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The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach.

Based --- Combining --- Corpus --- Data --- from --- Learning --- machine learning --- natural language learning --- Ontology --- Reasoning --- relation labeling --- Relations --- Semantic --- Sources --- Techniques --- Wohlgenannt

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Learning and memory processes are basic features of human existence. They allow us to (un)consciously adapt to changes in our social and physical environment in a variety of ways and may have been a precursor for survival in human evolution. Through several reviews and original work the book focuses on three key topics that enhanced our understanding of the topic in the last twenty years: first, the role of real-time auditory feedback in learning, second, the role of motor aspects for learning and memory, and third, representations in memory and the role of sleep on memory consolidation.

Languages --- Phonetics, phonology --- Learning --- Memory --- Speech --- Feedback --- Autism --- Gestures --- Sleep --- Exemplar models --- Sensorimotor adaptation --- Second language learning --- Formant perturbation --- Speech sound errors --- Non-native speech --- Speech motor control --- Coarticulation --- Reduction