A neural model for unsupervised taxonomy enrichment

ABSTRACT

The most important prerequisite for the success of the Semantic Web research is the construction of complete and reliable domain ontologies. In this paper we describe an unsupervised framework for domain ontology enrichment based on mining domain text corpora. Specifically, we enrich the hierarchical backbone of an existing ontology, i.e. its taxonomy, with new domain-specific concepts. The framework is based on an extended model of hierarchical self-organizing maps. As being founded on an unsupervised neural network architecture, the framework can be applied to different languages and domains. Terms extracted by mining a text corpus encode contextual content information, in a distributional vector space. The enrichment behaves like a classification of the extracted terms into the existing taxonomy by attaching them as hyponyms for the nodes of the taxonomy. The experiments reported are in the "Lonely Planet" tourism domain. The taxonomy and the corpus are the ones proposed in the PASCAL ontology learning and population challenge. The experimental results prove that the quality of the enrichment is considerably improved by using semantics based vector representations for the classified (newly added) terms, like the document category histograms (DCH) and the document frequency times inverse term frequency (DF-ITF) weighting scheme.

REFERENCES

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INDEX TERMS

Keywords:
document category histograms (DCH), document frequency times inverse term frequency (DF-ITF) weighting scheme, extended growing hierarchical self-organizing maps (Enrich-GHSOM), taxonomy enrichment, unsupervised neural network