TY - GEN

T1 - GARUM

T2 - 29th International Conference on Database and Expert Systems Applications, DEXA 2018

AU - Traverso-Ribón, Ignacio

AU - Vidal, Maria Esther

PY - 2018/8/9

Y1 - 2018/8/9

N2 - Knowledge graphs encode semantics that describes entities in terms of several characteristics, e.g., attributes, neighbors, class hierarchies, or association degrees. Several data-driven tasks, e.g., ranking, clustering, or link discovery, require for determining the relatedness between knowledge graph entities. However, state-of-the-art similarity measures may not consider all the characteristics of an entity to determine entity relatedness. We address the problem of similarity assessment between knowledge graph entities and devise GARUM, a semantic similarity measure for knowledge graphs. GARUM relies on similarities of entity characteristics and computes similarity values considering simultaneously several entity characteristics. This combination can be manually or automatically defined with the help of a machine learning approach. We empirically evaluate the accuracy of GARUM on knowledge graphs from different domains, e.g., networks of proteins and media news. In the experimental study, GARUM exhibits higher correlation with gold standards than studied existing approaches. Thus, these results suggest that similarity measures should not consider entity characteristics in isolation; contrary, combinations of these characteristics are required to precisely determine relatedness among entities in a knowledge graph. Further, the combination functions found by a machine learning approach outperform the results obtained by the manually defined aggregation functions.

AB - Knowledge graphs encode semantics that describes entities in terms of several characteristics, e.g., attributes, neighbors, class hierarchies, or association degrees. Several data-driven tasks, e.g., ranking, clustering, or link discovery, require for determining the relatedness between knowledge graph entities. However, state-of-the-art similarity measures may not consider all the characteristics of an entity to determine entity relatedness. We address the problem of similarity assessment between knowledge graph entities and devise GARUM, a semantic similarity measure for knowledge graphs. GARUM relies on similarities of entity characteristics and computes similarity values considering simultaneously several entity characteristics. This combination can be manually or automatically defined with the help of a machine learning approach. We empirically evaluate the accuracy of GARUM on knowledge graphs from different domains, e.g., networks of proteins and media news. In the experimental study, GARUM exhibits higher correlation with gold standards than studied existing approaches. Thus, these results suggest that similarity measures should not consider entity characteristics in isolation; contrary, combinations of these characteristics are required to precisely determine relatedness among entities in a knowledge graph. Further, the combination functions found by a machine learning approach outperform the results obtained by the manually defined aggregation functions.

UR - http://www.scopus.com/inward/record.url?scp=85052092006&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-98809-2_11

DO - 10.1007/978-3-319-98809-2_11

M3 - Conference contribution

AN - SCOPUS:85052092006

SN - 9783319988085

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 169

EP - 183

BT - Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings

A2 - Ma, Hui

A2 - Wagner, Roland R.

A2 - Hartmann, Sven

A2 - Pernul, Gunther

A2 - Hameurlain, Abdelkader

PB - Springer Verlag

Y2 - 3 September 2018 through 6 September 2018

ER -