Abstract
Ontology is a formal representation of a domain using a set of concepts of the domain and how these concepts are related. Class is one of the components of an ontology for describing the concepts of the system. It is used to create, update, search or delete instances which are digital representations of physical things. With the development of the IoT (Internet of Things) technology, developers create and manage the corresponding IoT instances on IoT platform. With the user’s query of a few key words, how to find the ontology classes accurately is a hard problem. IoT Ontology classes recommender system can help developers find the ontology classes that they want to use efficiently. In a general recommender system, user’s historical usage records, background features and input keywords are used for making personalized recommendations. However, the newly established IoT platforms do not have a large number of user usage records to optimize recommendation results. And recommendation based on input words’ semantics lacks relevance between the IoT ontology classes. This paper proposed a method for recommendation of IoT ontology classes based on knowledge graph building and semantics to introduce more auxiliary information and relationships for the recommendation. And the result shows that our proposed recommendation method can recommend more related IoT ontology classes and have better performance in results’ accuracy.
National Key R&D Program of China (No. 2019YFB2102200), National Natural Science Foundation of China (No. 61977003) and Orange R&D Beijing Company Limited.
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References
Guarino, N., Carrara, M., Giaretta, P.: An ontology of meta-level categories. In: Principles of Knowledge Representation and Reasoning, pp. 270–280. Elsevier (1994)
Nambi, S.A.U., Sarkar, C., Prasad, R.V., Rahim, A.: A unified semantic knowledge base for iot. In: 2014 IEEE World Forum on Internet of Things (WF-IoT), pp. 575–580. IEEE (2014)
Gai, K., Qiu, M., Zhao, H., Sun, X.: Resource management in sustainable cyber-physical systems using heterogeneous cloud computing. IEEE Trans. Sustainable Comput. 3(2), 60–72 (2017)
Dai, W., Qiu, L., Wu, A., Qiu, M.: Cloud infrastructure resource allocation for big data applications. IEEE Trans. Big Data 4(3), 313–324 (2016)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175–186 (1994)
Wartena, C., Slakhorst, W., Wibbels, M.: Selecting keywords for content based recommendation. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1533–1536 (2010)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space, arXiv preprint arXiv:1301.3781 (2013)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Khattar, D., Kumar, V., Varma, V., Gupta, M.: Weave&rec: a word embedding based 3-d convolutional network for news recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1855–1858 (2018)
Naumov, M., et al.: Deep learning recommendation model for personalization and recommendation systems, arXiv preprint arXiv:1906.00091 (2019)
Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)
Cao, Y., Wang, X., He, X., Hu, Z., Chua, T.-S.: Unifying knowledge graph learning and recommendation: towards a better understanding of user preferences. In: The World Wide Web Conference, pp. 151–161 (2019)
V. W. Anelli, T. Di Noia, E. Di Sciascio, A. Ragone, and J. Trotta, "How to make latent factors interpretable by feeding factorization machines with knowledge graphs. In: International Semantic Web Conference, pp. 38–56. Springer (2019). doi: 10.1007/978-3-030-30793-6\_3
Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web, pp. 111–112 (2015)
Jenatton, R., Le Roux, N., Bordes, A., Obozinski, G.: A latent factor model for highly multi-relational data. In: Advances in Neural Information Processing Systems 25 (NIPS 2012), pp. 3176–3184 (2012)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)
Okura, S., Tagami, Y., Ono, S., Tajima, A.: Embedding-based news recommendation for millions of users. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1933–1942 (2017)
Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: Pathsim: meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endowment 4(11), 992–1003 (2011)
Sha, X., Sun, Z., Zhang, J.: Attentive knowledge graph embedding for personalized recommendation, arXiv preprint arXiv:1910.08288 (2019)
Ma, W., et al.: Jointly learning explainable rules for recommendation with knowledge graph. In: The World Wide Web Conference, pp. 1210–1221 (2019)
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Wang, X., Yin, C., Fan, X., Wu, S., Wang, L. (2021). An IoT Ontology Class Recommendation Method Based on Knowledge Graph. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12815. Springer, Cham. https://doi.org/10.1007/978-3-030-82136-4_54
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