Knowledge Graphs Representation for Event-Related E-News Articles

Machine Learning and Knowledge Extraction

M.V.P.T. Lakshika¹  H.A. Caldera¹ 

[1] University of Colombo School of Computing (UCSC), University of Colombo, Colombo 00700, Sri Lanka;

关键词: knowledge graph;    knowledge base;    knowledge representation;    e-news articles;    SPO triples;   

DOI  :  10.3390/make3040040

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown