Abstract
Temporal knowledge is crucial in many knowledge-based systems. A common approach for expressing temporal knowledge on the syntactical level is to add temporal triples as metadata for triples, called RDF reification. However, this will increase the volume and complexity of data, and leads to a decrease in the storage and retrieval performance for temporal RDF. In the field of RDF storage, the bit matrix is a simple yet highly efficient structure for indexing RDF data. In this paper, we provide an extension to bit matrix architecture (TBitStore) to support the indexing of temporal RDF. To begin with, TBitStore constructs an index over both Subject-Object key and temporal information. Then, it uses a time-bound matching mechanism to accelerate subquery execution. Moreover, it leverages a temporal statistics-based index to optimize the query plans. The experimental results show that TBitStore can reduce the storage space compared to the original bit matrix database for temporal RDF data, as well as improve the performance of querying temporal RDF.
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This work is supported by the Science and Technology Innovation 2030 - “New Generation Artificial Intelligence” (2020AAA0108501).
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Zhou, T., Liu, Y., Zhang, H., Gao, F., Zhang, X. (2022). Optimization of Bit Matrix Index for Temporal RDF. In: Sun, M., et al. Knowledge Graph and Semantic Computing: Knowledge Graph Empowers the Digital Economy. CCKS 2022. Communications in Computer and Information Science, vol 1669. Springer, Singapore. https://doi.org/10.1007/978-981-19-7596-7_8
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DOI: https://doi.org/10.1007/978-981-19-7596-7_8
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