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The research goal of this work is to investigate modeling patterns that recur in ontologies. Such patterns may originate from certain design solutions, and they may possibly indicate emerging ontology design patterns. We describe our tree-mining method for identifying the emerging design patterns. The method works in two steps: (1) we transform the ontology axioms in a tree shape in order to find axiom patterns; and then, (2) we use association analysis to mine co-occuring axiom patterns in order to extract emerging design patterns. We conduct an experimental study on a set of 331 ontologies from the BioPortal repository. We show that recurring axiom patterns appear across all individual ontologies, as well as across the whole set. In individual ontologies, we find frequent and non-trivial patterns with and without variables. Some of the former patterns have more than 300,000 occurrences. The longest pattern without a variable discovered from the whole ontology set has size 12, and it appears in 14 ontologies. To the best of our knowledge, this is the first method for automatic discovery of emerging design patterns in ontologies. Finally, we demonstrate that we are able to automatically detect patterns, for which we have manually confirmed that they are fragments of ontology design patterns described in the literature. Since our method is not specific to particular ontologies, we conclude that we should be able to discover new, emerging design patterns for arbitrary ontology sets.

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

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In this study, we present Swift Linked Data Miner, an interruptible algorithm that can directly mine an online Linked Data source (e.g., a SPARQL endpoint) for OWL 2 EL class expressions to extend an ontology with new SubClassOf: axioms. The algorithm works by downloading only a small part of the Linked Data source at a time, building a smart index in the memory and swiftly iterating over the index to mine axioms. We propose a transformation function from mined axioms to RDF Data Shapes. We show, by means of a crowdsourcing experiment, that most of the axioms mined by Swift Linked Data Miner are correct and can be added to an ontology. We provide a ready to use Protégé plugin implementing the algorithm, to support ontology engineers in their daily modeling work.

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