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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
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| Series: | Machine Learning and Knowledge Extraction |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4990/3/4/40 |
| _version_ | 1797502873525813248 |
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| author | M.V.P.T. Lakshika H.A. Caldera |
| author_facet | M.V.P.T. Lakshika H.A. Caldera |
| author_sort | M.V.P.T. Lakshika |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T03:42:22Z |
| format | Article |
| id | doaj.art-d425d4f561084d48a00480ab5838b612 |
| institution | Directory Open Access Journal |
| issn | 2504-4990 |
| language | English |
| last_indexed | 2024-03-10T03:42:22Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj.art-d425d4f561084d48a00480ab5838b6122023-11-23T09:17:34ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902021-09-013480281810.3390/make3040040Knowledge Graphs Representation for Event-Related E-News ArticlesM.V.P.T. Lakshika0H.A. Caldera1University of Colombo School of Computing (UCSC), University of Colombo, Colombo 00700, Sri LankaUniversity of Colombo School of Computing (UCSC), University of Colombo, Colombo 00700, Sri LankaE-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.https://www.mdpi.com/2504-4990/3/4/40knowledge graphknowledge baseknowledge representatione-news articlesSPO triples |
| spellingShingle | M.V.P.T. Lakshika H.A. Caldera Knowledge Graphs Representation for Event-Related E-News Articles Machine Learning and Knowledge Extraction knowledge graph knowledge base knowledge representation e-news articles SPO triples |
| title | Knowledge Graphs Representation for Event-Related E-News Articles |
| title_full | Knowledge Graphs Representation for Event-Related E-News Articles |
| title_fullStr | Knowledge Graphs Representation for Event-Related E-News Articles |
| title_full_unstemmed | Knowledge Graphs Representation for Event-Related E-News Articles |
| title_short | Knowledge Graphs Representation for Event-Related E-News Articles |
| title_sort | knowledge graphs representation for event related e news articles |
| topic | knowledge graph knowledge base knowledge representation e-news articles SPO triples |
| url | https://www.mdpi.com/2504-4990/3/4/40 |
| work_keys_str_mv | AT mvptlakshika knowledgegraphsrepresentationforeventrelatedenewsarticles AT hacaldera knowledgegraphsrepresentationforeventrelatedenewsarticles |