Knowledge Graph Question Answering Based on Multi-granularity Feature Representation

doi:10.3969/j.issn.1006-2475.2018.09.002

Abstract

Abstract: Recently, knowledge graph question answering has gradually become the focus of academic and industrial circles. However, traditional methods often have problems of inefficiency and insufficient use of data information. In order to solve the problems above, this paper divides the Chinese knowledge graph question answering into two sub-tasks: entity extraction and property selection. The Bi-LSTM-CRF model is used to identify entities, and a multi-granularity feature representation model is proposed to carry out property selection. The model utilizes character-level and word-level to represent questions and properties and encode them through the encoder. At the same time, it also introduces the one-hot information for the properties. Through the combination of multi-granularity text representations and the similarity calculation of questions and properties, the system finally achieves a 73.96% F1 value on the NLPCC-ICCPOL 2016 KBQA data set, which finishes the knowledge graph question and answer task successfully.

Key words: knowledge graph, question answering system, entity extraction, property selection

CLC Number:

TP391

SHEN Cun1,2,3, HUANG Ting-lei2,3, LIANG Xiao2,3. Knowledge Graph Question Answering Based on Multi-granularity Feature Representation[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.09.002.

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