Knowledge Graphs Representation for Event-Related E-News Articles

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 mach...

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Published inMachine learning and knowledge extraction Vol. 3; no. 4; pp. 802 - 818
Main Authors Lakshika, M.V.P.T., Caldera, H.A.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2021
Subjects
Automation
e-news articles
Electronic newspapers
Graphical representations
Graphs
knowledge base
Knowledge bases (artificial intelligence)
knowledge graph
Knowledge representation
Machine learning
Multilingualism
Ontology
Search engines
Semantics
SPO triples
Subject specialists
Web sites
Online AccessGet full text
ISSN2504-4990
2504-4990
DOI10.3390/make3040040

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Abstract 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.
AbstractList 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.
Author Caldera, H.A.
Lakshika, M.V.P.T.
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SubjectTerms Automation
e-news articles
Electronic newspapers
Graphical representations
Graphs
knowledge base
Knowledge bases (artificial intelligence)
knowledge graph
Knowledge representation
Machine learning
Multilingualism
Ontology
Search engines
Semantics
SPO triples
Subject specialists
Web sites
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