Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

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

Multiphonon emission model of spin-dependent exciton formation in organic semiconductors

The maximum efficiency in organic light-emitting diodes (OLEDs) depends on the ratio, $r=k_S/k_T$, where $k_S$ ($k_T$) is the singlet (triplet) exciton formation rate. Several recent experiments found that r increases with increasing oligomer length from a value $r \approx 1$ in monomers and short oligomers. Here, we model exciton formation as a multi-phonon emission process. Our model is based on two assertions: (i) More phonons are emitted in triplet formation than in singlet formation. (ii) The Huang-Rhys parameter for this phonon emission is smaller in long oligomers than in short ones. We justify these assertions based on recent experimental and theoretical data.Comment: 8 pages, 7 figure