Submodularity in Batch Active Learning and Survey Problems on Gaussian Random Fields

Many real-world datasets can be represented in the form of a graph whose edge weights designate similarities between instances. A discrete Gaussian random field (GRF) model is a finite-dimensional Gaussian process (GP) whose prior covariance is the inverse of a graph Laplacian. Minimizing the trace of the predictive covariance Sigma (V-optimality) on GRFs has proven successful in batch active learning classification problems with budget constraints. However, its worst-case bound has been missing. We show that the V-optimality on GRFs as a function of the batch query set is submodular and hence its greedy selection algorithm guarantees an (1-1/e) approximation ratio. Moreover, GRF models have the absence-of-suppressor (AofS) condition. For active survey problems, we propose a similar survey criterion which minimizes 1'(Sigma)1. In practice, V-optimality criterion performs better than GPs with mutual information gain criteria and allows nonuniform costs for different nodes

Zur Lexikon-Grammatik-Schnittstelle in einem hypermedialen Informationssystem

Der Beitrag beschreibt Konzeption und Umsetzung der Anbindung von lexikalischen Datenbanken an das grammatische Informationssystem grammis, das seit Mitte 1993 am Institut für deutsche Sprache (IDS) entwickelt wird. Im Rahmen dieses Projekts wird erforscht, wie grammatisches Wissen mit moderner Computertechnik anschaulich dargestellt und verständlich vermittelt werden kann

Using a domain ontology for the semantic-statistical classification of specialist hypertexts

In this feasibility study we aim at contributing at the practical use of domain ontologies for hypertext classification by introducing an algorithm generating potential keywords. The algorithm uses structural markup information and lemmatized word lists as well as a domain ontology on linguistics. We present the calculation and ranking of keyword candidates based on ontology relationships, word position, frequency information, and statistical significance as evidenced by log-likelihood tests. Finally, the results of our machine-driven classification are validated empirically against manually assigned keywords

Conjunctive query inseparability of OWL 2 QL TBoxes

The OWL2 profile OWL 2 QL, based on the DL-Lite family of description logics, is emerging as a major language for developing new ontologies and approximating the existing ones. Its main application is ontology based data access, where ontologies are used to provide background knowledge for answering queries over data. We investigate the corresponding notion of query inseparability (or equivalence) for OWL 2 QL ontologies and show that deciding query inseparability is PSpace-hard and in ExpTime. We give polynomial-time (incomplete) algorithms and demonstrate by experiments that they can be used for practical module extraction

Module extraction via query inseparability in OWL 2 QL

We show that deciding conjunctive query inseparability for OWL 2 QL ontologies is PSpace-hard and in ExpTime. We give polynomial-time (incomplete) algorithms and demonstrate by experiments that they can be used for practical module extraction