Jedrzej Potoniec

Pattern Based Feature Construction in Semantic Data Mining

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.

Fr-ONT: an algorithm for frequent concept mining with formal ontologies

The paper introduces a task of frequent concept mining: mining frequent patterns of the form of (complex) concepts expressed in description logic. We devise an algorithm for mining frequent patterns expressed in standard EL++ description logic language. We also report on the implementation of our method. As description logic provides the theorethical foundation for standard Web ontology language OWL, and description logic concepts correspond to OWL classes, we envisage the possible use of our proposed method on a broad range of data and knowledge intensive applications that exploit formal ontologies.

ASPARAGUS - a system for automatic SPARQL query results aggregation using semantics

We present a prototype system, named ASPARAGUS, that performs aggregation of SPARQL query results on a semantic baseline, that is by an exploitation of the background ontology expressing the semantics of the returned results. The system implements the recent research results on semantic grouping, and semantic clustering. In the former case, results are deductively grouped taking into account the subsumption hierarchy deduced by the knowledge base. In the latter case, the results are clustered that is inductively formed, based on the similarity of the individual resources. We discuss the architecture of the implemented system, its underlying technologies, and applied technical solutions.

Finding Unexplainable Triples in an RDF Graph.

We consider how to select a subgraph of an RDF graph in an ontology learning problem in order to avoid learning redundant axioms. We propose to address this by selecting RDF triples that can not be inferred using a reasoner and we present an algorithm to find them.

Discovery of emerging design patterns in ontologies using tree mining

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.

Swift Linked Data Miner Extension for WebProtégé

Swift Linked Data Miner (SLDM) is a data mining algorithm capable to infer new knowledge and thus extend an ontology by mining a Linked Data dataset. We present an extension to WebProtege providing SLDM capabilities in a web browser. The extension is open source and readily available to use.

Towards Mining Patterns for Exploratory Search with Keval Algorithm

For a given set of URIs, finding their common graph patterns may provide useful knowledge. We present an algorithm searching for the best patterns while trying to extend the set of relevant URIs. It involves interaction with the user in order to supervise extension of the set.

Hypothesis-Driven Interactive Classification Based on AVO

We consider a classification process, that the representation precision of new examples is interactively increased. We use an attribute value ontology (AVO) to represent examples at different levels of abstraction (levels of precision). This precision can be improved by conducting diagnostic tests. The selection of these diagnostic tests is generally a non-trivial task. We consider the hypothesis-driven interactive classification, where a decision maker chooses diagnostic tests that approve or reject her hypothesis (the classification of a new example to a one or more selected decision classes). Specifically, we present two approaches to the selection of diagnostic tests: the use of the measure of information gain and the analysis of the classification results for these diagnostic tests using an ontological Bayes classifier (OBC).