Abstract
Entity linking, bridging text and knowledge base, is a fundamental task in the field of information extraction. Most existing approaches highly depend on the structural features and statistics in the target knowledge base. Compared with raw text, they provide more discriminative information and make the task easier. However, in many closed domains, structural features and statistics are rarely available and the target knowledge base may be as simple and sparse as a series of separate entity records only with description. Therefore, few algorithms could work well on the incomplete knowledge base. In this paper, we propose a novel neural approach which only requires minimal text information from the knowledge base. To extract features from text effectively, we employ the co-attention mechanism to emphasize discriminative words and weaken noise. Compared with existing “black box” neural approaches, co-attention mechanism also brings better interpretability to our model. We conduct experiments on the AIDA-CoNLL benchmark and evaluate the performance with accuracy. Results show that our model achieves 82.3% in accuracy and outperforms the baseline by 1.1%.
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Acknowledgements
This work is supported by FDCT 0007/2018/A1, DCT-MoST Joint-project No. (025/2015/AMJ) of SAR Macau; University of Macau Funds Nos: CPG2018-00032-FST & SRG2018-00111-FST; Chinese National Research Fund (NSFC) Key Project No. 61532013; National China 973 Project No. 2015CB352401; Shanghai Scientific Innovation Act of STCSM No.15JC1402400 and 985 Project of Shanghai Jiao Tong University: WF220103001.
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Zhang, S., Lou, J., Zhou, X., Jia, W. (2018). Entity Linking Facing Incomplete Knowledge Base. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11234. Springer, Cham. https://doi.org/10.1007/978-3-030-02925-8_23
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