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One of the challenging problem that Web service technology is now facing is effective service discovery. To solve the deficiencies of Web service description, matching and choosing under WSDL language, this paper presents a web service discovery method based on keyword clustering and concept expansion, mainly from the
With the advent of Web 2.0, RESTful web services are becoming increasingly popular to emphasize the web as platform. There are already many RESTful web services and the number of services is increasing rapidly. Thus, it can be difficult to find specific services using keyword based retrieval. To solve this problem, a
With the number of registered Web services growing, Identifying desired Web service is crucial for Web users. Current keyword based service search are inefficient in two main aspects: poor scalability and lack of semantics. Firstly ,the users are overwhelmed by the huge number of irrelevant services returned. Secondly
relevant to a keyword-based query can be retrieved only if they share many words. Recently, Word Embeddings emerged and tried to cope with this problem by representing words in a language as vectors in a continuous vector space. An interesting property of these vectors is that two different words with similar meaning are
tell the same story, share the same space-time extension, and they might be closely related to each other. Previous efforts in information retrieval and integration have been limited to classical keyword-based and content-based indexing and querying for multiple media documents. Furthermore, the concept-based approach
Traditional Web Service search methodology dependant on keywords is time consuming and inefficient. The search results are often inexact. The substantive reason is that, the description of Web Service lacks semantic information. Search Engines can't accomplish the communication automatically and intelligently between
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