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Knowledge Graph Enhanced Web API Recommendation via Neighbor Information Propagation for Multi-service Application Development

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2022)

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Abstract

In cloud era, Web APIs have been the best carrier for service delivery, capability replication and data output in multi-service application development. Currently, the number of Web APIs on the Internet is huge and growing exponentially. To enable accurate and fast Web API selection for developers, researchers have proposed a variety of Web API recommendation methods. However, existing methods cannot solve the inherent data sparsity problem well. In addition, existing methods use context information indirectly by finding neighbors or discretely through embedding techniques, while rich semantic information in the Web API ecosystem is ignored. To solve the above problems, we firstly crawl and analyze Web API data to construct a Web API knowledge graph, which laid a data foundation for alleviating the data sparsity problem. Then, we propose a knowledge graph-enhanced Web API recommendation model, so as to improve recommendation accuracy by capturing high-order structural information and semantic information. Typically, multivariate representations of user and Web API are made by the neighbor information propagation in Web API knowledge graph. The proposed model supports end-to-end learning for beneficial feature extraction. Finally, experiments results demonstrate the proposed model outperforms baselines significantly, thereby promoting the development of Web API economy.

Supported by National Natural Science Foundation of China, No. 62102348, 62276226. Natural Science Foundation of Hebei Province, China, No. F2022203012, F2021203038. Science and Technology Research Project of Hebei University, No. QN2020183. Innovation Capability Improvement Plan Project of Hebei Province, No. 22567626H.

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Authors and Affiliations

  1. College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

    Zhen Chen, Yujie Li, Yuying Wang, Xiaowei Liu, Yifan Xing, Dianlong You & Limin Shen

  2. National Science Libraries, Chinese Academy of Sciences, Beijing, 100864, China

    Linlin Liu

  3. The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, 066004, China

    Zhen Chen, Dianlong You & Limin Shen

Authors
  1. Zhen Chen
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  2. Yujie Li
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  3. Yuying Wang
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  4. Xiaowei Liu
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  5. Yifan Xing
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  6. Linlin Liu
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  7. Dianlong You
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  8. Limin Shen
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Corresponding author

Correspondence to Zhen Chen .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

  3. Zhejiang University City College, Hangzhou, China

    Wei Wei

  4. London South Bank University, London, UK

    Tasos Dagiuklas

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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Chen, Z. et al. (2022). Knowledge Graph Enhanced Web API Recommendation via Neighbor Information Propagation for Multi-service Application Development. In: Gao, H., Wang, X., Wei, W., Dagiuklas, T. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 460 . Springer, Cham. https://doi.org/10.1007/978-3-031-24383-7_2

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  • DOI: https://doi.org/10.1007/978-3-031-24383-7_2

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  • Online ISBN: 978-3-031-24383-7

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