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Answer Acquisition for Knowledge Base Question Answering Systems Based on Dynamic Memory Network
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DOI：10.1109/ACCESS.2019.2949993.png)
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摘要
En 中文
In recent years, with the rapid growth of Artificial Intelligence (AI) and the Internet of Things (IoT), the question answering systems for human-machine interaction based on deep learning have become a research hotspot of the IoT. Different from the structured query method in traditional Knowledge Base Question Answering (KBQA) systems based on templates or rules, representation learning is one of the most promising approaches to solving the problems of data sparsity and semantic gaps. In this paper, an answer acquisition method for KBQA systems based on a dynamic memory network is proposed, in which representation learning is employed to represent the natural language questions that are raised by users and the knowledge base subgraphs of the related entities. These representations are taken as inputs of the dynamic memory network. The correct answers are obtained by utilizing the memory and inferential capabilities. The experimental results demonstrate the effectiveness of the proposed approach.
Keyword:
Semantics
Cognition
Knowledge based systems
Knowledge discovery
Internet of Things
Man-machine systems
Task analysis
Internet of things
human-machine interaction
knowledge base question answering systems
dynamic memory network
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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DBpedia - A large-scale, multilingual knowledge base extracted from WikipediaDBpedia-从维基百科中提取的大规模多语言知识库.png)
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