A Network Analysis-Driven Framework for Factual Explainability of Knowledge Graphs DOI Creative Commons
Siraj Munir, Rauf Ahmed Shams Malick, Stefano Ferretti

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 28071 - 28082

Published: Jan. 1, 2024

Knowledge Graphs are widely used to represent knowledge structures in complex domains. In most real-world scenarios, these dynamic. As a result, measures must be developed assess the robustness and usability of temporal settings. Additionally, explainability inherent constituents is crucial for desired attention Graphs, particularly this paper, we framework understand factual Graphs. The method further verified by using meso-level attributes graph. network analysis along with community co-evaluated through homophilic heterophilic properties within graph validate interpretations. reveals that symbolic representation could as reasonable metric extracting link-based communities.

Language: Английский

A Network Analysis-Driven Framework for Factual Explainability of Knowledge Graphs DOI Creative Commons
Siraj Munir, Rauf Ahmed Shams Malick, Stefano Ferretti

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 28071 - 28082

Published: Jan. 1, 2024

Knowledge Graphs are widely used to represent knowledge structures in complex domains. In most real-world scenarios, these dynamic. As a result, measures must be developed assess the robustness and usability of temporal settings. Additionally, explainability inherent constituents is crucial for desired attention Graphs, particularly this paper, we framework understand factual Graphs. The method further verified by using meso-level attributes graph. network analysis along with community co-evaluated through homophilic heterophilic properties within graph validate interpretations. reveals that symbolic representation could as reasonable metric extracting link-based communities.

Language: Английский

Citations

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