Abstract
The link prediction (LP) and triplet classification (TC) are important tasks in the field of knowledge graph mining. However, the traditional link prediction methods of social networks cannot directly apply to knowledge graph data which contains multiple relations. In this paper, we apply the knowledge graph embedding method to solve the specific tasks with Chinese knowledge base Zhishi.me. The proposed method has been successfully used in the evaluation task of CCKS2016. Hopefully, it can achieve excellent performance.
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Acknowledgement
This work has been partially funded by the National Basic Reseach Program of China (2014CB340404) and the IBM SUR (2015) grant.
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Shijia, E., Jia, S., Xiang, Y., Ji, Z. (2016). Knowledge Graph Embedding for Link Prediction and Triplet Classification. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds) Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. CCKS 2016. Communications in Computer and Information Science, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-10-3168-7_23
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DOI: https://doi.org/10.1007/978-981-10-3168-7_23
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