Solving Semantic Ambiguity to Improve Semantic Web based Ontology Matching

Presented at: Ontology Matching Workshop (OM2007)

by Jorge Gracia, Vanesa Lopez, Mathieu D'Aquin, Marta Sabou, Enrico Motta, Eduardo Mena

A new paradigm in Semantic Web research focuses on the development of a new generation of knowledge-based problem solvers, which can exploit the massive amounts of formally specified information available on the Web, to produce novel intelligent functionalities. An important example of this paradigm can be found in the area of Ontology Matching, where new algorithms, which derive mappings from an exploration of multiple and heterogeneous online ontologies, have been proposed. While these algorithms exhibit very good performance, they rely on merely syntactical techniques to anchor the terms to be matched to those found on the Semantic Web. As a result, their precision can be affected by ambiguous words. In this paper, we aim to solve these problems by introducing techniques from Word Sense Disambiguation, which validate the mappings by exploring the semantics of the ontological terms involved in the matching process. Specifically we discuss how two techniques, which exploit the ontological context of the matched and anchor terms, and the information provided by WordNet, can be used to filter out mappings resulting from the incorrect anchoring of ambiguous terms. Our experiments show that each of the proposed disambiguation techniques, and even more their combination, can lead to an important increase in precision, without having too negative an impact on recall.

Solving Semantic Ambiguity to Improve Semantic Web based Ontology Matching was presented at this event.


Resource URI on the dog food server: http://data.semanticweb.org/workshop/om/2007/papers/451
Same as: http://revyu.com/things/om-2007-paper-451-solving-semantic


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