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GARUM: A Semantic Similarity Measure Based on Machine Learning and Entity Characteristics Description: This paper presents GARUM, a semantic similarity measure that resorts to machine learning methods for learning the most suitable characteristics to consider during the computation of the relatedness of two entities. Knowledge graphs are used to represent the entities, and semantics encoded in the knowledge graph is exploited during training and evaluation of GARUM.