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Knowledge Graphs Representation for Event-Related E-News Articles
Content Provider | MDPI |
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Author | Lakshika, M. V. P. T. Caldera, H. A. |
Copyright Year | 2021 |
Description | 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. |
Ending Page | 818 |
Page Count | 17 |
Starting Page | 802 |
e-ISSN | 25044990 |
DOI | 10.3390/make3040040 |
Journal | Machine Learning and Knowledge Extraction |
Issue Number | 4 |
Volume Number | 3 |
Language | English |
Publisher | MDPI |
Publisher Date | 2021-09-26 |
Access Restriction | Open |
Subject Keyword | Machine Learning and Knowledge Extraction Information and Library Science Medical Informatics Knowledge Graph Knowledge Base Knowledge Representation E-news Articles Spo Triples |
Content Type | Text |
Resource Type | Article |