A Semantic Similarity Measure for Expressive Description Logics

A totally semantic measure is presented which is able to calculate a similarity value between concept descriptions and also between concept description and individual or between individuals expressed in an expressive description logic. It is applicable on symbolic descriptions although it uses a numeric approach for the calculus. Considering that Description Logics stand as the theoretic framework for the ontological knowledge representation and reasoning, the proposed measure can be effectively used for agglomerative and divisional clustering task applied to the semantic web domain.Comment: 13 pages, Appeared at CILC 2005, Convegno Italiano di Logica Computazionale also available at http://www.disp.uniroma2.it/CILC2005/downloads/papers/15.dAmato_CILC05.pd

An unsupervised approach to disjointness learning based on terminological cluster trees

In the context of the Semantic Web regarded as a Web of Data, research efforts have been devoted to improving the quality of the ontologies that are used as vocabularies to enable complex services based on automated reasoning. From various surveys it emerges that many domains would require better ontologies that include non-negligible constraints for properly conveying the intended semantics. In this respect, disjointness axioms are representative of this general problem: these axioms are essential for making the negative knowledge about the domain of interest explicit yet they are often overlooked during the modeling process (thus affecting the efficacy of the reasoning services). To tackle this problem, automated methods for discovering these axioms can be used as a tool for supporting knowledge engineers in modeling new ontologies or evolving existing ones. The current solutions, either based on statistical correlations or relying on external corpora, often do not fully exploit the terminology. Stemming from this consideration, we have been investigating on alternative methods to elicit disjointness axioms from existing ontologies based on the induction of terminological cluster trees, which are logic trees in which each node stands for a cluster of individuals which emerges as a sub-concept. The growth of such trees relies on a divide-and-conquer procedure that assigns, for the cluster representing the root node, one of the concept descriptions generated via a refinement operator and selected according to a heuristic based on the minimization of the risk of overlap between the candidate sub-clusters (quantified in terms of the distance between two prototypical individuals). Preliminary works have showed some shortcomings that are tackled in this paper. To tackle the task of disjointness axioms discovery we have extended the terminological cluster tree induction framework with various contributions: 1) the adoption of different distance measures for clustering the individuals of a knowledge base; 2) the adoption of different heuristics for selecting the most promising concept descriptions; 3) a modified version of the refinement operator to prevent the introduction of inconsistency during the elicitation of the new axioms. A wide empirical evaluation showed the feasibility of the proposed extensions and the improvement with respect to alternative approaches

Minimal long-term neurobehavioral impairments after endovascular perforation subarachnoid hemorrhage in mice

AbstractCognitive deficits are among the most severe and pervasive consequences of aneurysmal subarachnoid hemorrhage (SAH). A critical step in developing therapies targeting such outcomes is the characterization of experimentally-tractable pre-clinical models that exhibit multi-domain neurobehavioral deficits similar to those afflicting humans. We therefore searched for neurobehavioral abnormalities following endovascular perforation induction of SAH in mice, a heavily-utilized model. We instituted a functional screen to manage variability in injury severity, then assessed acute functional deficits, as well as activity, anxiety-related behavior, learning and memory, socialization, and depressive-like behavior at sub-acute and chronic time points (up to 1 month post-injury). Animals in which SAH was induced exhibited reduced acute functional capacity and reduced general activity to 1 month post-injury. Tests of anxiety-related behavior including central area time in the elevated plus maze and thigmotaxis in the open field test revealed increased anxiety-like behavior at subacute and chronic time-points, respectively. Effect sizes for subacute and chronic neurobehavioral endpoints in other domains, however, were small. In combination with persistent variability, this led to non-significant effects of injury on all remaining neurobehavioral outcomes. These results suggest that, with the exception of anxiety-related behavior, alternate mouse models are required to effectively analyze cognitive outcomes after SAH.</jats:p

Performance Assessment in Fingerprinting and Multi Component Quantitative NMR Analyses

An interlaboratory comparison (ILC) was organized with the aim to set up quality control indicators suitable for multicomponent quantitative analysis by nuclear magnetic resonance (NMR) spectroscopy. A total of 36 NMR data sets (corresponding to 1260 NMR spectra) were produced by 30 participants using 34 NMR spectrometers. The calibration line method was chosen for the quantification of a five-component model mixture. Results show that quantitative NMR is a robust quantification tool and that 26 out of 36 data sets resulted in statistically equivalent calibration lines for all considered NMR signals. The performance of each laboratory was assessed by means of a new performance index (named Qp-score) which is related to the difference between the experimental and the consensus values of the slope of the calibration lines. Laboratories endowed with a Qp-score falling within the suitable acceptability range are qualified to produce NMR spectra that can be considered statistically equivalent in terms of relative intensities of the signals. In addition, the specific response of nuclei to the experimental excitation/relaxation conditions was addressed by means of the parameter named NR. NR is related to the difference between the theoretical and the consensus slopes of the calibration lines and is specific for each signal produced by a well-defined set of acquisition parameters

Towards Interpretable Probabilistic Classification Models for Knowledge Graphs

Tackling the problem of learning probabilistic classifiers that can be used the context of knowledge graphs, we describe an inductive approach based on learning networks of Bernoulli variables. Namely, we consider the application of multivariate Bernoulli models, a simple one and a two-levels mixture model. In addition, we also consider a hierarchical model combining the multivariate Bernoulli model with a restricted Boltzmann machine as the first level. We show how such models can be converted into probabilistic rule bases ensuring more understandability. A preliminary empirical evaluation is presented to test the effectiveness of these models on a number of classification problems with different knowledge graphs