A Knowledge Representation Model using Concept-Relation Graph
Praveena Rachel Kamala S1, Justus S2
1Praveena Rachel Kamala S, Research Scholar, Department of Computing Science and Engineering, VIT University, Chennai (Tamil Nadu), India.
2Justus S, Associate Professor, Department of Computing Science and Engineering, VIT University, Chennai (Tamil Nadu), India.
Manuscript received on 14 December 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 31 December 2019 | PP: 170-175 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10341291S319/19©BEIESP | DOI: 10.35940/ijeat.A1034.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Huge volume of relevant and irrelevant information is available from different sources for gaining knowledge about a system. If the required data is in a structured form, then the fact can be easily understood otherwise the process becomes laborious and results are vague after intense analysis. In this paper, we are proposing a framework for fetching knowledge from unstructured source of data. The algorithms proposed identifies the concepts and separates the concepts and relation words, enables to add new concepts and also modify the old concept word with new concept and locate the concept. This Concept-Relation Model enables the system to work according to the users’ connivance and deliveries accurate knowledge. This model does not manipulate or interpret the information provided but only represent and share the desired knowledge.
Keywords: Knowledge Representation, Logic, Visualization.
Scope of the Article: Graph Algorithms and Graph Drawing