Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications
Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications
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DOI：
10.1016/j.compenvurbsys.2022.101884
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Wenwen Li;Sizhe Wang;Sheng Wu;Zhining Gu;Yuanyuan Tian
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文献类型：
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作者：
Wenwen Li;Sizhe Wang;Sheng Wu;Zhining Gu;Yuanyuan Tian
知识图谱已经成为连接和集成异构、跨领域数据集以解决关键科学问题的前沿技术。随着大数据在当今科学分析中的普及，能够存储和管理大型知识图谱数据的语义数据存储库对于成功部署空间显式知识图谱应用程序至关重要。本文对目前流行的语义数据存储库及其在管理和提供空间查询语义支持方面的计算性能进行了综合评价。有三种类型的语义数据存储库：(1)三重存储解决方案（RDF4j, Fuseki, GraphDB, Virtuoso），(2)属性图数据库（Neo4j），(3)基于本体的数据访问（OBDA）方法（Ontop）。实验比较了每个存储库在处理几何、拓扑和空间语义相关查询方面的效率（例如查询响应时间）。结果表明，Virtuoso在非空间和空间语义查询中都达到了最佳的总体性能。OBDA解决方案Ontop在空间和复杂查询方面具有第二好的查询性能和最佳的存储效率，需要最少的数据到rdf转换工作。其他三重存储解决方案存在各种问题，这些问题会在处理空间查询时导致性能瓶颈，例如低效的内存管理和缺乏适当的查询优化。
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.