Scalable Web Service Composition with Partial Matches

Abstract. We will investigate scalable algorithms for automated Semantic Web Service Composition (WSC). Our notion of WSC is very general: it allows the generation of new constants by web service outputs; the composition semantics includes powerful background ontologies; and we use the most general notion of matching, partial matches, where several services can cooperate each covering only a part of a requirement. Herein, we define a first formalism. We show that automatic composition is very hard: even testing solutions is Π 2 p-complete. We identify a special case that covers many relevant WSC scenarios, and where solution testing is only coNP-complete. While coNP is still hard, in the area of planning under uncertainty, scalable tools have been developed that deal with the same complexity. In our next step, we will adapt the techniques underlying one of these tools to develop a scalable tool for WSC. In the long term, we will investigate richer formalisms, and accordingly adapt our algorithms.

Towards Scalable Web Service Composition With Partial Matches

We investigate scalable algorithms for automated composition (WSC) of Semantic Web Services. Our notion of WSC is very general: the composition semantics includes background knowledge and we use the most general notion of matching, partial matches, where several web services can cooperate, each covering only a part of a requirement. Unsurprisingly, automatic composition in this setting is very hard. We identify a special case with simpler semantics, which covers many relevant scenarios. We develop a composition tool for this special case. Our goal is to achieve scalability: we overcome large search spaces by guiding the search using heuristic techniques. The computed solutions are optimal up to a constant factor. We test our approach on a simple, yet powerful real world use-case; the initial results attest the potential of the approach.