Abstract
Web service discovery is a fundamental task in service-oriented architectures which searches for suitable web services based on users’ goals and preferences. In this paper, we present a novel service discovery approach that can support user queries with various-size-grained text elements. Compared with existing approaches that only support semantics matchmaking in single texture granularity (either word level or paragraph level), our approach enables the requester to search for services with any type of query content with high performance, including word, phrase, sentence, or paragraph. Specifically, we present an unsupervised Bayesian probabilistic model, bi-Directional Sentence-Word Topic Model (bi-SWTM), to achieve semantic matchmaking between possible textual types of queries (word, phrase, sentence, paragraph) and the texts in web service descriptions, by mapping words and sentences in the same semantic space. The bi-SWTM captures textual semantics of the words and sentences in a probabilistic simplex, which provides a flexible method to build the semantic links from user queries to service descriptions. The novel approach is validated using a collection of comprehensive experiments on ProgrammableWeb data. The results demonstrate that the bi-SWTM outperforms state-of-the-art methods on service discovery and classification. The visualization of the nearest-neighbored queries and descriptions shows the capability of our model on capturing the latent semantics of web services.
Highlights
The main contribution of the proposed bi-Directional Sentence-Word Topic Model (bi-SWTM) is that it learns the latent features of different level textual elements in the same computation semantic space, which brings the benefits in handling the complex user queries in service discovery
Compared with Latent Dirichlet Allocation (LDA) and Word2Vec, the bi-SWTM can achieve 0.97 accuracy values on the top five, which is sufficient to apply our model in real-world service discovery. 5.3 Comparison results of services discovery we conduct experiments on information retrieval (IR) to evaluate the performance of service discovery in terms of F-Measure and precision
We propose bi-SWTM, a novel approach to achieve the semantic matchmaking for the words and sentences in the retrieval tasks of service discovery
Summary
Along with the rapid advancement of World Wide Web technologies, web services have become the de facto standard for consumers to access software systems and resources over. World Wide Web the Internet [4]. The quantity of published web services on the Internet has been rapidly growing; they offer developers and users more resources to customize the IT services based on their goals and preferences [5]. Over 23,000 web services have been published at ProgrammableWeb by June, 2020, almost increased to five times since 2013. The overwhelming amount of services available makes it a critical challenge for developers to precisely select service candidates that meet specific requirements [5]. To precisely and efficiently search for services from large-scale repositories, many approaches on service discovery have been investigated as well as the related research tracks including service classification [21], clustering [43], selection [32, 40] and recommendation [1, 28] have been proposed
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