AL-QuIn

Lisi, Francesca A.

doi:10.4018/jswis.2011070101

Cited by 12 publications

(1 citation statement)

References 63 publications

(100 reference statements)

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“…In turn, onto-relational methods SPADA (Lisi & Malerba, 2004), SEMINTEC (Józefowska, Ławrynowicz, & Łukaszewski, 2010), and AL-QuIn (Lisi, 2011) all exploit taxonomies in some way, and all use semantic generality relations such as generalized subsumption or query containment. SPADA (further refined to AL-QuIn) uses a hybrid knowledge representation formalism, AL-log , that combines Datalog with description logic.…”

Section: Related Workmentioning

confidence: 99%

Pattern Based Feature Construction in Semantic Data Mining

Ławrynowicz

Potoniec

2014

International Journal on Semantic Web and Information Systems

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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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Section: Related Workmentioning

confidence: 99%

Pattern Based Feature Construction in Semantic Data Mining

Ławrynowicz

Potoniec

2014

International Journal on Semantic Web and Information Systems

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On the Complexity of Learning Description Logic Ontologies

Ozaki

2020

Reasoning Web. Declarative Artificial Intelligence

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Ontologies are a popular way of representing domain knowledge, in particular, knowledge in domains related to life sciences. (Semi-) automating the process of building an ontology has attracted researchers from different communities into a field called "Ontology Learning". We provide a formal specification of the exact and the probably approximately correct learning models from computational learning theory. Then, we recall from the literature complexity results for learning lightweight description logic (DL) ontologies in these models. Finally, we highlight other approaches proposed in the literature for learning DL ontologies.

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