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

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 28071 - 28082

Опубликована: Янв. 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.

Язык: Английский

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

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 28071 - 28082

Опубликована: Янв. 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.

Язык: Английский

Процитировано

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