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

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.

Mashup Advisor: A Recommendation Tool for Mashup Development

“…The systems described in [3], [11], and [2] are examples of such efforts. The main goal of this body of work is to go beyond the exact matching of the inputs and outputs during service matching, and instead rely on the semantic similarity.…”

“…The MashupAdvisor uses an AI planner [2] in order to compute the service composition with the highest utility that will achieve a goal (selected concept) given the state of the world (partial mashup concepts). The utility function used to guide the search process depends on the plan popularity and the quality of semantic matching in the plan.…”

Assessing the effectiveness of automated service composition

“…Such approach-used by Agarwal et al [8], Akkiraju et al [9], Klusch and Gerber [29], McIlraith and Son [23], Rodríguez-Mier et al [30], Sohrabi et al [31], Traverso and Pistore [24], Wu et al [25], among others-has a few practical obstacles in order to be put into production scenarios. There is the need for complete formal descriptions of each service, sometimes requiring a detailed description of service interactions, as in [23,24], for example.…”

“…Using logic-enabled semantic languages provided by the Semantic Web (SW) [4] community, automated composition tools are able to find service compositions that meet the developer requirements, but only if the underlying services are correctly described in terms of their capabilities. Such approaches go beyond the typical Web service description by looking at enriched input and output descriptions [5][6][7], and pre-and postconditions [8,9].…”

Using Markov Decision Process Model with Logic Scoring of Preference Model to Optimize HTN Web Services Composition

“…However, with the rapid increase of Web services, increasingly complex requirement of business process in the real world automatic service composition requires a flexible mechanism to deal with changing service availability. AI planning has often been adopted for automated Web services composition, as exemplified by the methods presented in for example Sirin, Wu, Hendler, and Nau (2004), Schuschel and Weske (2004), Paik, Maruyama, and Huhns (2006), and Akkiraju, Srivastava, Ivan, Goodwin, and Syeda-Mahmood (2006) to handle this issue.…”