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Improving semantic Web services discovery and ranking: A lightweight, integrated approach

 

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Opened Access Improving semantic Web services discovery and ranking: A lightweight, integrated approach
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Author: García Rodríguez, José María
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Date: 2016
Published in: AI Communications, 29 (1), 215-217.
Document type: Article
Abstract: Semantic Web services frameworks provide the means to automatically discover, rank, compose and invoke services according to user requirements and preferences. However, current preference models offer limited expressiveness and they are tightly coupled with underlying discovery and ranking mechanisms. Furthermore, these mechanisms present performance, interoperability and integration issues that prevent the uptake of semantic technologies in these scenarios. In this work, we discuss three interrelated contributions on preference modeling, discovery optimization, and flexible, integrated ranking, tackling specifically the identified challenges on those areas using a lightweight approach.
Cite: García Rodríguez, J.M. (2016). Improving semantic Web services discovery and ranking: A lightweight, integrated approach. AI Communications, 29 (1), 215-217.
Size: 257.7Kb
Format: PDF

URI: http://hdl.handle.net/11441/67604

DOI: 10.3233/AIC-140644

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This work is under a Creative Commons License: 
Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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