Please use this identifier to cite or link to this item: http://repository.iiitd.edu.in/xmlui/handle/123456789/2152
Title: KG quality metrics
Authors: Gupta, Aanchal
Kumar, Yogender
Mukherjee, Manuj (Advisor)
Mutharaju, Raghava (Advisor)
Keywords: Knowledge graph (KG)
Quality assessment
Novel matrices
Accuracy
Contextual relevance
Issue Date: 26-Nov-2024
Publisher: IIIT-Delhi
Abstract: In the field of knowledge representation, the construction and maintenance of high quality knowledge graphs (KG’s) play a pivotal role in ensuring the accuracy and reliability of information. This research endeavors to establish a comprehensive framework for assessing the quality of knowledge graphs, introducing novel matrices and metrics tailored to capture the intricacies of knowledge representation. Our approach involves the development of quantifiable measures that evaluate aspects such as completeness, consistency, accuracy, and contextual relevance within a knowledge graph.
URI: http://repository.iiitd.edu.in/xmlui/handle/123456789/2152
Appears in Collections:Year-2024

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