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A subgraph-representation-based method for answering complex questions over knowledge bases.

Zhifeng Hao1, Biao Wu2, Wen Wen2

  • 1School of Computer Science, Guangdong University of Technology, China; School of Mathematics and Big Data, Foshan University, China.

Neural Networks : the Official Journal of the International Neural Network Society
|August 4, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning method for knowledge-based question answering. The approach effectively answers complex questions involving multiple entities and relations, outperforming existing methods.

Keywords:
Distributional representationKnowledge baseMemory networksQuestion answeringRecurrent neural network

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Area of Science:

  • Natural Language Processing
  • Information Retrieval
  • Artificial Intelligence

Background:

  • Knowledge-based question answering (KBQA) is a significant research area.
  • Existing KBQA methods struggle with complex questions requiring multi-entity and multi-relation fact extraction.

Purpose of the Study:

  • To develop a novel approach for answering complex questions in knowledge-based question answering.
  • To improve the accuracy and capability of systems in handling intricate queries.

Main Methods:

  • A unified deep-learning framework utilizing directed-acyclic-graph-structured long short-term memory (DAG-LSTM) and memory networks.
  • Encoding questions to match directed acyclic subgraphs (DASGs) within the knowledge base.
  • Learning distributional representations for questions and candidate answers.

Main Results:

  • The proposed approach demonstrates superior performance compared to existing methods on the SPADES dataset.
  • Significant improvements were observed specifically for complex questions involving multiple entities.
  • The method effectively handles questions requiring information from multiple relations.

Conclusions:

  • The novel deep-learning framework offers a robust solution for complex knowledge-based question answering.
  • The use of DAG-LSTMs and memory networks enhances the ability to process intricate queries.
  • This research advances the field by providing a more capable system for complex question answering.