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This paper introduces the term semantic data mining to denote a data mining approach where domain ontologies are used as background knowledge for data mining. It is motivated by successful applications of SEGS (search for enriched gene sets), a system that uses biological ontolo-gies as background knowledge to construct descriptions of interesting gene sets(More)
This paper addresses semantic data mining, a new data mining paradigm in which ontologies are exploited in the process of data mining and knowledge discovery. This paradigm is introduced together with new semantic subgroup discovery systems SDM-search for enriched gene sets (SEGS) and SDM-Aleph. These systems are made publicly available in the new(More)
With the expanding of the Semantic Web and the availability of numerous ontologies which provide domain background knowledge and semantic descriptors to the data, the amount of semantic data is rapidly growing. The data mining community is faced with a paradigm shift: instead of mining the abundance of empirical data supported by the background knowledge,(More)
Subgroup discovery (SD) methods can be used to find interesting subsets of objects of a given class. While subgroup describing rules are themselves good explanations of the subgroups, domain ontologies can provide additional descriptions to data and alternative explanations of the constructed rules. Such explanations in terms of higher level ontology(More)
Definition extraction is an emerging field of NLP research. This paper presents an innovative information extraction work-flow aimed to extract definition candidates from domain-specific corpora, using mor-phosyntactic patterns, automatic terminology recognition and semantic tagging with wordnet senses. The workflow, implemented in a novel service-oriented(More)
This paper describes a propositionalization technique called wordification. Wordification is inspired by text mining and can be seen as a transformation of a relational database into a corpus of documents. As in previous propositionalization methods, after the wordification step any propositional data mining algorithm can be applied. The most notable(More)
Subgroup discovery aims at constructing symbolic rules that describe statistically interesting subsets of instances with a chosen property of interest. Semantic subgroup discovery extends standard subgroup discovery approaches by exploiting ontological concepts in rule construction. Compared to previously developed semantic data mining systems SDM-SEGS and(More)