Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications

Wenwen Li, Sizhe Wang, Sheng Wu, Zhining Gu, Yuanyuan Tian

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Knowledge graph has become a cutting-edge technology for linking and integrating heterogeneous, cross-domain datasets to address critical scientific questions. As big data has become prevalent in today's scientific analysis, semantic data repositories that can store and manage large knowledge graph data have become critical in successfully deploying spatially explicit knowledge graph applications. This paper provides a comprehensive evaluation of the popular semantic data repositories and their computational performance in managing and providing semantic support for spatial queries. There are three types of semantic data repositories: (1) triple store solutions (RDF4j, Fuseki, GraphDB, Virtuoso), (2) property graph databases (Neo4j), and (3) an Ontology-Based Data Access (OBDA) approach (Ontop). Experiments were conducted to compare each repository's efficiency (e.g., query response time) in handling geometric, topological, and spatial-semantic related queries. The results show that Virtuoso achieves the overall best performance in both non-spatial and spatial-semantic queries. The OBDA solution, Ontop, has the second-best query performance in spatial and complex queries and the best storage efficiency, requiring the least data-to-RDF conversion efforts. Other triple store solutions suffer from various issues that cause performance bottlenecks in handling spatial queries, such as inefficient memory management and lack of proper query optimization.

Original languageEnglish (US)
Article number101884
JournalComputers, Environment and Urban Systems
StatePublished - Dec 2022


  • Knowledge graph
  • Ontology
  • Ontology-based Data Access (OBDA)
  • Property graph databases
  • Relational database
  • Triple store

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Ecological Modeling
  • General Environmental Science
  • Urban Studies


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