Multi-dimensional Information Integration Based Entity Linking for Knowledge Base Question Answering
ZENG Yutao1,2, LIN Xiexiong1,2, JIN Xiaolong1,2, XI Pengbi1,2, WANG Yuanzhuo1,2
1.CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100090
2.School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049
The entity linking task of knowledge base question answering(KBQA) is to accurately link the content of questions to the entities in the knowledge base. Recall rate and accuracy of linked entities cannot be balanced by most of the current methods, and only the text information is applied to distinguish and filter the entities. Therefore, a multi-dimensional information integration based entity linking for KBQA(MDIIEL) combining multi-dimensional features based on merging substeps is proposed in this paper. By representing learning methods, information such as text symbols, entities and question types, and semantic structure expressions of entities in the knowledge base are integrated and introduced into the entity linking task. The differentiation of similar entities is strengthened, and candidate sets are reduced while the accuracy is improved. The experiment proves that the MDIIEL model makes a holistic improvement on the entity linking task compared with the current methods, and it achieves the best current linking results on most indicators.
曾宇涛,林谢雄,靳小龙,席鹏弼,王元卓. 基于多维信息融合的知识库问答实体链接[J]. 模式识别与人工智能, 2019, 32(7): 642-651.
ZENG Yutao, LIN Xiexiong, JIN Xiaolong, XI Pengbi, WANG Yuanzhuo. Multi-dimensional Information Integration Based Entity Linking for Knowledge Base Question Answering. , 2019, 32(7): 642-651.
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2019, Vol. 32 
