Semantic search for matching user requests with profiled enterprises

Semantic search is an important approach that promises significant improvements for customers to identify products of their interest. To achieve semantic search, enterprises need to publish semantically enriched descriptions of their offered goods and services; then a customer expresses its request in a easy, Google like fashion, providing a list of desired features. If enterprise offerings and customer requests are based on the same vocabulary (i.e., ontology) then offerings and requests can be semantically matched by using advanced semantic methods. In this paper we propose an ontology-based method aimed at finding the best matches between a user request and the services offered by different enterprises. We assume that in a given business ecosystem (in the paper, as an example, the tourism sector) a group of SMEs agreed on the adoption of a reference ontology, used to build the company profiles, with the offered services. Symmetrically, a user request, represented by a set of desired features, is expressed in terms of the reference ontology terminology (i.e., concepts). In this paper, we illustrate SemSim, a search method used to collectively search the SME profiles to identify the services that match at best the user request. SemSim is based on the semantic similarity notion derived from the well-known information content approach. An experimental assessment is presented showing that our proposal performs better than some of the most representative similarity search methods proposed in the literature.