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Representing and Querying Data Tensors in RDF and SPARQL
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DOI：10.1007/978-3-031-99554-5_12.png)
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Abstract
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Embedding tensors in databases has recently gained in significance, due to the rapid proliferation of machine learning methods (including LLMs) which produce embeddings in the form of tensors. To support emerging use cases hybridizing machine learning with knowledge graphs, a robust and efficient tensor representation scheme is needed. We introduce a novel approach for representing data tensors as literals in RDF, along with an extension of SPARQL implementing specialized functionalities for handling such literals. The extension includes 36 SPARQL functions and four aggregates. To support this approach, we provide a thoroughly tested, open-source implementation based on Apache Jena, along with an exemplary knowledge graph and query set.
Keywords:
Data tensors
SPARQL
RDF
Embedding
Neurosymbolic AI
Large Language Models
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