Abstract
Recent years have witnessed the boom of heterogeneous information network (HIN), which contains different types of nodes and relations. Many data mining tasks have been explored in this kind of network. Among them, link prediction is an important task to predict the potential links among nodes, which are required in many applications. The contemporary link prediction usually are based on simple HIN whose schema are bipartite or star-schema. In these HINs, the meta paths are predefined or can be enumerated. However, in many real networked data, it is hard to describe their network structure with simple schema. For example, the knowledge base with RDF format include tens of thousands types of objects and links. On this kind of schema-rich HIN, it is impossible to enumerate meta paths. In this paper, we study the link prediction in schema-rich HIN and propose a novel Link Prediction with automatic meta Paths method (LiPaP). The LiPaP designs an algorithm called Automatic Meta Path Generation (AMPG) to automatically extract meta paths from schema-rich HIN and a supervised method with likelihood function to learn weights of the extracted meta paths. Experiments on real knowledge database, Yago, validate that LiPaP is an effective, steady and efficient method.
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References
Rdf current status. http://www.w3.org/standards/techs/rdf#w3c_all
Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: Dbpedia-a crystallization point for the web of data. Web Semant.: Sci. Serv. Agents World Wide Web 7(3), 154–165 (2009)
Cao, B., Kong, X., Yu, P.S.: Collective prediction of multiple types of links in heterogeneous information networks. In: ICDM, pp. 50–59 (2014)
Deng, H., Lyu, M.R., King, I.: A generalized co-hits algorithm and its application to bipartite graphs. In: KDD, pp. 239–248 (2009)
Jaiwei, H.: Mining heterogeneous information networks: the next frontier. In: SIGKDD, pp. 2–3 (2012)
Jamali, M., Lakshmanan, L.: HeteroMF: recommendation in heterogeneous information networks using context dependent factor models. In: WWW, pp. 643–654 (2013)
Kong, X., Yu, P.S., Ding, Y., Wild, D.J.: Meta path-based collective classification in heterogeneous information networks. In: CIKM, pp. 1567–1571 (2012)
Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn. 81(1), 53–67 (2010)
Shi, C., Kong, X., Yu, P.S., Xie, S., Wu, B.: Relevance search in heterogeneous networks. In: EDBT, pp. 180–191 (2012)
Singhal, A.: Introducing the knowledge graph: things, not strings. Official Google Blog (2012)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: WWW, pp. 697–706 (2007)
Sun, Y., Barber, R., Gupta, M., Aggarwal, C.C., Han, J.: Co-author relationship prediction in heterogeneous bibliographic networks. In: ASONAM, pp. 121–128 (2011)
Sun, Y., Han, J., Aggarwal, C.C., Chawla, N.V.: When will it happen?: relationship prediction in heterogeneous information networks. In: WSDM, pp. 663–672 (2012)
Sun, Y., Norick, B., Han, J., Yan, X., Yu, P.S., Yu, X.: Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. In: KDD, pp. 1348–1356 (2012)
Yu, X., Gu, Q., Zhou, M., Han, J.: Citation prediction in heterogeneous bibliographic networks. In: SDM, pp. 1119–1130 (2012)
Zha, H., He, X., Ding, C.H.Q., Gu, M., Simon, H.D.: Bipartite graph partitioning and data clustering (2001). CoRR cs.IR/0108018
Acknowledgment
This work is supported in part by National Key Basic Research and Department (973) Program of China (No. 2013CB329606), and the National Natural Science Foundation of China (No. 71231002, 61375058,11571161), and the CCF-Tencent Open Fund, the Co-construction Project of Beijing Municipal Commission of Education, and Shenzhen Sci.-Tech Fund No. JCYJ20140509143748226.
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Cao, X., Zheng, Y., Shi, C., Li, J., Wu, B. (2016). Link Prediction in Schema-Rich Heterogeneous Information Network. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9651. Springer, Cham. https://doi.org/10.1007/978-3-319-31753-3_36
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DOI: https://doi.org/10.1007/978-3-319-31753-3_36
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