Abstract
We present an algorithm to inductively learn Web Ontology Language (OWL) 2 property chains to be used in object subproperty axioms. For efficiency, it uses specialized encodings and data structures based on hash-maps and sparse matrices. The algorithm is based on the frequent pattern search principles and uses a novel measure called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s-support</i> . We prove soundness and termination of the algorithm, and report on evaluation where we mine axioms from DBpedia 2016-10. We extensively discuss the 36 mined axioms and conclude that 30 (83%) of them are correct and could be added to the ontology.
Highlights
The Semantic Web, as envisioned in the paper by BernersLee et al [1], is the Internet where the semantics of information is explicitly represented and comprehensible for both humans and machines in an unambiguous way
The representation of choice for the Semantic Web are node- and edge-labeled graphs expressed in Resource Description Framework (RDF) and accompanied by formal semantics expressed in Web Ontology Language 2 (OWL 2), both detailed in subsection II-A
Ontology learning is a well-developed area in the research on the Semantic Web, concerned with aautomatic creation of ontologies from various, preexisting resources, structured and unstructured alike
Summary
The Semantic Web, as envisioned in the paper by BernersLee et al [1], is the Internet where the semantics of information is explicitly represented and comprehensible for both humans and machines in an unambiguous way. The representation of choice for the Semantic Web are node- and edge-labeled graphs expressed in Resource Description Framework (RDF) and accompanied by formal semantics expressed in Web Ontology Language 2 (OWL 2), both detailed in subsection II-A. One of the main blockers, present in the Semantic Web, but in all other knowledge-oriented systems, is the so-called knowledge acquisition bottleneck, which represents the slowness and complexity of collecting and formalizing general knowledge, e.g., as a formal ontology To address this blocker in the context of the Semantic Web, a problem of ontology learning was posed in 2001 by Maedche and Staab [2], and extensively researched since . An approach for discovering property characteristic axioms based on recurrent neural networks was recently proposed by Potoniec [14]
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