Abstract
Contextual information about a statement is usually represented in RDF knowledge graphs via reification: creating a fresh ‘anchor’ term that represents the statement and using it in the triples that describe it. Current approaches make the connection between the reified statement and its anchor by either extending the RDF syntax, resulting in non-compliant RDF, or via additional triples to connect the anchor with the terms of the statement, at the cost of size and complexity.
This work tackles this challenge and presents HDTr, a binary serialization format for reified triples that is model-agnostic, compact, and queryable. HDTr is based on, and compatible with, the counterpart HDT format, leveraging its underlying structure to connect the reified statements with the terms that represent them. Our evaluation shows that HDTr improves compression and retrieval time of reified statements w.r.t. several triplestores and HDT serialization of different reification approaches.
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Notes
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- 2.
Note that the triples that indicate the types of some individuals can be considered as optional, since they can be inferred by the semantics of the vocabularies.
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- 4.
- 5.
- 6.
- 7.
? is used to indicate variables in the triple pattern.
- 8.
The subscript T is added to the names of the sections to indicate that they belong to the Triples Dictionary.
- 9.
The subscript A refers to the Anchors Dictionary.
- 10.
For the sake of simplicity, we assume that tp and a have previously mapped to IDs and, conversely, the returned resultset is then mapped to their corresponding terms.
- 11.
We use a HDTr prototype implemented in C++. See the supplemental material statement at the end of the document.
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We use the HDT C++ library. Please find the concrete forked version and additional details in the supplemental material statement specified at the end of this document.
- 13.
Our hypothesis is that HDTQ was designed to encode named graphs, making assumptions (e.g., the proportion of named graphs to triples or the use of named graphs as terms in statements) that have a negative impact in its ability to encode reified statements.
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Acknowledgments
This work has been partially funded by the Spanish Ministry of Science and Innovation through LOD.For.Trees (TED2021-130667B-I00), EXTRACompact (PID2020-114635RB-I00), and PLAGEMIS-UDC (TED2021-129245B-C21) projects, and from the EU H2020 research and innovation program under the Marie Skłodowska-Curie grant No 642795.
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Gimenez-Garcia, J.M., Gautrais, T., Fernández, J.D., Martínez-Prieto, M.A. (2023). Compact Encoding of Reified Triples Using HDTr. In: Payne, T.R., et al. The Semantic Web – ISWC 2023. ISWC 2023. Lecture Notes in Computer Science, vol 14265. Springer, Cham. https://doi.org/10.1007/978-3-031-47240-4_17
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