Automatic learning of text-to-concept mappings exploiting WordNet-like lexical networks

ABSTRACT

A great jump towards the advent of the Semantic Web will take place when a critical mass of web resources is available for use in a semantic way. This goal can be reached by the creation of semantic meta-data in the publication workflow, or by the development of systems and applications able to associate semantics to resources (i.e., annotating them) automatically. Those applications should analyze the content of a web page and should be able to associate some ontology classes to it. One particular issue in this context is to define a suitable relationship between each concept of the ontology and some words (or, more in general, strings) which are expected to appear in resources dealing with that concept, playing the role of "triggers" suggesting the relevance of a given text fragment to a concept.We hereby propose an approach that, starting from a set of textual representations created by experts (synsets), is able to automatically widen their lexical coverage by computing new, larger synsets, increasing the capability of a semantic application to correctly recognize the ontology classes a document is related to. In such approach, the initial textual representations are integrated and augmented by exploiting lexical networks like WordNet, which contain syntactic information connected through semantic relationships. Some algorithms are proposed to avoid misleading terms and consequently to perform sense disambiguation in WordNet.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing

General Terms:
Algorithms

Keywords:
WordNet, semantic indexing, semantic web, synset