Mfsma: Multi-Feature Similarity Measuring Algorithm For Semantic Annotation Of Wsdl Documents

Keywords

Ontology; Semantic annotation; Semantic similarity; Semantic Web Services; Web Services

Abstract

Semantic annotation of WSDL (Web Services Description Language) document is an efficient, convenient, and practical method to implement Semantic Web Services. Semantic similarity is the backbone of semantic annotation. There are some limitations of previous semantic similarity measuring approaches. Most of them focused on measuring semantic similarity between concepts in a specific domain ontology. How- ever, terms used in Web Services often are from multiple domains with different knowledge sources, which makes traditional approaches not applicable. In addition, previous works provide low discrimination due to incomplete utilizing of the knowledge resources. To address these, we propose MFSMA (multi-feature similarity measuring algorithm) that consists of two parts as structural similarity and lexical similarity to measure se- mantic similarity. Our method combines three common used approaches (Edge-based, Feature-based, and Information Content-based) with mapping them to three proposed features (depth, width, and density) in structural representation. Finally, we implement a comparison experiment, and results show that our approach provides better discrimina- tion among different experiment sets. Theoretically, proposed approach can be applied in semantic annotation of any type user defined Web Services description documents.

Publication Date

1-1-2017

Publication Title

ICIC Express Letters, Part B: Applications

Volume

8

Issue

3

Number of Pages

655-663

Document Type

Article

Personal Identifier

scopus

Socpus ID

85014754232 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85014754232

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