Co-evolution of RDF datasets

Citation data:

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 9671, Page: 225-243

Publication Year:
Usage 16
Downloads 10
Abstract Views 6
Captures 11
Readers 11
Citations 1
Citation Indexes 1
Repository URL:
Faisal, Sidra; Endris, Kemele M.; Shekarpour, Saeedeh; Auer, Sören; Vidal, Maria-Esther
Springer Nature
Mathematics; Computer Science; Dataset Synchronization; Dataset Co-Evolution; Conflict Identification; Conflict Resolution; RDF Dataset; Dataset Synchronization; Dataset Co-Evolution; Conflict Identification; Conflict Resolution; RDF Dataset; Bioinformatics; Communication; Communication Technology and New Media; Computer Sciences; Databases and Information Systems; Life Sciences; OS and Networks; Physical Sciences and Mathematics; Science and Technology Studies; Social and Behavioral Sciences
conference paper description
Linking Data initiatives have fostered the publication of large number of RDF datasets in the Linked Open Data (LOD) cloud, as well as the development of query processing infrastructures to access these data in a federated fashion. However, different experimental studies have shown that availability of LOD datasets cannot be always ensured, being RDF data replication required for envisioning reliable federated query frameworks. Albeit enhancing data availability, RDF data replication requires synchronization and conflict resolution when replicas and source datasets are allowed to change data over time, i.e., co-evolution management needs to be provided to ensure consistency. In this paper, we tackle the problem of RDF data co-evolution and devise an approach for conflict resolution during co-evolution of RDF datasets. Our proposed approach is property-oriented and allows for exploiting semantics about RDF properties during co-evolution management. The quality of our approach is empirically evaluated in different scenarios on the DBpedia-live dataset. Experimental results suggest that proposed proposed techniques have a positive impact on the quality of data in source datasets and replicas.