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Intelligent Semantic Annotation for Mobile Services for IoT Computing from Heterogeneous Data

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Abstract

The rapid development of Internet-of-Things (IoT) computing leads to many problems, among which the management of massive mobile services has attracted much attention. When developers are looking for a service for IoT computing from mobile services, they typically try to discover the services according to annotations. If the mobile services are not assigned by proper annotations, it will be difficult for developers to find the suitable service. For providers, if the services that they provide cannot be used by developers, there will be no revenue. Therefore, it is a critical to assign proper semantic annotations to the mobile services. Existing approaches usually use the call records between services and developers to construct a score matrix, and compute the similarity between services and semantic annotations. However, these approaches do not leverage the natural association between services, providers and users. To make full use of the information inherent in services, we construct a heterogeneous information network (HIN) for service data, and propose a new model named GoT, which fully utilizes the structural and semantic information. GoT contains four components, which are the metapath construction, the intra-metapath fusion, the inter-metapath fusion, and the semantic annotation recommendation. We collected a real-world Web API dataset and performed adequate experiments. The experimental results show that our model produces superior recommendation accuracy and alleviates the cold-start problem.

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Data Availability

The data that support the findings of this study are available on request from the corresponding author.

Notes

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  2. https://www.programmableweb.com/

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Funding

This paper is supported by National Key R&D Program (2021YFF0901002) and Fundamental Research Funds for the Central Universities (JB210311).

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, Shaanxi, China

    Yueshen Xu, Xinyu Zhao, Zhiping Jiang, Zhibo Qiu & Rui Li

  2. The Journal Center, Xidian University, Xi’an, 710071, Shaanxi, China

    Lei Hei

Authors
  1. Yueshen Xu
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  2. Xinyu Zhao
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  3. Zhiping Jiang
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  4. Zhibo Qiu
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  5. Lei Hei
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Contributions

Yueshen Xu proposed the idea of using heterogeneous data to complete the task of semantic annotation for mobile services for IoT computing and designed the framework of the graph neural network model. Yueshen Xu also finished the writing of the Introduction section, the Conclusion and Future Work section and Abstract. Xinyu Zhao constructed the heterogeneous information network from the data in IoT computing and realized the graph neural network model by coding. Zhiping Jiang designed the method of the intra-metapath fusion and inter-metapath fusion. Zhibo Qiu contributed to the analysis of experimental results and finished the writing of the Experiments and Evaluation section. Lei Hei finished the writing of the Proposed Model for Tag Recommendation section. Rui Li proofread the whole paper, revised the manuscript and also gave suggestions for the design of experiments.

Corresponding author

Correspondence to Zhiping Jiang.

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Xu, Y., Zhao, X., Jiang, Z. et al. Intelligent Semantic Annotation for Mobile Services for IoT Computing from Heterogeneous Data. Mobile Netw Appl 28, 348–358 (2023). https://doi.org/10.1007/s11036-023-02091-0

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