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

Signaling in natural killer cells: SHIP, 2B4 and the Kinome

The NK cell is a large granular lymphocyte that plays a key role in protecting the body against numerous pathogens including parasites, intracellular bacteria, viral infections, as well as showing anti-tumor activity and playing a role in the rejection of allogeneic BM. Unlike other lymphocytic cell types, that utilize rearranging receptors, NK cells are regulated by a complex array of germ line encoded activating and inhibitory receptors. NK cells are often described as a front line or rapid defense given their response to stimuli can be immediate, although they also maintain functions that extend their role well into the adaptive immune system. Inhibitory receptors that recognize MHC class I molecules regulate NK cell responses and self-tolerance. Recent evidence indicates self-ligands not present in the MHC locus can also modulate NK function. We previously demonstrated that the NK receptor repertoire is disrupted by SHIP-deficiency. Here we show that an inhibitory receptor, 2B4, that recognizes an MHC-independent ligand is over expressed in NK cells of SHIP-/- mice at all stages of NK development and differentiation. Overexpression of 2B4 compromises key cytolytic NK functions, including killing of allogeneic, tumor and viral targets. These results demonstrate that in SHIP-/- NK cell 2B4 is the dominant inhibitory receptor. We then furthered this finding by examining the molecular basis of 2B4 dominance. We show that in SHIP-/- NK cells there is increased 2B4 expression as well as a strong bias towards the 2B4L isoform. We have also identified a greater than tenfold increase in SHP1 recruitment to 2B4. Consistent with this SHP1 over recruitment,both a broad and a selective SHP1 inhibitor restore SHIP-/- NK killing of complex targets.Through this study we have identified the molecular mechanism of 2B4 receptor dominance as SHP1 over-recruitment.In addition we have utilized protein array technology to explore NK signaling through the determination of the NK kinome. To this end we have been able to identify multiple pathways that may mark crucial differences between the mature and immature NK cell

Numerical Methods for the Nonlocal Wave Equation of the Peridynamics

In this paper we will consider the peridynamic equation of motion which is described by a second order in time partial integro-differential equation. This equation has recently received great attention in several fields of Engineering because seems to provide an effective approach to modeling mechanical systems avoiding spatial discontinuous derivatives and body singularities. In particular, we will consider the linear model of peridynamics in a one-dimensional spatial domain. Here we will review some numerical techniques to solve this equation and propose some new computational methods of higher order in space; moreover we will see how to apply the methods studied for the linear model to the nonlinear one. Also a spectral method for the spatial discretization of the linear problem will be discussed. Several numerical tests will be given in order to validate our results

Learning terminological Naïve Bayesian classifiers under different assumptions on missing knowledge

Knowledge available through Semantic Web standards can easily be missing, generally because of the adoption of the Open World Assumption (i.e. the truth value of an assertion is not necessarily known). However, the rich relational structure that characterizes ontologies can be exploited for handling such missing knowledge in an explicit way. We present a Statistical Relational Learning system designed for learning terminological naïve Bayesian classifiers, which estimate the probability that a generic individual belongs to the target concept given its membership to a set of Description Logic concepts. During the learning process, we consistently handle the lack of knowledge that may be introduced by the adoption of the Open World Assumption, depending on the varying nature of the missing knowledge itself

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

A graph regularization based approach to transductive class-membership prediction

Considering the increasing availability of structured machine processable knowledge in the context of the Semantic Web, only relying on purely deductive inference may be limiting. This work proposes a new method for similarity-based class-membership prediction in Description Logic knowledge bases. The underlying idea is based on the concept of propagating class-membership information among similar individuals; it is non-parametric in nature and characterised by interesting complexity properties, making it a potential candidate for large-scale transductive inference. We also evaluate its effectiveness with respect to other approaches based on inductive inference in SW literature

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

Message from the ICSC 2012 workshop co-chairs

Welcome to the proceedings containing the papers from two workshops selected for presentation at the Sixth IEEE International Conference on Semantic Computing (ICSC 2012) in Palermo, Italy, September 19–21, 2012

Injecting Background Knowledge into Embedding Models for Predictive Tasks on Knowledge Graphs

Embedding models have been successfully exploited for Knowledge Graph refinement. In these models, the data graph is projected into a low-dimensional space, in which graph structural information are preserved as much as possible, enabling an efficient computation of solutions. We propose a solution for injecting available background knowledge (schema axioms) to further improve the quality of the embeddings. The method has been applied to enhance existing models to produce embeddings that can encode knowledge that is not merely observed but rather derived by reasoning on the available axioms. An experimental evaluation on link prediction and triple classification tasks proves the improvement yielded implementing the proposed method over the original ones

Une phénoménologie de la souffrance enseignante

Cet article présente une réflexion à propos de la souffrance des professeurs des écoles au Brésil. L’examen de ce phénomène a été réalisé à partir de la définition que Paul Ricœur propose de la « souffrance », à l’occasion de sa conférence « La souffrance n’est pas la douleur ». Dans le pays étudié, la souffrance est souvent liée à la précarité de l’exercice du métier d’enseignant : les classes en sureffectifs, les bas salaires, le cumul des postes, les ressources matérielles dégradées et insuffisantes. Nous visons, cependant, à examiner les phénomènes de la souffrance à partir de la condition désignée dans nos recherches comme « précarité symbolique du métier d’enseignant ». De cette condition découle un changement dans la possibilité pour l’enseignant d’exercer ses capacités en tant qu’agent humain, ainsi que dans sa relation avec les autres. C’est donc à partir de ces deux axes que nous proposons une phénoménologie de la souffrance enseignante