Information-theoretic analysis of entity dynamics on the linked open data cloud

Citation

Nishioka C & Scherp A (2016) Information-theoretic analysis of entity dynamics on the linked open data cloud. In: Demidova E, Dietze S, Szymański J & Breslin J (eds.) Dataset Profiling and Federated Search for Linked Data: Proceedings of the 3rd International Workshop on Dataset PROFIling and fEderated Search for Linked Data (PROFILES '16) co-located with the 13th ESWC 2016 Conference, volume 1597. CEUR Workshop Proceedings, 1597. PROFILES 2016: 3rd International Workshop on Dataset Profiling and Federated Search for Linked Data, Anissaras, Greece, 30.05.2016-30.05.2016. Aachen, Germany: CEUR Workshop Proceedings. http://ceur-ws.org/Vol-1597/

Abstract
The Linked Open Data (LOD) cloud is expanding continuously. Entities appear, change, and disappear over time. However, relatively little is known about the dynamics of the entities, i. e., the characteristics of their temporal evolution. In this paper, we employ clustering techniques over the dynamics of entities to determine common temporal patterns. We define an entity as RDF resource together with its attached RDF types and properties. The quality of the clusterings is evaluated using entity features such as the entities’ properties, RDF types, and pay-level domain. In addition, we investigate to what extend entities that share a feature value change together over time. As dataset, we use weekly LOD snapshots over a period of more than three years provided by the Dynamic Linked Data Observatory. Insights into the dynamics of entities on the LOD cloud has strong practical implications to any application requiring fresh caches of LOD. The range of applications is from determining crawling strategies for LOD, caching SPARQL queries, to programming against LOD, and recommending vocabularies for reusing LOD vocabularies.

Journal
CEUR Workshop Proceedings: Volume 1597

• Nishioka-Scherp 2016