KG quality metrics

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DC Field	Value	Language
dc.contributor.author	Sharma, Pranav
dc.contributor.author	Mutharaju, Vijaya Raghava (Advisor)
dc.contributor.author	Mukherjee, Manuj (Advisor)
dc.date.accessioned	2024-05-08T12:25:21Z
dc.date.available	2024-05-08T12:25:21Z
dc.date.issued	2023-11-27
dc.identifier.uri	http://repository.iiitd.edu.in/xmlui/handle/123456789/1408
dc.description.abstract	In the burgeoning field of knowledge representation, the construction and maintenance of highquality 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.	en_US
dc.language.iso	en_US	en_US
dc.publisher	IIIT-Delhi	en_US
dc.subject	Knowledge graph (KG)	en_US
dc.subject	Quality assessment	en_US
dc.subject	Novel matrices	en_US
dc.subject	Accuracy	en_US
dc.subject	Completeness	en_US
dc.subject	Consistency	en_US
dc.subject	Contextual relevance	en_US
dc.title	KG quality metrics	en_US
dc.type	Other	en_US
Appears in Collections:	Year-2023

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