Abstract
Information retrieval-based Web service discovery approach suffers from the semantic sparsity problem caused by lacking of statistical information when the Web services are described in short texts. To handle this problem, external information is often utilized to improve the discovery performance. Inspired by this, we propose a novel Web service discovery approach based on a neural topic model and leveraging Web service labels. More specifically, words in Web services are mapped into continuous embeddings, and labels are integrated by a neural topic model simultaneously for embodying external semantics of the Web service description. Based on the topic model, the services are interpreted into hierarchical models for building a service querying and ranking model. Extensive experiments on several datasets demonstrated that the proposed approach achieves improved performance in terms of F-measure. The results also suggest that leveraging external information is useful for semantic sparse Web service discovery.
Highlights
In the era of Big Data, a growing number of business enterprises worldwide are driven to deploy their business applications into Web services in both intranet and internet [1, 2]
One is the semantic sparsity problem resulting from short text descriptions of Web services that there is no sufficient information to express the full semantics of the Web service. e current Web service marketplaces often briefly describe the main functions, the providers, and the types of a Web service using short sentences which do not contain enough statistic information so as to hinder effective similarity computing and pose challenges to traditional
Transfer of external knowledge to enrich the semantic representation of short text documents has been proposed such as Tian et al [5] transfer external knowledge by using Gaussian Latent Dirichlet Allocation (LDA) and the word embedding model from auxiliary information to enhance the semantics of the Web services
Summary
In the era of Big Data, a growing number of business enterprises worldwide are driven to deploy their business applications into Web services in both intranet and internet [1, 2]. Mathematical Problems in Engineering service retrieval approaches [8, 9] Faced with this issue, transfer of external knowledge to enrich the semantic representation of short text documents has been proposed such as Tian et al [5] transfer external knowledge by using Gaussian LDA and the word embedding model from auxiliary information to enhance the semantics of the Web services. According to the above description, we propose a labelaided neural topic model (LNTM) derived from Gaussian LDA [13] which leverages word embeddings and external label information to improve Web service discovery. (1) We presented an approach that leverages pretrained word embeddings to enrich the semantics of Web service descriptions (2) We proposed a label-augmented neural topic model to retrieve the Web services based on word embeddings and categories of the Web services (3) We experimentally illustrate that the proposed approach outperforms several other approaches with higher evaluation metrics
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