Improving Question Answering over Knowledge Graphs with a Chunked Learning Network

Author:

Zuo Zicheng1ORCID,Zhu Zhenfang1ORCID,Wu Wenqing2,Wang Wenling3,Qi Jiangtao1,Zhong Linghui1

Affiliation:

1. School of Information Science and Electrical Engineering, Shandong Jiao Tong University, Jinan 250104, China

2. School of Economic and Management, Nanjing University of Science and Technology, Nanjing 210094, China

3. Chinese Lexicography Research Center, Lu Dong University, Yantai 264025, China

Abstract

The objective of knowledge graph question answering is to assist users in answering questions by utilizing the information stored within the graph. Users are not required to comprehend the underlying data structure. This is a difficult task because, on the one hand, correctly understanding the semantics of a problem is difficult for machines. On the other hand, the growing knowledge graph will inevitably lead to information retrieval errors. Specifically, the question-answering task has three difficulties: word abbreviation, object complement, and entity ambiguity. An object complement means that different entities share the same predicate, and entity ambiguity means that words have different meanings in different contexts. To solve these problems, we propose a novel method named the Chunked Learning Network. It uses different models according to different scenarios to obtain a vector representation of the topic entity and relation in the question. The answer entity representation that yields the closest fact triplet, according to a joint distance metric, is returned as the answer. For sentences with an object complement, we use dependency parsing to construct dependency relationships between words to obtain more accurate vector representations. Experiments demonstrate the effectiveness of our method.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Effects of a knowledge graph-based learning approach on student performance and experience;International Journal of Mobile Learning and Organisation;2024

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