MATAWS: A Multimodal Approach for Automatic WS Semantic Annotation

TL;DR

This paper introduces MATAWS, a fully automatic multimodal method for semantically annotating large collections of Web Service descriptions by leveraging parameter names, type names, and structures using WordNet and SUMO.

Contribution

It presents a novel multimodal approach that automates semantic annotation of large, realistic Web Service collections, addressing limitations of previous datasets.

Findings

Effective semantic annotation of large WS collections.

Utilizes latent semantics in parameter names, types, and structures.

Demonstrates efficiency on real-world syntactic WS descriptions.

Abstract

Many recent works aim at developing methods and tools for the processing of semantic Web services. In order to be properly tested, these tools must be applied to an appropriate benchmark, taking the form of a collection of semantic WS descriptions. However, all of the existing publicly available collections are limited by their size or their realism (use of randomly generated or resampled descriptions). Larger and realistic syntactic (WSDL) collections exist, but their semantic annotation requires a certain level of automation, due to the number of operations to be processed. In this article, we propose a fully automatic method to semantically annotate such large WS collections. Our approach is multimodal, in the sense it takes advantage of the latent semantics present not only in the parameter names, but also in the type names and structures. Concept-to-word association is performed by using Sigma, a mapping of WordNet to the SUMO ontology. After having described in details our annotation method, we apply it to the larger collection of real-world syntactic WS descriptions we could find, and assess its efficiency.