Computing Entity Semantic Similarity by Features Ranking
This work addresses the problem of entity similarity for applications using Linked Data, but it appears incremental as it builds on existing feature-based methods.
The authors tackled the problem of estimating semantic similarity between entities by using ranked lists of features from Linked Data, and their experiments showed that this approach achieved better accuracy than state-of-the-art measures.
This article presents a novel approach to estimate semantic entity similarity using entity features available as Linked Data. The key idea is to exploit ranked lists of features, extracted from Linked Data sources, as a representation of the entities to be compared. The similarity between two entities is then estimated by comparing their ranked lists of features. The article describes experiments with museum data from DBpedia, with datasets from a LOD catalog, and with computer science conferences from the DBLP repository. The experiments demonstrate that entity similarity, computed using ranked lists of features, achieves better accuracy than state-of-the-art measures.