Abstract
Knowledge base (KB) completion aims to infer missing facts based on existing ones in a KB. Many approaches firstly suppose that the constituents themselves (e.g., head, tail entity and relation) of a fact meet some formulas and then minimize the loss of formula to obtain the feature vectors of entities and relations. Due to the sparsity of KB, some methods also take into consideration the indirect relations between entities. However, indirect relations further widen the differences of training times of high-degree entities (entities linking by many relations) and low-degree entities. This results in underfitting of low-degree entities. In this paper, we propose the path-based TransE with aggregation (PTransE-ag) to fine-tune the feature vector of an entity by comparing it to its related entities that linked by the same relations. In this way, low-degree entities can draw useful information from high-degree entities to directly adjust their representations. Conversely, the overfitting of high-degree entities can be relieved. Extensive experiments carried on the real world dataset show our method can define entities more accurately, and inferring is more effectively than in previous methods.
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Acknowledgements
This work is supported by National Key Research and Development Plan of China under Grant No. 2017YFD0400101, and National Natural Science Foundation of China under Grant No. 61502294, and Natural Science Foundation of Shanghai under Grant No. 16ZR1411200.
The url of the source code is https://github.com/IdelCoder/PTransE-ag.
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Zhang, T., Liu, F., Shen, Y., Gao, H., Duan, J. (2019). Learning from High-Degree Entities for Knowledge Graph Modeling. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_35
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