Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications. (December 2022)

• Record Type: Journal Article
• Authors: Li, Wenwen; Wang, Sizhe; Wu, Sheng; Gu, Zhining; Tian, Yuanyuan
• Is Part Of: Computers, environment and urban systems. Volume 98(2023)
• Publication Date: 2022-12
• Subjects: Triple store -- Property graph databases -- Ontology -- Knowledge graph -- Relational database -- Ontology-based Data Access (OBDA); City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2022.101884
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: 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. Highlights: Provide a comprehensive review of the popular semantic data repositories and their computational performance in managing and providing semantic support for spatial queries. Three types of semantic data repositories, including triple stores, property graph databases and OBDA solutions are evaluated. Each repository's response time in handling geometric, topological, and spatial-semantic related queries is compared. Results show that Virtuoso achieves the overall best performance in both non-spatial and spatial-semantic queries, followed by OBDA solution Ontop.