Measuring Accuracy of Triples in Knowledge Graphs

An increasing amount of large-scale knowledge graphs have been constructed in recent years. Those graphs are often created from text-based extraction, which could be very noisy. So far, cleaning knowledge graphs are often carried out by human experts and thus very inefficient. It is necessary to explore automatic methods for identifying and eliminating erroneous information. In order to achieve this, previous approaches primarily rely on internal information i.e. the knowledge graph itself. In this paper, we introduce an automatic approach, Triples Accuracy Assessment (TAA), for validating RDF triples (source triples) in a knowledge graph by finding consensus of matched triples (among target triples) from other knowledge graphs. TAA uses knowledge graph interlinks to find identical resources and apply different matching methods between the predicates of source triples and target triples. Then based on the matched triples, TAA calculates a confidence score to indicate the correctness of a source triple. In addition, we present an evaluation of our approach using the FactBench dataset for fact validation. Our findings show promising results for distinguishing between correct and wrong triples

Оценка эффективности управления деятельностью предприятия

Целью исследования является оценка эффективности управления деятельностью предприятия как интегрального показателя, то есть управления совокупностью деятельностей, таких как производственная, инвестиционная, инновационная, маркетинговая и финансовая

Canonicalizing Knowledge Base Literals

Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching

Effects of aripiprazole once-monthly on domains of personal and social performance: Results from 2 multicenter, randomized, double-blind studies

Objective: To assess the effects of maintenance therapy with aripiprazole once-monthly 400 mg on personal and social functioning. Methods: Data were analyzed from 2 randomized, double-blind trials of patients with schizophrenia requiring chronic antipsychotic treatment. One study was a 52-week trial of aripiprazole once-monthly 400 mg versus placebo; the other was a 38-week trial of aripiprazole once-monthly 400 mg, oral aripiprazole (10-30 mg daily), and aripiprazole once-monthly 50 mg (subtherapeutic dose to test assay sensitivity). Functioning was assessed using the Personal and Social Performance (PSP) scale, comprising 4 domain subscales. Results: In the 52-week study, 403 patients stabilized on aripiprazole once-monthly 400 mg were randomized to receive aripiprazole once-monthly 400 mg (n=269) or placebo (n=134). In the 38-week study, 662 patients stabilized on oral aripiprazole were randomized to receive aripiprazole once-monthly 400 mg (n=265), oral aripiprazole (n=266), or aripiprazole once-monthly 50 mg (subtherapeutic dose; n=131). In the 52-week study, mean changes from baseline were significantly worsened with placebo compared with aripiprazole once-monthly 400 mg for PSP total score (

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

Biochemical properties of Paracoccus denitrificans FnrP:Reactions with molecular oxygen and nitric oxide

In Paracoccus denitrificans, three CRP/FNR family regulatory proteins, NarR, NnrR and FnrP, control the switch between aerobic and anaerobic (denitrification) respiration. FnrP is a [4Fe-4S] cluster containing homologue of the archetypal O2 sensor FNR from E. coli and accordingly regulates genes encoding aerobic and anaerobic respiratory enzymes in response to O2, and also NO, availability. Here we show that FnrP undergoes O2-driven [4Fe-4S] to [2Fe-2S] cluster conversion that involves up to 2 O2 per cluster, with significant oxidation of released cluster sulfide to sulfane observed at higher O2 concentrations. The rate of the cluster reaction was found to be ~6-fold lower than that of E. coli FNR, suggesting that FnrP can remain transcriptionally active under microaerobic conditions. This is consistent with a role for FnrP in activating expression of the high O2 affinity cytochrome c oxidase under microaerobic conditions. Cluster conversion resulted in dissociation of the transcriptionally active FnrP dimer into monomers. Therefore, along with E. coli FNR, FnrP belongs to the subset of FNR proteins in which cluster type is correlated with association state. Interestingly, two key charged residues, Arg140 and Asp154, that have been shown to play key roles in the monomer-dimer equilibrium in E. coli FNR are not conserved in FnrP, indicating that different protomer interactions are important for this equilibrium. Finally, the FnrP [4Fe-4S] cluster is shown to undergo reaction with multiple NO molecules, resulting in iron nitrosyl species and dissociation into monomers

Care for patients with severe mental illness: the general practitioner's role perspective

BACKGROUND: Patients with severe mental illness (SMI) experience distress and disabilities in several aspects of life, and they have a higher risk of somatic co-morbidity. Both patients and their family members need the support of an easily accessible primary care system. The willingness of general practitioners and the impeding factors for them to participate in providing care for patients with severe mental illness in the acute and the chronic or residual phase were explored. METHODS: A questionnaire survey of a sample of Dutch general practitioners spread over the Netherlands was carried out. This comprised 20 questions on the GP's 'Opinion and Task Perspective', 19 questions on 'Treatment and Experiences', and 27 questions on 'Characteristics of the General Practitioner and the Practice Organisation'. RESULTS: 186 general practitioners distributed over urban areas (49%), urbanised rural areas (38%) and rural areas (15%) of the Netherlands participated. The findings were as follows: GPs currently considered themselves as the first contact in the acute psychotic phase. In the chronic or residual phase GPs saw their core task as to diagnose and treat somatic co-morbidity. A majority would be willing to monitor the general health of these patients as well. It appeared that GP trainers and GPs with a smaller practice setting made follow-up appointments and were willing to monitor the self-care of patients with SMI more often than GPs with larger practices.GPs also saw their role as giving support and information to the patient's family.However, they felt a need for recognition of their competencies when working with mental health care specialists. CONCLUSION: GPs were willing to participate in providing care for patients with SMI. They considered themselves responsible for psychotic emergency cases, for monitoring physical health in the chronic phase, and for supporting the relatives of psychotic patients

Predictors of switching antipsychotic medications in the treatment of schizophrenia

<p>Abstract</p> <p>Background</p> <p>To identify patient characteristics and early changes in patients' clinical status that best predict subsequent switching of antipsychotic agents in the long-term treatment of schizophrenia.</p> <p>Methods</p> <p>This post-hoc analysis used data from a one-year randomized, open-label, multisite study of antipsychotics in the treatment of schizophrenia. The study protocol permitted switching of antipsychotics when clinically warranted after the first eight weeks. Baseline patient characteristics were assessed using standard psychiatric measures and reviews of medical records. The prediction model included baseline sociodemographics, comorbid psychiatric and non-psychiatric conditions, body weight, clinical and functional variables, as well as change scores on standard efficacy and tolerability measures during the first two weeks of treatment. Cox proportional hazards modeling was used to identify the best predictors of switching from the initially assigned antipsychotic medication.</p> <p>Results</p> <p>About one-third of patients (29.5%, 191/648) switched antipsychotics before the end of the one-year study. There were six variables identified as the best predictors of switching: lack of antipsychotic use in the prior year, pre-existing depression, female gender, lack of substance use disorder, worsening of akathisia (as measured by the Barnes Akathisia Scale), and worsening of symptoms of depression/anxiety (subscale score on the Positive and Negative Syndrome Scale) during the first two weeks of antipsychotic therapy.</p> <p>Conclusions</p> <p>Switching antipsychotics appears to be prevalent in the naturalistic treatment of schizophrenia and can be predicted by a small and distinct set of variables. Interestingly, worsening of anxiety and depressive symptoms and of akathisia following two weeks of treatment were among the more robust predictors of subsequent switching of antipsychotics.</p