Web Services have become essential to the software industry as they represent reusable, remotely accessible functionality and data. Since Web Services must be discovered before being consumed, many discovery approaches applying classic Information Retrieval techniques, which store and process textual service descriptions, have arisen. These efforts are affected by term mismatch: a description relevant to a query can be retrieved only if they share many words. We present an approach to improve Web Service discoverability that automatically augments Web Service descriptions and can be used on top of such existing syntactic-based approaches. We exploit Named Entity Recognition to identify entities in descriptions and expand them with information from public text corpora, for example, Wikidata, mitigating term mismatch since it exploits both synonyms and hypernyms. We evaluated our approach together with classical syntactic-based service discovery approaches using a real 1274-service dataset, achieving up to 15.06% better Recall scores, and up to 17% Precision-at-1, 8% Precision-at-2 and 4% Precision-at-3.
Tourism Information System- Integration and Information Retrieval of Tourism Information Systems using Semantic Web Services
