Journal of Web Semantics Paper Published on a Lexico-Semantic Pattern Language for Learning Ontology Instances from Text

ECBI members Frederik Hogenboom and dr. Flavius Frasincar published a paper, which tackles learning ontology instances from text by proposing a lexico-semantic pattern language. The paper is published in pages 37-50 of volume 15, number 1, of the Journal of Web Semantics, an ISI first decile Elsevier journal.

The Semantic Web aims to extend the World Wide Web with a layer of semantic information, so that it is understandable not only by humans, but also by computers. At its core, the Semantic Web consists of ontologies that describe the meaning of concepts in a certain domain or across domains. The domain ontologies are mostly created and maintained by domain experts using manual, time-intensive processes. In this paper, Hogenboom and Frasincar propose a rule-based method for learning ontology instances from text that helps domain experts with the ontology population process. In this method, the authors define a lexico-semantic pattern language that, in addition to the lexical and syntactical information present in lexico-syntactic rules, also makes use of semantic information. Hogenboom and Frasincar show that the lexico-semantic patterns are superior to lexico-syntactic patterns with respect to efficiency and effectivity. When applied to event relation recognition in text-based news items in the domains of finance and politics using Hermes, an ontology-driven news personalization service, the proposed approach has a precision and recall of approximately 80% and 70%, respectively.

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