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

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

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

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

Papular-purpuric gloves and socks syndrome due to parvovirus B19: a report of two simultaneous cases in cohabitant families.

The so-called papular-purpuric gloves and socks syndrome (PPGSS) is a condition characterized by acute onset of intense erythema, edema and petechiae with a typical localization on the hands and feet, besides mucosal lesions of the oral cavity. The syndrome has a favorable and self-limited course, requiring only a symptomatic therapy. In the 50% of the cases described in literature (ninety cases in 22 years), is documented an acute infection caused by parvovirus B19 and in only two cases the onset of PPGSS is reported among different members of the same family. The aim of the work is to describe two cases of PPGSS arisen during a short time period in two family members affected by an acute parvovirus B19 infection found by serum sampling. The peculiarity of the study was the infrequence of the syndrome and the rareness of the description of PPGSS in rheumatology. This syndrome is usually described in dermatology, but it is also interesting for the rheumatologist because it comes in differential diagnosis with various autoimmune diseases

1H-NMR metabolite fingerprinting analysis reveals a disease biomarker and a field treatment response in xylella fastidiosa subsp. Pauca-infected olive trees

Xylella fastidiosa subsp. pauca is a xylem-limited bacterial phytopathogen currently found associated on many hectares with the “olive quick decline syndrome” in the Apulia region (Southern Italy), and the cultivars Ogliarola salentina and Cellina di Nardò result in being particularly sensitive to the disease. In order to find compounds showing the capability of reducing the population cell density of the pathogen within the leaves, we tested, in some olive orchards naturally-infected by the bacterium, a zinc-copper-citric acid biocomplex, namely Dentamet®, by spraying it to the crown, once per month, during spring and summer. The occurrence of the pathogen in the four olive orchards chosen for the trial was molecularly assessed. A 1H NMR metabolomic approach, in conjunction with a multivariate statistical analysis, was applied to investigate the metabolic pattern of both infected and treated adult olive cultivars, Ogliarola salentina and Cellina di Nardò trees, in two sampling periods, performed during the first year of the trial. For both cultivars and sampling periods, the orthogonal partial least squares discriminant analysis (OPLS-DA) gave good models of separation according to the treatment application. In both cultivars, some metabolites such as quinic acid, the aldehydic form of oleoeuropein, ligstroside and phenolic compounds, were consistently found as discriminative for the untreated olive trees in comparison with the Dentamet®-treated trees. Quinic acid, a precursor of lignin, was confirmed as a disease biomarker for the olive trees infected by X. fastidiosa subsp. pauca. When treated with Dentamet®, the two cultivars showed a distinct response. A consistent increase in malic acid was observed for the Ogliarola salentina trees, whereas in the Cellina di Nardò trees the treatments attenuate the metabolic response to the infection. To note that in Cellina di Nardò trees at the first sampling, an increase in γ-aminobutyric acid (GABA) was observed. This study highlights how the infection incited by X. fastidiosa subsp. pauca strongly modifies the overall metabolism of olive trees, and how a zinc-copper-citric acid biocomplex can induce an early re-programming of the metabolic pathways in the infected trees

A roadmap towards breast cancer therapies supported by explainable artificial intelligence

In recent years personalized medicine reached an increasing importance, especially in the design of oncological therapies. In particular, the development of patients’ profiling strategies suggests the possibility of promising rewards. In this work, we present an explainable artificial intelligence (XAI) framework based on an adaptive dimensional reduction which (i) outlines the most important clinical features for oncological patients’ profiling and (ii), based on these features, determines the profile, i.e., the cluster a patient belongs to. For these purposes, we collected a cohort of 267 breast cancer patients. The adopted dimensional reduction method determines the relevant subspace where distances among patients are used by a hierarchical clustering procedure to identify the corresponding optimal categories. Our results demonstrate how the molecular subtype is the most important feature for clustering. Then, we assessed the robustness of current therapies and guidelines; our findings show a striking correspondence between available patients’ profiles determined in an unsupervised way and either molecular subtypes or therapies chosen according to guidelines, which guarantees the interpretability characterizing explainable approaches to machine learning techniques. Accordingly, our work suggests the possibility to design data-driven therapies to emphasize the differences observed among the patients