SEMAPLAN: Combining Planning with Semantic Matching to Achieve Web Service Composition

Authors:
Rama Akkiraju;Biplav Srivastava;Anca-Andreea Ivan;Richard Goodwin;Tanveer Syeda-Mahmood
Affiliations:
IBM T. J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY, 10532, USA;IBM India Research Laboratory, Block 1, IIT Campus, Hauz Khaus, New Delhi, 11016, India;IBM T. J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY, 10532, USA;IBM India Research Laboratory, Block 1, IIT Campus, Hauz Khaus, New Delhi, 11016, India;IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120, USA
Venue:
ICWS '06 Proceedings of the IEEE International Conference on Web Services
Year:
2006

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Abstract

In this paper, we present a novel algorithm to compose Web services in the presence of semantic ambiguity by combining semantic matching and AI planning algorithms. Specifically, we use cues from domain-independent and domain-specific ontologies to compute an overall semantic similarity score between ambiguous terms. This semantic similarity score is used by AI planning algorithms to guide the searching process when composing services. Experimental results indicate that planning with semantic matching produces better results than planning or semantic matching alone. The solution is suitable for semi-automated composition tools or directory browsers.