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	有向知識グラフ分析	Betweenness Centrality
分野	ネットワーク分析	ネットワーク分析
系統	Machine learning	Machine learning
提唱年	2000s–2010s	1977
提唱者	Hogan, A. et al. (formalized); roots in Berners-Lee, T. et al. (Semantic Web)	Freeman, L. C.
種類	Graph-based knowledge representation and inference	Centrality measure
原典	Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G. D., Gutierrez, C., ... & Polleres, A. (2021). Knowledge graphs. ACM Computing Surveys, 54(4), 1–37.	Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.
別名	directed KG analysis, knowledge graph mining, directed semantic graph analysis, KG reasoning	Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness
関連	6	6
概要	Directed Knowledge Graph Analysis represents factual knowledge as a directed labeled multigraph of entities (nodes) and typed relations (directed edges), enabling structured reasoning, inference, and discovery over large heterogeneous datasets. The direction of edges encodes asymmetric relationships such as 'authored-by', 'causes', or 'is-a', making the graph semantically richer than undirected alternatives.	Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes.