Introducing External Knowledge to Answer Questions with Implicit Temporal Constraints over Knowledge Base

Wenqing Wu, Zhenfang Zhu, Qiang Lu, Dianyuan Zhang and Qiangqiang Guo
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Wenqing Wu: School of Information Science and Electrical Engineering, Shandong Jiao tong University, Jinan 250357, China
Zhenfang Zhu: School of Information Science and Electrical Engineering, Shandong Jiao tong University, Jinan 250357, China
Qiang Lu: School of Information Science and Electrical Engineering, Shandong Jiao tong University, Jinan 250357, China
Dianyuan Zhang: School of Information Science and Electrical Engineering, Shandong Jiao tong University, Jinan 250357, China
Qiangqiang Guo: School of Information Science and Electrical Engineering, Shandong Jiao tong University, Jinan 250357, China

Future Internet, 2020, vol. 12, issue 3, 1-13

Abstract: Knowledge base question answering (KBQA) aims to analyze the semantics of natural language questions and return accurate answers from the knowledge base (KB). More and more studies have applied knowledge bases to question answering systems, and when using a KB to answer a natural language question, there are some words that imply the tense (e.g., original and previous) and play a limiting role in questions. However, most existing methods for KBQA cannot model a question with implicit temporal constraints. In this work, we propose a model based on a bidirectional attentive memory network, which obtains the temporal information in the question through attention mechanisms and external knowledge. Specifically, we encode the external knowledge as vectors, and use additive attention between the question and external knowledge to obtain the temporal information, then further enhance the question vector to increase the accuracy. On the WebQuestions benchmark, our method not only performs better with the overall data, but also has excellent performance regarding questions with implicit temporal constraints, which are separate from the overall data. As we use attention mechanisms, our method also offers better interpretability.

Keywords: knowledge base question answering; attention mechanism; external knowledge (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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