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

The use of planning for automated and semi-automated composition of web services has enormous potential to reduce costs and improve quality in inter and intra-enterprise business process integration. Composing existing Web services to deliver new functionality is a difficult problem as it involves resolving semantic, syntactic and structural differences among the interfaces of a large number of services. Unlike most planning problems, it can not be assumed that web services are described using terms from a single domain theory. While service descriptions may be controlled to some extent in restricted settings (e.g., intra-enterprise integration), in web-scale open integration, lack of common, formalized service descriptions prevent the direct application of standard planning methods. In this paper, we present a novel algorithm to compose webmservices 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. In addition, we integrate semantic and ontological matching with an indexing method, which we call attribute hashing, to enable fast lookup of semantically related concepts. Experimental results indicate that planning with semantic matching produces better results than planning or semantic matching alone. The solution is suitable for semiautomated composition tools or directory browsers.