Journal of Enterprise and Business Intelligence


A Schematic Review of Knowledge Reasoning Approaches Based on the Knowledge Graph



Journal of Enterprise and Business Intelligence

Received On : 21 November 2022

Revised On : 16 January 2023

Accepted On : 30 March 2023

Published On : 05 July 2023

Volume 03, Issue 03

Pages : 179-189


Abstract


In the contemporary world, the Internet technology and its implementation mode are advancing at a swift pace, leading to an exponential growth in the scale of Internet data. This data contains a significant amount of valuable knowledge. The effective organization and articulation of knowledge, as well as the ability to conduct thorough calculations and analyses, have garnered significant attention and developments within a particular environmental context. The utilization of knowledge graphs for knowledge reasoning has emerged as a prominent area of focus within the realm of knowledge graph research. It holds substantial significance in the realm of vertical search, intelligent answering, and various other applications. This article will be centered on fundamental principles of reasoning. The approach of knowledge reasoning oriented towards knowledge graphs is focused on the derivation of novel knowledge or the detection of erroneous knowledge through the utilization of pre-existing knowledge. In contrast to conventional knowledge reasoning approaches, the knowledge reasoning technique employed in knowledge graphs is characterized by greater diversity, owing to the succinct, adaptable, and flexible representation of knowledge.


Keywords


Knowledge Graphs, Knowledge Reasoning, Representation Learning, Visual Representations.


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Cite this article


Ignacio Villegas Vergara and Liza Chung Lee, “A Schematic Review of Knowledge Reasoning Approaches Based on the Knowledge Graph”, Journal of Enterprise and Business Intelligence, vol.3, no.3, pp. 179-189, July 2023. doi: 10.53759/5181/JEBI202303018.


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© 2023 Ignacio Villegas Vergara and Liza Chung Lee. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.