Validating DBpedia Triple Sets for Natural Language Generation

Abstract:We present a study of the quality of individual DBpedia triples from the perspective of Natural Language Generation, and propose and evaluate an approach for collecting entity-specific triple sets that filters out questionable triples while minimizing the loss of correct ones. We show in an evaluation against manually annotated data that with validation rules, it is possible to reach 98% precision in triple selection, and with improvements to a few Property definitions, it is possible to improve recall by 40% without harming precision.

Computation and Language (cs.CL)

arXiv:2609.07589 [cs.CL]

(or arXiv:2609.07589v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.07589 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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