Abstract
RDF knowledge base summarization produces a compact and faithful abstraction for entities, relations, and ontologies. The summary is critical to a wide range of knowledge-based applications, such as query answering and KB indexing. The patterns of graph structure and/or association are commonly employed to summarize and reduce the number of triples. However, knowledge coverage is low in state-of-the-art techniques due to limited expressiveness of patterns, where variables are under-explored to capture matched arguments in relations. This paper proposes a novel summarization technique based on first-order logic rules where quantified variables are extensively taken into account. We formalize this new summarization problem to illustrate how the rules are used to replace triples. The top-down rule mining is also improved to maximize the reusability of cached results. Qualitative and quantitative analyses are comprehensively done by comparing our technique against state-of-the-art tools, with showing that our approach outperforms the rivals in conciseness, completeness, and performance.
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Notes
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E, D, S are available at: https://relational.fit.cvut.cz/; Fm, Fs are synthetic, and the generators are available with the project source code.
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Wang, R., Sun, D., Wong, R. (2023). RDF Knowledge Base Summarization by Inducing First-Order Horn Rules. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13714. Springer, Cham. https://doi.org/10.1007/978-3-031-26390-3_12
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