Automatic domain-specific learning: towards a methodology for ontology enrichment

[EN] At the current rate of technological development, in a world where enormous amount of data are constantly created and in which the Internet is used as the primary means for information exchange, there exists a need for tools that help processing, analyzing and using that information. However, while the growth of information poses many opportunities for social and scientific advance, it has also highlighted the difficulties of extracting meaningful patterns from massive data. Ontologies have been claimed to play a major role in the processing of large-scale data, as they serve as universal models of knowledge representation, and are being studied as possible solutions to this. This paper presents a method for the automatic expansion of ontologies based on corpus and terminological data exploitation. The proposed ¿ontology enrichment method¿ (OEM) consists of a sequence of tasks aimed at classifying an input keyword automatically under its corresponding node within a target ontology. Results prove that the method can be successfully applied for the automatic classification of specialized units into a reference ontology.Financial support for this research has been provided by the DGI, Spanish Ministry of Education and Science, grant FFI2011-29798-C0201.Ureña Gómez-Moreno, P.; Mestre-Mestre, EM. (2017). Automatic domain-specific learning: towards a methodology for ontology enrichment. LFE. Revista de Lenguas para Fines Específicos. 23(2):63-85. http://hdl.handle.net/10251/148357S638523

Semi-Automated Development of Conceptual Models from Natural Language Text

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Lexical Acquisition with WordNet and the Mikrokosmos Ontology

This paper discusses an approach to augmenting a lexicon for knowledge-based machine translation (KBMT) with information derived from WordNet. The Mikrokosmos project at NMSU's Computing Research Laboratory has concentrated on the creation of the Spanish and Japanese lexicons, so the English lexicon is less developed. We investigated using WordNet as a means to autonmte p0rtions of the English lexicon development. Several heuristics axe used to find the WordNet synonym sets corresponding to the concepts in the Mikrokosmos language-independent ontology. Two of these heuristics exploit the WordNet /s-a hierarchy: one performs hierarchical matching of both taxonomies, and the other computes similarity based on frequency of defining words and their ancestors in a corpus. The result is a lexicon acquisition tool that produces plausible lexical mappings from English words into the Mikrokosmns ontology. Initial performance results axe included, which indicate good accuracy in the mappings