[1] Li Huiying, Zhao Man, Yu Wenqi,. A multi-attention RNN-based relation linking approachfor question answering over knowledge base [J]. Journal of Southeast University (English Edition), 2020, 36 (4): 385-392. [doi:10.3969/j.issn.1003-7985.2020.04.003]

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A multi-attention RNN-based relation linking approachfor question answering over knowledge base()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

36

Issue:

2020 4

Page:

385-392

Research Field:

Computer Science and Engineering

Publishing date:

2020-12-20

Info

Title:

A multi-attention RNN-based relation linking approachfor question answering over knowledge base

Author(s):

Li Huiying;  Zhao Man;  Yu Wenqi

School of Computer Science and Engineering, Southeast University, Nanjing 211189, China

Keywords:

question answering over knowledge base(KBQA);  entity linking;  relation linking;  multi-attention bidirectional long short-term memory(Bi-LSTM);  large-scale complex question answering dataset(LC-QuAD)

PACS:

TP311

DOI:

10.3969/j.issn.1003-7985.2020.04.003

Abstract:

Aiming at the relation linking task for question answering over knowledge base, especially the multi relation linking task for complex questions, a relation linking approach based on the multi-attention recurrent neural network(RNN)model is proposed, which works for both simple and complex questions. First, the vector representations of questions are learned by the bidirectional long short-term memory(Bi-LSTM)model at the word and character levels, and named entities in questions are labeled by the conditional random field(CRF)model. Candidate entities are generated based on a dictionary, the disambiguation of candidate entities is realized based on predefined rules, and named entities mentioned in questions are linked to entities in knowledge base. Next, questions are classified into simple or complex questions by the machine learning method. Starting from the identified entities, for simple questions, one-hop relations are collected in the knowledge base as candidate relations; for complex questions, two-hop relations are collected as candidates. Finally, the multi-attention Bi-LSTM model is used to encode questions and candidate relations, compare their similarity, and return the candidate relation with the highest similarity as the result of relation linking. It is worth noting that the Bi-LSTM model with one attentions is adopted for simple questions, and the Bi-LSTM model with two attentions is adopted for complex questions. The experimental results show that, based on the effective entity linking method, the Bi-LSTM model with the attention mechanism improves the relation linking effectiveness of both simple and complex questions, which outperforms the existing relation linking methods based on graph algorithm or linguistics understanding.

References:

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Memo

Memo:

Biography: Li Huiying(1977—), female, doctor, associate professor, huiyingli@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.61502095).
Citation: Li Huiying, Zhao Man, Yu Wenqi. A multi-attention RNN-based relation linking approach for question answering over knowledge base[J].Journal of Southeast University(English Edition), 2020, 36(4):385-392.DOI:10.3969/j.issn.1003-7985.2020.04.003.

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