Please use this identifier to cite or link to this item: http://repository.iiitd.edu.in/xmlui/handle/123456789/2152
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dc.contributor.authorGupta, Aanchal-
dc.contributor.authorKumar, Yogender-
dc.contributor.authorMukherjee, Manuj (Advisor)-
dc.contributor.authorMutharaju, Raghava (Advisor)-
dc.date.accessioned2026-09-15T10:41:22Z-
dc.date.available2026-09-15T10:41:22Z-
dc.date.issued2024-11-26-
dc.identifier.urihttp://repository.iiitd.edu.in/xmlui/handle/123456789/2152-
dc.description.abstractIn 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.en_US
dc.language.isoen_USen_US
dc.publisherIIIT-Delhien_US
dc.subjectKnowledge graph (KG)en_US
dc.subjectQuality assessmenten_US
dc.subjectNovel matricesen_US
dc.subjectAccuracyen_US
dc.subjectContextual relevanceen_US
dc.titleKG quality metricsen_US
dc.typeOtheren_US
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