A Semantic Question Answering Framework for Large Data Sets

Traditionally, the task of answering natural language questions has involved a keyword-based document retrieval step, followed by in-depth processing of candidate answer documents and paragraphs. This post-processing uses semantics to various degrees. In this article, we describe a purely semantic question answering (QA) framework for large document collections. Our high-precision approach transforms the semantic knowledge extracted from natural language texts into a language-agnostic RDF representation and indexes it into a scalable triplestore. In order to facilitate easy access to the information stored in the RDF semantic index, a user's natural language questions are translated into SPARQL queries that return precise answers back to the user. The robustness of this framework is ensured by the natural language reasoning performed on the RDF store, by the query relaxation procedures, and the answer ranking techniques. The improvements in performance over a regular free text search index-based question answering engine prove that QA systems can benefit greatly from the addition and consumption of deep semantic information

Ten Ways of Leveraging Ontologies for Rapid Natural Language Processing Customization for Multiple Use Cases in Disjoint Domains

With the ever-growing adoption of AI technologies by large enterprises, purely data-driven approaches have dominated the field in the recent years. For a single use case, a development process looks simple: agreeing on an annotation schema, labeling the data, and training the models. As the number of use cases and their complexity increases, the development teams face issues with collective governance of the models, scalability and reusablity of data and models. These issues are widely addressed on the engineering side, but not so much on the knowledge side. Ontologies have been a well-researched approach for capturing knowledge and can be used to augment a data-driven methodology. In this paper, we discuss 10 ways of leveraging ontologies for Natural Language Processing (NLP) and its applications. We use ontologies for rapid customization of a NLP pipeline, ontologyrelated standards to power a rule engine and provide standard output format. We also discuss various use cases for medical, enterprise, financial, legal, and security domains, centered around three NLP-based applications: semantic search, question answering and natural language querying

Sex and age differences and outcomes in acute coronary syndromes

Background: There is conflicting information about sex differences in presentation, treatment, and outcome after acute coronary syndromes (ACS) in the era of reperfusion therapy and percutaneous coronary intervention. The aim of this study was to examine presentation, acute therapy, and outcomes of men and women with ACS with special emphasis on their relationship with younger age ( lt = 65 years). Methods: From January 2010 to June 2015, we enrolled 5140 patients from 3 primary PCI capable hospitals. Patients were registered according to the International Survey of Acute Coronary Syndrome in Transitional Countries (ISACS-TC) registry protocol (ClinicalTrials.gov: NCT01218776). The primary outcome was the incidence of in-hospital mortality. Results: The study population was constituted by 2876 patients younger than 65 years and 2294 patients older. Women were older than men in both the young (56.2 +/- 6.6 vs. 54.1 +/- 7.4) and old (74.9 +/- 6.4 vs. 73.6 +/- 6.0) age groups. There were 3421 (66.2%) patients with ST elevation ACS (STE-ACS) and 1719 (33.8%) patients without ST elevation ACS (NSTE-ACS). In STE-ACS, the percentage of patients who failed to receive reperfusion was higher in women than in men either in the young (21.7% vs. 15.8%) than in the elderly (35.2% vs. 29.6%). There was a significant higher mortality in women in the younger age group (age-adjusted OR 1.52, 95% CI: 1.01-2.29), but there was no sex difference in the older group (age-adjusted OR 1.10, 95% CI: 0.87-1.41). Significantly sex differences in mortality were not seen in NSTE-ACS patients. Conclusions: In-hospital mortality from ACS is not different between older men and women. A higher short-term mortality can be seen only in women with STEMI and age of 65 or less

Genetic landscape of 6089 inherited retinal dystrophies affected cases in Spain and their therapeutic and extended epidemiological implications

Inherited retinal diseases (IRDs), defined by dysfunction or progressive loss of photoreceptors, are disorders characterized by elevated heterogeneity, both at the clinical and genetic levels. Our main goal was to address the genetic landscape of IRD in the largest cohort of Spanish patients reported to date. A retrospective hospital-based cross-sectional study was carried out on 6089 IRD affected individuals (from 4403 unrelated families), referred for genetic testing from all the Spanish autonomous communities. Clinical, demographic and familiar data were collected from each patient, including family pedigree, age of appearance of visual symptoms, presence of any systemic findings and geographical origin. Genetic studies were performed to the 3951 families with available DNA using different molecular techniques. Overall, 53.2% (2100/3951) of the studied families were genetically characterized, and 1549 different likely causative variants in 142 genes were identified. The most common phenotype encountered is retinitis pigmentosa (RP) (55.6% of families, 2447/4403). The most recurrently mutated genes were PRPH2, ABCA4 and RS1 in autosomal dominant (AD), autosomal recessive (AR) and X-linked (XL) NON-RP cases, respectively; RHO, USH2A and RPGR in AD, AR and XL for non-syndromic RP; and USH2A and MYO7A in syndromic IRD. Pathogenic variants c.3386G > T (p.Arg1129Leu) in ABCA4 and c.2276G > T (p.Cys759Phe) in USH2A were the most frequent variants identified. Our study provides the general landscape for IRD in Spain, reporting the largest cohort ever presented. Our results have important implications for genetic diagnosis, counselling and new therapeutic strategies to both the Spanish population and other related populations.This work was supported by the Instituto de Salud Carlos III (ISCIII) of the Spanish Ministry of Health (FIS; PI16/00425 and PI19/00321), Centro de Investigación Biomédica en Red Enfermedades Raras (CIBERER, 06/07/0036), IIS-FJD BioBank (PT13/0010/0012), Comunidad de Madrid (CAM, RAREGenomics Project, B2017/BMD-3721), European Regional Development Fund (FEDER), the Organización Nacional de Ciegos Españoles (ONCE), Fundación Ramón Areces, Fundación Conchita Rábago and the University Chair UAM-IIS-FJD of Genomic Medicine. Irene Perea-Romero is supported by a PhD fellowship from the predoctoral Program from ISCIII (FI17/00192). Ionut F. Iancu is supported by a grant from the Comunidad de Madrid (CAM, PEJ-2017-AI/BMD7256). Marta del Pozo-Valero is supported by a PhD grant from the Fundación Conchita Rábago. Berta Almoguera is supported by a Juan Rodes program from ISCIII (JR17/00020). Pablo Minguez is supported by a Miguel Servet program from ISCIII (CP16/00116). Marta Corton is supported by a Miguel Servet program from ISCIII (CPII17/00006). The funders played no role in study design, data collection, data analysis, manuscript preparation and/or publication decisions

A Semantic Approach to Recognizing Textual Entailment

Exhaustive extraction of semantic information from text is one of the formidable goals of state-of-the-art NLP systems. In this paper, we take a step closer to this objective. We combine the semantic information provided by different resources and extract new semantic knowledge to improve the performance of a recognizing textual entailment system. 1 Recognizing Textual Entailment While communicating, humans use different expressions to convey the same meaning. Therefore, numerous NLP applications, such as, Question Answering, Information Extraction, or Summarization require computational models of language that recognize if two texts semantically overlap. Trying to capture the major inferences needed to understand equivalent semantic expressions, the PASCAL Network proposed the Recognizing Textual Entailment (RTE) challenge (Dagan et al., 2005). Given two text fragments, the task is to determine if the meaning of one text (the entailed hypothesis, H) can be inferred from the meaning of the other text (the entailing text, T). Given the wide applicability of this task, there is an increased interest in creating systems which detect the semantic entailment between two texts. The systems that participated in the Pascal RTE challenge competition exploit various inference elements which, later, they combine within statistical models, scoring methods, or machine learning frameworks. Several systems (Bos and Markert, 2005; Herrera et al., 2005; Jijkoun and de Rijke, 2005; Kouylekov and Magnini, 2005; Newman et al., 2005) measured the word overlap between the two text strings. Using either statistical or Word-Net’s relations, almost all systems considered lexical relationships that indicate entailment. The degree of similarity between the syntactic parse trees of the two texts was also used as a clue for entailment by several systems (Herrera et al., 2005; Kouylekov and Magnini, 2005; de Salvo Braz et al., 2005; Raina et al., 2005). Several groups used logic provers to show the entailment between T and H (Bayer e

Models for the Semantic Classification of Noun Phrases

This paper presents an approach for detecting semantic relations in noun phrases. A learning algorithm, called semantic scattering, is used to automatically label complex nominals, genitives and adjectival noun phrases with the corresponding semantic relation