Abstract

The Liao Dynasty was a minority regime established by the Khitan on the grasslands of northern China. To promote and spread the cultural knowledge of the Liao Dynasty, an intelligent question-and-answer system is constructed based on the knowledge graph in the historical and cultural field of the Liao Dynasty. In the traditional question answering system, the quality of answers was not high due to incomplete data and distinctive vocabulary. To solve this problem, a combination method of Liao Dynasty question-and-answer database and KB is proposed to realize knowledge graph question answering, and a joint model of Siamese LSTM and fusion MatchPyramid is proposed for semantic matching between questions in the question-and-answer database. With the joint model, it is easy to perform semantic matching by fusing sentence-level and word-level interactive features through LSTM and MatchPyramid. Furthermore, the question sentence with the same semantics as the user input question sentence is retrieved in the question-and-answer database, and the answer corresponding to the question sentence is returned as the result. The experimental results show that our proposed method has achieved relatively good performance in the historical domain of the Liao Dynasty and the open-domain knowledge graph, and improved the accuracy of question and answer.

Highlights

  • With the rapid development of Internet Technology, information resources have gradually increased

  • Feature extraction layer: the high-order features extracted from the fusion MatchPyramid model and the context features generated by the Siamese LSTM are spliced

  • To prove the effectiveness of the proposed model in knowledge base question answering (KBQA), the constructed Liao Dynasty historical knowledge graph and the Liao Dynasty question-and-answer dataset are used in the following models to complete the comparative experiment

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Summary

Introduction

With the rapid development of Internet Technology, information resources have gradually increased. Knowledge Graph uses data processing technology to fuse massive amounts of historical unstructured text data of the Liao Dynasty and transform it into semantically rich structured data composed of domain entities, attributes, and their interrelationships. It provides a high-quality data source for intelligent question answering system to improve the accuracy of data query effectively. The method proposed in this paper uses the Siamese LSTM and the fusion MatchPyramid model to extract different question feature combinations and judges whether the semantics of the two questions is the same, and retrieves the answer corresponding to the question from the QAB.

Related Work
Background
Construction of Knowledge Graph in Liao Dynasty
QAB Construction
Model Description
Siamese LSTM‐Fusion MatchPyramid Model
Siamese LSTM
Fusion MatchPyramid
Joint Model
Experiment
Datasets
Liao Dynasty History QAB
Parameter Setting
Method
Deep Text Matching Model Comparison Experiment
Ablation Experiment
Full Text
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