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Time-Aware PolarisX: Auto-Growing Knowledge Graph

Yeon-Sun Ahn, Ok-Ran Jeong*

Department of Software, Gachon University, Gyeonggi-do, 13120, Korea

* Corresponding Author: Ok-Ran Jeong. Email: email

Computers, Materials & Continua 2021, 67(3), 2695-2708. https://doi.org/10.32604/cmc.2021.015636

Abstract

A knowledge graph is a structured graph in which data obtained from multiple sources are standardized to acquire and integrate human knowledge. Research is being actively conducted to cover a wide variety of knowledge, as it can be applied to applications that help humans. However, existing researches are constructing knowledge graphs without the time information that knowledge implies. Knowledge stored without time information becomes outdated over time, and in the future, the possibility of knowledge being false or meaningful changes is excluded. As a result, they can’t reflect information that changes dynamically, and they can’t accept information that has newly emerged. To solve this problem, this paper proposes Time-Aware PolarisX, an automatically extended knowledge graph including time information. Time-Aware PolarisX constructed a BERT model with a relation extractor and an ensemble NER model including a time tag with an entity extractor to extract knowledge consisting of subject, relation, and object from unstructured text. Through two application experiments, it shows that the proposed system overcomes the limitations of existing systems that do not consider time information when applied to an application such as a chatbot. Also, we verify that the accuracy of the extraction model is improved through a comparative experiment with the existing model.

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Cite This Article

Y. Ahn and O. Jeong, "Time-aware polarisx: auto-growing knowledge graph," Computers, Materials & Continua, vol. 67, no.3, pp. 2695–2708, 2021. https://doi.org/10.32604/cmc.2021.015636

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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