Algorithm for Web Service Discovery Based on Information Retrieval Using WordNet and Linear Discriminant Functions

Abstract

An algorithm based on information retrieval that applies the lexical database WordNet together with a linear discriminant function is proposed. It calculates the degree of similarity between words and their relative importance to support the development of distributed applications based on web services. The algorithm uses the semantic information contained in the Web Service Description Language specifications and ranks web services based on their similarity to the one the developer is searching for. It is applied to a set of 48 real web services in five categories, then compared them to four other algorithms based on information retrieval, showing an averaged improvement over all data between 0.6% and 1.9% in precision and 0.7% and 3.1% in recall for the top 15 ranked web services. The objective was to reduce the burden and time spent searching web services during the development of distributed applications, and it can be used as an alternative to current web service discovery systems such as brokers in the Universal Description, Discovery, and Integration (UDDI) platform.

Exploiting named entity recognition for improving syntactic-based web service discovery

“…Other approaches take advantage of WordNet to improve discoverability. Sotolongo et al [28] combine WordNet with a linear discriminant function. The algorithm parses the WSDL descriptions to extract documentation and name attributes.…”

Aggregated search of data and services

“…In [39] author propose an IR approach to Web Service discovery but they use the lexical database WordNet to consider the semantic meaning of words, enabling a more precise degree of similarity. Our approach, based on the use of a domain ontology allow more accurate keyword definitions than a generic lexical database such as WordNet.…”