Semantic search for matching user requests with profiled enterprises
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- 作者：Anna Formica anna.formica@iasi.cnr.itAuthor Vitae ; Michele Missikoff michele.missikoff@iasi.cnr.itAuthor Vitae ; Elaheh Pourabbas elaheh.pourabbas@iasi.cnr.itAuthor Vitae ; Francesco Taglino ; francesco.taglino@iasi.cnr.itAuthor Vitae
- 关键词：Similarity reasoning ; Weighted Reference Ontology ; Information content ; Digital resources
- 刊名：Computers in Industry
- 出版年：2013
- 出版时间：April, 2013
- 年：2013
- 卷：64
- 期：3
- 页码：191-202
- 全文大小：1000 K
文摘
Semantic search is an important approach that promises significant improvements for customers to identify products of their interest. To perform semantic search, enterprises need to publish semantically enriched descriptions of their offered goods and services; then a customer expresses his/her request, in an easy Google like fashion, by providing a list of desired features. If enterprise offerings and customer requests are based on the same vocabulary (i.e., ontology), they 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 agree on the adoption of a reference ontology, used to build the company profiles on the basis of the offered services. Accordingly, 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 method used to collectively search the SME profiles to identify the services that match at best the user request. SemSim is based on the well-known information content approach used to evaluate the semantic similarity between concepts. The experimental results show that our proposal performs better than some of the most representative similarity search methods proposed in the literature.