Classifying Candidate Axioms via Dimensionality Reduction Techniques

We assess the role of similarity measures and learning methods in classifying candidate axioms for automated schema induction through kernel-based learning algorithms. The evaluation is based on (i) three different similarity measures between axioms, and (ii) two alternative dimensionality reduction techniques to check the extent to which the considered similarities allow to separate true axioms from false axioms. The result of the dimensionality reduction process is subsequently fed to several learning algorithms, comparing the accuracy of all combinations of similarity, dimensionality reduction technique, and classification method. As a result, it is observed that it is not necessary to use sophisticated semantics-based similarity measures to obtain accurate predictions, and furthermore that classification performance only marginally depends on the choice of the learning method. Our results open the way to implementing efficient surrogate models for axiom scoring to speed up ontology learning and schema induction methods

Using Grammar-Based Genetic Programming for Mining Disjointness Axioms Involving Complex Class Expressions

International audienceIn the context of the Semantic Web, learning implicit knowledge in terms of axioms from Linked Open Data has been the object of much current research. In this paper, we propose a method based on grammar-based genetic programming to automatically discover disjoint-ness axioms between concepts from the Web of Data. A training-testing model is also implemented to overcome the lack of benchmarks and comparable research. The acquisition of axioms is performed on a small sample of DBpedia with the help of a Grammatical Evolution algorithm. The accuracy evaluation of mined axioms is carried out on the whole DBpe-dia. Experimental results show that the proposed method gives high accuracy in mining class disjointness axioms involving complex expressions

Active axial stress in mouse aorta

The study verifies the development of active axial stress in the wall of mouse aorta over a range of physiological loads when the smooth muscle cells are stimulated to contract. The results obtained show that the active axial stress is virtually independent of the magnitude of pressure, but depends predominately on the longitudinal stretch ratio. The dependence is non-monotonic and is similar to the active stress-stretch dependence in the circumferential direction reported in the literature. The expression for the active axial stress fitted to the experimental data shows that the maximum active stress is developed at longitudinal stretch ratio 1.81, and 1.56 is the longitudinal stretch ratio below which the stimulation does not generate active stress. The study shows that the magnitude of active axial stress is smaller than the active circumferential stress. There is need for more experimental investigations on the active response of different types of arteries from different species and pathological conditions. The results of these studies can promote building of refined constrictive models in vascular rheology. (C) 2012 Elsevier Ltd. All rights reserved