled Web documents as corpus, our method is less sensitive to the problem of data sparseness because we access as much content as possible using a search engine. At the core of our proposed methodology, we present two different measures for similarity computation, a mutual information based and a feature-based metric. Moreover, we show how the proposed metrics can be utilized for modifying existing ontologies. Finally, we compare the extracted similarity relations with semantic similarity using WordNet. Experimental results show that our method can extract topical relations between terms that are not present in conventional concept-based ontologies.