Big data fusion with knowledge graph: a comprehensive overview

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Intelligence Pub Date : 2025-04-21 DOI:10.1007/s10489-025-06549-4

Jia Liu, Ruotian Lan, Yajun Du, Xipeng Yuan, Huan Xu, Tianrui Li, Wei Huang, Pengfei Zhang

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引用次数: 0

Abstract

Along with the wide application of intelligent systems in various fields, the combination of data fusion and knowledge graph has become the key to enhance the system’s problem solving capability. However, existing data fusion methods still face challenges when dealing with multi-source heterogeneous data, especially in how to effectively combine knowledge graph. Therefore, this paper systematically reviews existing data fusion methods based on knowledge graph and classifies them into three categories: fusion of raw data, fusion of raw data with knowledge graph, and fusion of knowledge graphs. Each category of methods is described and analyzed in detail by combining a general framework with specific examples. In addition, this paper also discusses the future research direction of data fusion based on knowledge graph, and analyzes the challenges and opportunities it faces. This paper provides a theoretical framework and practical guidance for the problem of multi-source heterogeneous data fusion, and provides methodological support for the development of intelligent systems.

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大数据与知识图谱融合：全面概述

随着智能系统在各个领域的广泛应用，数据融合与知识图谱的结合已成为提高系统问题解决能力的关键。然而，现有的数据融合方法在处理多源异构数据时仍然面临挑战，特别是如何有效地结合知识图。因此，本文系统回顾了现有的基于知识图的数据融合方法，将其分为三类：原始数据融合、原始数据与知识图融合和知识图融合。通过结合一般框架和具体实例，对每一类方法进行了详细的描述和分析。此外，本文还讨论了基于知识图谱的数据融合的未来研究方向，并分析了其面临的挑战和机遇。本文为多源异构数据融合问题提供了理论框架和实践指导，为智能系统的发展提供了方法支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Intelligence 工程技术-计算机：人工智能

CiteScore

6.60

自引率

20.80%

发文量

1361

审稿时长

5.9 months

期刊介绍： With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.