Abstract
Knowledge graphs usually contain much implicit semantic information, which needs to be further mined through semantic inference. Current algorithms can effectively accomplish such task, however they often require a full re-reasoning even when only a few new triples is added to expand the knowledge graph. In this paper, we propose an incremental reasoning algorithm which can effectively avoid re-reasoning over the entire knowledge graph while keeping the relative completeness of the final deduction results. Key to our approach is the filter algorithms which reduce the scale of data that need to be considered and a delay strategy which limit the number of time-consuming iterations while still preserve relative completeness. Extensive experiments and comprehensive evaluations are conducted and experimental results prove that our methods significantly outperform re-reasoning methods.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (Grand No. 61502022) and State Key Laboratory of Software Development Environment (Grand No. SKLSDE-2017ZX-17).
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Wang, Y., Luo, J. (2018). An Incremental Reasoning Algorithm for Large Scale Knowledge Graph. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_45
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