General Terminology Induction in OWL

Abstract. Automated acquisition, or learning, of ontologies has attracted re-search attention because it can help ontology engineers build ontologies and give domain experts new insights into their data. However, existing approaches to on-tology learning are considerably limited, e.g. focus on learning descriptions for given classes, require intense supervision and human involvement, make assump-tions about data, do not fully respect background knowledge. We investigate the problem of general terminology induction, i.e. learning sets of general class in-clusions, GCIs, from data and background knowledge. We introduce measures that evaluate logical and statistical quality of a set of GCIs. We present methods to compute these measures and an anytime algorithm that induces sets of GCIs. Our experiments show that we can acquire interesting sets of GCIs and provide insights into the structure of the search space.

Predicting Performance of OWL Reasoners: Locally or Globally?

We propose a novel approach to performance prediction of OWL reasoners. The existing strategies take a view of an entire ontology corpus: they extract multiple fea-tures from the ontologies in the corpus and use them for training machine learning models. We call these global approaches. In contrast, our approach is a local one: it examines a single ontology (independent of any cor-pus), selects suitable, small ontology subsets, and ex-trapolates their performance measurements to the whole ontology. Our results show that this simple idea leads to accurate performance predictions, comparable or su-perior to global approaches. Our second contribution concerns ontology features: we are the first to investi-gate intercorrelation of ontology features using Princi-pal Component Analysis (PCA). We report that extract-ing multiple features- as global approaches do- makes surprisingly little difference for performance prediction. In fact, it turns out that the ontologies in two major cor-pora basically only differ in one or two features

Predicting OWL Reasoners: Locally or Globally?

Abstract. We propose a novel approach for performance prediction of OWL reasoners. It selects suitable, small ontology subsets, and then extrapolates rea-soner’s performance on them to the whole ontology. We investigate intercorrela-tion of ontology features using PCA. Finally, we discuss various error measures for performance prediction and compare our approach against an existing one using these measures.

General Terminology Induction in OWL

Abstract. Automated acquisition, or learning, of ontologies has attracted re-search attention because it can help ontology engineers build ontologies and give domain experts new insights into their data. However, existing approaches to on-tology learning are considerably limited, e.g. focus on learning descriptions for given classes, require intense supervision and human involvement, make assump-tions about data, do not fully respect background knowledge. We investigate the problem of general terminology induction, i.e. learning sets of general class in-clusions, GCIs, from data and background knowledge. We introduce measures that evaluate logical and statistical quality of a set of GCIs. We present methods to compute these measures and an anytime algorithm that induces sets of GCIs. Our experiments show that we can acquire interesting sets of GCIs and provide insights into the structure of the search space.