Abstract
We introduce a system that provides explanations in Natural Language for individual clusters of RDF resources, where clusters are obtained using an external clustering tool. Our system is based on the theory of (Least) Common Subsumers (CS) in RDF. We propose an optimized algorithm for computing a CS, which allows us to compute the CS for up to 80 RDF resources (each with its own RDF-graph of linked data). We then generate a Natural Language sentence to describe each cluster. A unique aspect of our explanations is the use of relative sentences, including nested ones, to represent blank nodes in an RDF-path. We demonstrate the usefulness of our tool by describing the resulting clusters of a real, publicly available, dataset on Public Procurements.
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Data Availability Statement
Data publicly available at https://tbfy.github.io/data/.
Notes
- 1.
The same conclusion was recently reached [1] using cycles instead of trees.
- 2.
A lean graph G [17] is an RDF-graph which is \(\subseteq \)-minimal with respect to all other RDF-graphs logically equivalent to G.
- 3.
Average execution time for running Algorithm 1 on 80 randomly selected resources—machine equipped with an Intel i7 processor at 3.60 GHz and 32 GB RAM.
- 4.
- 5.
- 6.
The full Knowledge Graph is downloadable at https://tbfy.github.io/data/.
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Acknowledgements
We acknowledge support by project “LIFE: the itaLian system wIde Frailty nEtwork” founded by Ministry of Health (CUP D93C22000640001).
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Colucci, S., Donini, F.M., Di Sciascio, E. (2024). Explaining Commonalities of Clusters of RDF Resources in Natural Language. In: Appice, A., Azzag, H., Hacid, MS., Hadjali, A., Ras, Z. (eds) Foundations of Intelligent Systems. ISMIS 2024. Lecture Notes in Computer Science(), vol 14670. Springer, Cham. https://doi.org/10.1007/978-3-031-62700-2_15
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