Efficient and scalable discovery mechanisms are critical for enabling service-oriented architectures on the Semantic Web. The majority of currently existing approaches focuses on centralized architectures, and deals with efficiency typically by pre-computing and storing the results of the semantic matcher for all possible query concepts. Such approaches, however, fail to scale with respect to the number of service advertisements and the size of the ontologies involved. On the other hand, this paper presents an efficient and scalable index-based method for Semantic Web service discovery that allows for fast selection of services at query time and is suitable for both centralized and P2P environments. We employ a novel encoding of the service descriptions, allowing the match between a request and an advertisement to be evaluated in constant time, and we index these representations to prune the search space, reducing the number of comparisons required. Given a desired ranking function, the search algorithm can retrieve the top-k matches progressively, i.e., better matches are computed and returned first, thereby further reducing the search engine's response time. We also show how this search can be performed efficiently in a suitable structured P2P overlay network. The benefits of the proposed method are demonstrated through experimental evaluation on both real and synthetic data.
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