Transferring auxiliary knowledge to enhance heterogeneous web service clustering

Tian, Gang; Sun, Chengai; He, Keqing; Ji, Xiang-min

doi:10.1504/ijhpcn.2016.074669

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…There are two ways to alleviate the problem of poor recall in discovering non‐semantic Web services using search engines . One is to perform a broad searching and get a potentially large number of Web services, most of which may not be of interest to users.…”

Section: Introductionmentioning

confidence: 99%

“…2 There are two ways to alleviate the problem of poor recall in discovering non-semantic Web services using search engines. 3 One is to perform a broad searching and get a potentially large number of Web services, most of which may not be of interest to users. The other is to enhance the capability of the search engine by clustering services into similar functional groups after the search engine crawls the descriptions of Web services.…”

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confidence: 99%

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Tagging augmented neural topic model for semantic sparse Web service discovery

Tian

Liu

Peng

et al. 2018

Concurrency and Computation

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Search engine based Web service discovery model suffers from the semantic sparsity problem due to the fact that Web services are described in short texts, which in turn leads to poor recall. To address this issue, external information that enriches the semantics of the Web service and improves discovery performance has been highly concerned. In light of this, we propose a novel Web service discovery approach that uses the neural topic model, which seamlessly integrates tagging information and word embedding for semantic sparsity problem. More specifically, instead of clustering Web services as done in most existing service discovery approaches, we use word embedding to map the words as continuous embeddings to embody external semantics of the service description. We also leverage the neural topic model in service discovery, which takes continuous word distribution as the input and interprets the Web service description as a hierarchical model. Based on the neural topic model and word embedding, we propose an efficient Web service query and ranking approach. Experiments conducted on a real-world Web service dataset demonstrate the effectiveness of the proposed approach. KEYWORDStagging augmented neural topic model, Web service discovery, word embeddings

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Tagging augmented neural topic model for semantic sparse Web service discovery

Tian

Liu

Peng

et al. 2018

Concurrency and Computation

Self Cite

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Degree of Match-Based Hierarchical Clustering Technique for Efficient Service Discovery

Premalatha

Subasree²

2018

Advances in Intelligent Systems and Computing

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Clustering and Association Rules for Web Service Discovery and Recommendation: A Systematic Literature Review

2019

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The purpose of this study is to identify, summarize, and systematically compare various clustering and association rule techniques for web service discovery and recommendation, identify the most common data sets used in the extant literature, and highlight current trends and future research directions. Following the methodology of Kitchenham and Charters (Guidelines for performing Systematic Literature reviews in Software Engineering, 2007) for a systematic literature review (SLR), a set of research questions are designed. Six digital databases are searched. A total of 4581 papers were initially retrieved, and a rigorous two-stage scanning process resulted in 66 relevant papers. Based on the selection criteria and data extraction, 57 final studies were selected. These papers are summarized and compared, and the relevant information is extracted to answer the research questions. The synthesis resulted in knowledge of currently proposed methods for web service discovery and recommendation based on clustering and association rule techniques. Furthermore, it identifies algorithms, similarity measures, evaluation metrics, and data sets. Also identifies challenges, research gaps, trends, and future directions. We propose a classification of web service discovery and recommendation methods and map the 57 final selected papers into these classes. This review will help researchers to understand the current state-of-the-art in clustering and association rules techniques for web service discovery and recommendation, and also recognize trends and future directions for improvement. Future studies should broaden the basis of discovery and recommendation by including various types of web service descriptions including plain text that are currently used in web APIs. An opportunity for improvement by utilizing modern techniques based on big data analytics and social network analysis.

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Transferring auxiliary knowledge to enhance heterogeneous web service clustering

Cited by 6 publications

References 19 publications

Tagging augmented neural topic model for semantic sparse Web service discovery

Tagging augmented neural topic model for semantic sparse Web service discovery

Degree of Match-Based Hierarchical Clustering Technique for Efficient Service Discovery

Clustering and Association Rules for Web Service Discovery and Recommendation: A Systematic Literature Review

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