Semantic Sparse Service Discovery Using Word Embedding and Gaussian LDA
Nowadays, a growing number of web services are offered in API marketplaces browsed by service developers or third-party registries. Under this situation, API marketplaces' users greatly rely on a search engine to find suitable web services. However, due to the fact that functional attributes of...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8754673/ |
| _version_ | 1818936347384086528 |
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| author | Gang Tian Shengtao Zhao Jian Wang Ziqi Zhao Junju Liu Lantian Guo |
| author_facet | Gang Tian Shengtao Zhao Jian Wang Ziqi Zhao Junju Liu Lantian Guo |
| author_sort | Gang Tian |
| collection | DOAJ |
| description | Nowadays, a growing number of web services are offered in API marketplaces browsed by service developers or third-party registries. Under this situation, API marketplaces' users greatly rely on a search engine to find suitable web services. However, due to the fact that functional attributes of web services are usually described in short texts, the search engine-based discovery approach suffers from the semantic sparsity problem, which hinders the effect of service discovery. To address this issue, we propose a novel web service discovery approach using word embedding and Gaussian latent Dirichlet allocation (Gaussian LDA). Unlike most existing service discovery approaches, our approach first uses context information generated by word embedding to enrich the semantics of service descriptions and users' queries. Then, the enriched service description is loaded into the Gaussian LDA model to acquire service description representation. Finally, the services are ranked by considering the relevance between the extended user's query and service description representation. The experiments conducted on a real-world web service dataset and the results demonstrate that the proposed approach achieves superior effectiveness on web service discovery. |
| first_indexed | 2024-12-20T05:34:37Z |
| format | Article |
| id | doaj.art-52f4c7baccb24828911e5306dc78f7ce |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T05:34:37Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-52f4c7baccb24828911e5306dc78f7ce2022-12-21T19:51:39ZengIEEEIEEE Access2169-35362019-01-017882318824210.1109/ACCESS.2019.29265598754673Semantic Sparse Service Discovery Using Word Embedding and Gaussian LDAGang Tian0https://orcid.org/0000-0003-0161-3020Shengtao Zhao1Jian Wang2Ziqi Zhao3Junju Liu4Lantian Guo5https://orcid.org/0000-0002-1792-4926School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaSchool of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaState Key Laboratory of Software Engineering, Wuhan University, Wuhan, ChinaUniversity of New South Wales, Sydney, NSW, AustraliaZhixing College, Hubei University, Wuhan, ChinaSchool of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, ChinaNowadays, a growing number of web services are offered in API marketplaces browsed by service developers or third-party registries. Under this situation, API marketplaces' users greatly rely on a search engine to find suitable web services. However, due to the fact that functional attributes of web services are usually described in short texts, the search engine-based discovery approach suffers from the semantic sparsity problem, which hinders the effect of service discovery. To address this issue, we propose a novel web service discovery approach using word embedding and Gaussian latent Dirichlet allocation (Gaussian LDA). Unlike most existing service discovery approaches, our approach first uses context information generated by word embedding to enrich the semantics of service descriptions and users' queries. Then, the enriched service description is loaded into the Gaussian LDA model to acquire service description representation. Finally, the services are ranked by considering the relevance between the extended user's query and service description representation. The experiments conducted on a real-world web service dataset and the results demonstrate that the proposed approach achieves superior effectiveness on web service discovery.https://ieeexplore.ieee.org/document/8754673/Semantic sparsityservice discoveryword embeddingGaussian LDAservice description representation |
| spellingShingle | Gang Tian Shengtao Zhao Jian Wang Ziqi Zhao Junju Liu Lantian Guo Semantic Sparse Service Discovery Using Word Embedding and Gaussian LDA IEEE Access Semantic sparsity service discovery word embedding Gaussian LDA service description representation |
| title | Semantic Sparse Service Discovery Using Word Embedding and Gaussian LDA |
| title_full | Semantic Sparse Service Discovery Using Word Embedding and Gaussian LDA |
| title_fullStr | Semantic Sparse Service Discovery Using Word Embedding and Gaussian LDA |
| title_full_unstemmed | Semantic Sparse Service Discovery Using Word Embedding and Gaussian LDA |
| title_short | Semantic Sparse Service Discovery Using Word Embedding and Gaussian LDA |
| title_sort | semantic sparse service discovery using word embedding and gaussian lda |
| topic | Semantic sparsity service discovery word embedding Gaussian LDA service description representation |
| url | https://ieeexplore.ieee.org/document/8754673/ |
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