Inferring ontology graph structures using OWL reasoning.

BACKGROUND: Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge. RESULTS: We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph . CONCLUSIONS: Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

Using data crawlers and semantic Web to build financial XBRL data generators: the SONAR extension approach.

Precise, reliable and real-time financial information is critical for added-value financial services after the economic turmoil from which markets are still struggling to recover. Since the Web has become the most significant data source, intelligent crawlers based on Semantic Technologies have become trailblazers in the search of knowledge combining natural language processing and ontology engineering techniques. In this paper, we present the SONAR extension approach, which will leverage the potential of knowledge representation by extracting, managing, and turning scarce and disperse financial information into well-classified, structured, and widely used XBRL format-oriented knowledge, strongly supported by a proof-of-concept implementation and a thorough evaluation of the benefits of the approach.

Integrating phenotype ontologies with PhenomeNET.

BACKGROUND: Integration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast. RESULTS: Here, we apply the PhenomeNET to identify related classes from two phenotype and two disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone. Combining automated reasoning with lexical matching further improves results in aligning ontologies. CONCLUSIONS: PhenomeNET can be used to align and integrate phenotype ontologies. The results can be utilized for biomedical analyses in which phenomena observed in model organisms are used to identify causative genes and mutations underlying human disease.

Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach.

In recent years, some methods of sentiment analysis have been developed for the health domain; however, the diabetes domain has not been explored yet. In addition, there is a lack of approaches that analyze the positive or negative orientation of each aspect contained in a document (a review, a piece of news, and a tweet, among others). Based on this understanding, we propose an aspect-level sentiment analysis method based on ontologies in the diabetes domain. The sentiment of the aspects is calculated by considering the words around the aspect which are obtained through N-gram methods (N-gram after, N-gram before, and N-gram around). To evaluate the effectiveness of our method, we obtained a corpus from Twitter, which has been manually labelled at aspect level as positive, negative, or neutral. The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an F-measure of 81.24%.

[Quality of life in patients with heart failure: the perspective of primary health care]. / La calidad de vida en los enfermos con insuficiencia cardiaca: visión desde atención primaria de salud.

OBJECTIVE: To measure and identify the dimensions and determinants of health-related quality of life (HRQoL) in patients with chronic heart failure. METHODS: We performed a cross-sectional study, in which HRQoL was measured with the short-form (SF)-36 and the Minnesota Living with Heart Failure Questionnaire (MLHFQ) in 544 clinically-stable patients with chronic heart failure managed by 97 primary care physicians. RESULTS: The mean age of the patients was 77.6 years (SD: 9.9) and was significantly higher in women. A total of 31.2% were in New York Heart Association (NYHA) grade III-IV and 88.6% had at least one chronic condition. In both questionnaires, physical dimensions scored worse than emotional dimensions. After adjustment was made for multiple regression, seven variables entered into one of the five models and explained between 22% and 36% of the variance. CONCLUSIONS: HRQoL in patients with chronic heart failure is impaired across all domains. Being female and being in NYHA functional class III-IV, as well as other factors such as depression, osteoarticular disease, hospital admission, body mass index and age, were associated with poorer self-perceived HRQoL.