Topics

The topics of interest of the workshop include, but are not limited to:

• Knowledge Discovery and Ontologies: data mining techniques using ontologies, ontology mining and knowledge discovery from ontological knowledge bases, ontology-based interpretation and validation of discovered knowledge, evaluation methodologies and metrics for the interaction of knowledge discovery and ontologies, ontology-based meta mining
• Inductive Reasoning with Concept Languages: inductive aggregation, concept retrieval and query answering, approximate classification, inductive methods and fuzzy reasoning for ontology mapping, construction and evolution, concept change and novelty detection for ontology evolution
• Statistical learning for the Semantic Web: refinement operators for concept and rule languages, concept and rules learning, kernels and instance-based learning for structured representations, semantic (dis )similarity measures and conceptual clustering, probabilistic methods for concept and rule languages
• Inductive aspects of Linked Data aggregations: learning from Linked Data to construct new vocabularies or improve existing ones, learning the mappings among vocabularies, learning for resource interlinking and entity fusion
• Web mining for the Semantic Web: graph mining, link prediction, (sequential) pattern mining, learning semantic relations, ranking methods and learning to rank
• Special focus topics: OWA vs CWA in learning, applicability of relational learning in the Semantic Web context, integration of induction and deduction, benchmarking of datasets
• Applications: (life sciences, cultural heritage, semantic multimedia, geo-informatics, recommender systems and others)