We present a new approach for computing complete, stable and preferred extensions of abstract argumentation frameworks. Unlike existing approaches that reduce these problems to the propositional satisfiability problem and solve them with the help of SAT-solvers, our approach solves them directly by making use of the fact that the mentioned extensions are contained in certain closure systems. Our algorithms enumerate these closed sets and filter the searched extensions. Experimental results show that our approach outperforms the existing approaches for a large number of the test cases.

We present a probably approximately correct algorithm for learning the terminological part of a description-logic knowledge base via subsumption queries. The axioms we learn are concept inclusions between conjunctions of concepts from a specified set of concept descriptions. By varying the distribution of queries posed to the oracle, we adapt the algorithm to improve the recall when using the resulting TBox for ontology-mediated query answering. Experimental evaluation on OWL 2 EL ontologies suggests that our approach helps significantly improve recall while maintaining a high precision of query answering. • A PAC algorithm for learning DL ontologies via subsumption queries. • A method to fine-tune query distribution during learning to boost recall in ontology-mediated query answering. • Experimental evaluation.

Russian Learner Corpus (RLC) is a large collection of learner texts in Russian written by native speakers of over forty languages. Learner errors in part of the corpus are manually corrected and annotated. Diverging from conventional error classifications, which typically focus on isolated lexical and grammatical features, the RLC error classification intends to highlight learners’ strategies employed in the process of text production, such as derivational patterns and syntactic relations (including agreement and government). In this paper, we present two open datasets derived from RLC: a manually annotated full-text dataset and a dataset with crowdsourced corrections for individual sentences. In addition, we introduce an automatic error annotation tool that, given an original sentence and its correction, locates and labels errors according to a simplified version of the RLC error-type system. We evaluate the performance of the tool on manually annotated data from RLC.

This special issue contains extended versions of selected papers presented at the International Conference on Concept Lattices and Their Applications (CLA 2010) held in Seville, Spain, from October 19 to 21, 2010.

Recently concept lattices became widely used tools for intelligent data analysis. In this paper, several algorithms that generate the set of all formal concepts and diagram graphs of concept lattices are considered. Some modifications of wellknown algorithms are proposed. Algorithmic complexity of the algorithms is studied both theoretically (in the worst case) and experimentally. Conditions of preferable use of some algorithms are given in terms of density/sparseness of underlying formal contexts. Principles of comparing practical performance of algorithms are discussed.