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Knowledge Graphs Representation for Event-Related E-News Articles

University of Colombo School of Computing (UCSC), University of Colombo, Colombo 00700, Sri Lanka
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Academic Editor: Andreas Holzinger
Mach. Learn. Knowl. Extr. 2021, 3(4), 802-818; https://doi.org/10.3390/make3040040
Received: 15 August 2021 / Revised: 20 September 2021 / Accepted: 20 September 2021 / Published: 26 September 2021
E-newspaper readers are overloaded with massive texts on e-news articles, and they usually mislead the reader who reads and understands information. Thus, there is an urgent need for a technology that can automatically represent the gist of these e-news articles more quickly. Currently, popular machine learning approaches have greatly improved presentation accuracy compared to traditional methods, but they cannot be accommodated with the contextual information to acquire higher-level abstraction. Recent research efforts in knowledge representation using graph approaches are neither user-driven nor flexible to deviations in the data. Thus, there is a striking concentration on constructing knowledge graphs by combining the background information related to the subjects in text documents. We propose an enhanced representation of a scalable knowledge graph by automatically extracting the information from the corpus of e-news articles and determine whether a knowledge graph can be used as an efficient application in analyzing and generating knowledge representation from the extracted e-news corpus. This knowledge graph consists of a knowledge base built using triples that automatically produce knowledge representation from e-news articles. Inclusively, it has been observed that the proposed knowledge graph generates a comprehensive and precise knowledge representation for the corpus of e-news articles. View Full-Text
Keywords: knowledge graph; knowledge base; knowledge representation; e-news articles; SPO triples knowledge graph; knowledge base; knowledge representation; e-news articles; SPO triples
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MDPI and ACS Style

Lakshika, M.V.P.T.; Caldera, H.A. Knowledge Graphs Representation for Event-Related E-News Articles. Mach. Learn. Knowl. Extr. 2021, 3, 802-818. https://doi.org/10.3390/make3040040

AMA Style

Lakshika MVPT, Caldera HA. Knowledge Graphs Representation for Event-Related E-News Articles. Machine Learning and Knowledge Extraction. 2021; 3(4):802-818. https://doi.org/10.3390/make3040040

Chicago/Turabian Style

Lakshika, M.V.P.T., and H.A. Caldera 2021. "Knowledge Graphs Representation for Event-Related E-News Articles" Machine Learning and Knowledge Extraction 3, no. 4: 802-818. https://doi.org/10.3390/make3040040

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