Constructing virtual documents for ontology matching

TLDR

The paper proposes virtual documents as a cost‑effective linguistic matching technique for ontology matching. Virtual documents are weighted word collections that incorporate local and neighboring ontology information derived from RDF graph structure, and their similarity is computed via vector‑space methods for ontology matching. Experiments demonstrate that virtual documents outperform other linguistic matching methods in average F‑measure and are more cost‑effective.

Abstract

On the investigation of linguistic techniques used in ontology matching, we propose a new idea of virtual documents to pursue a cost-effective approach to linguistic matching in this paper. Basically, as a collection of weighted words, the virtual document of a URIref declared in an ontology contains not only the local descriptions but also the neighboring information to reflect the intended meaning of the URIref. Document similarity can be computed by traditional vector space techniques, and then be used in the similarity-based approaches to ontology matching. In particular, the RDF graph structure is exploited to define the description formulations and the neighboring operations. Experimental results show that linguistic matching based on the virtual documents is dominant in average F-Measure as compared to other three approaches. It is also demonstrated by our experiments that the virtual documents approach is cost-effective as compared to other linguistic matching approaches.