Knowledge Graphs Representation for Event-Related E-News Articles

• Published in: Machine learning and knowledge extraction Vol. 3; no. 4; pp. 802 - 818
• Main Authors: Lakshika, M.V.P.T., Caldera, H.A.
• Format: Journal Article
• Language: English
• 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
• ISSN: 2504-4990; 2504-4990
• DOI: 10.3390/make3040040

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.