Abstract
Knowledge graphs have become essential for integrating data from heterogeneous sources powering intelligent applications. Integrating data from various sources often results in incomplete knowledge that needs to be enriched based on custom inference rules. Handling a large number of facts requires a scalable storage layer that must be seamlessly integrated into the reasoning algorithms to guarantee efficient evaluation of rules and query answering over the knowledge graph. To this end, we present SemReasoner, a comprehensive, scalable, high-performance knowledge graph store and rule-based reasoner. SemReasoner includes a deductive reasoning engine and fully supports document store functionality for JSON documents. SemReasoner’s modular architecture is easy to extend and integrate into existing IT landscapes and applications. We evaluate SemReasoner against the state-of-the-art rule-based reasoning engines using test cases from OpenRuleBench. The results show that SemReasoner outperforms existing engines in most test cases.
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Notes
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For performance reasons, the first configuration is preferable.
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see Sect. 6 for a detailed description of the rule-engines.
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For a full list and detailed description of those test cases consider [17].
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for more results and the benchmarking data check https://github.com/kev-ang/SemReasoner and the ESWC branch in https://github.com/kev-ang/RUBEN.
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SemReasoner has been tested with up to 32B triples.
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Angele, K., Angele, J., Simsek, U., Fensel, D. (2023). SemReasoner - A High-Performance Knowledge Graph Store and Rule-Based Reasoner. In: Pesquita, C., et al. The Semantic Web. ESWC 2023. Lecture Notes in Computer Science, vol 13870. Springer, Cham. https://doi.org/10.1007/978-3-031-33455-9_34
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