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

School of Information Science and Electrical Engineering, Shandong Jiao tong University, Jinan 250357, China
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Future Internet 2020, 12(3), 45; https://doi.org/10.3390/fi12030045
Received: 27 January 2020 / Revised: 3 March 2020 / Accepted: 4 March 2020 / Published: 5 March 2020
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. View Full-Text
Keywords: knowledge base question answering; attention mechanism; external knowledge knowledge base question answering; attention mechanism; external knowledge
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Wu, W.; Zhu, Z.; Lu, Q.; Zhang, D.; Guo, Q. Introducing External Knowledge to Answer Questions with Implicit Temporal Constraints over Knowledge Base. Future Internet 2020, 12, 45.

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