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Similarity in Semantic Graphs: Combining Structural, Literal, and Ontology-based Measures
2015
Semantic Technologies for Intelligence, Defense, and Security
Semantic graphs provide a valuable way to represent data while preserving real world meaning. As these graphs become more popular for storing large quantities of data, it is important to have methods of determining similarity between nodes in the graph. This paper extends previous structural similarity algorithms by taking advantage of meaning contained in a graph's literals and the graph's ontology and allowing users to control how much each type of similarity effects overall scores.
dblp:conf/stids/VanderlynAP15
fatcat:jwkmcijvszcwzgtt3p4z35tr7q