RDF Knowledge Graph Visualization From a Knowledge Extraction System

In this paper, we present a system to visualize RDF knowledge graphs. These graphs are obtained from a knowledge extraction system designed by GEOLSemantics. This extraction is performed using natural language processing and trigger detection. The user can visualize subgraphs by selecting some ontology features like concepts or individuals. The system is also multilingual, with the use of the annotated ontology in English, French, Arabic and Chinese

Slow Radiation-Driven Wind Solutions of A-Type Supergiants

The theory of radiation-driven winds succeeded in describing terminal velocities and mass loss rates of massive stars. However, for A-type supergiants the standard m-CAK solution predicts values of mass loss and terminal velocity higher than the observed values. Based on the existence of a slow wind solution in fast rotating massive stars, we explore numerically the parameter space of radiation-driven flows to search for new wind solutions in slowly rotating stars, that could explain the origin of these discrepancies. We solve the 1-D hydrodynamical equation of rotating radiation-driven winds at different stellar latitudes and explore the influence of ionization's changes throughout the wind in the velocity profile. We have found that for particular sets of stellar and line-force parameters, a new slow solution exists over the entire star when the rotational speed is slow or even zero. In the case of slow rotating A-type supergiant stars the presence of this novel slow solution at all latitudes leads to mass losses and wind terminal velocities which are in agreement with the observed values. The theoretical Wind Momentum-Luminosity Relationship derived with these slow solutions shows very good agreement with the empirical relationship. In addition, the ratio between the terminal and escape velocities, which provides a simple way to predict stellar wind energy and momentum input into the interstellar medium, is also properly traced.Comment: 7 Pages, 3 figures, Astrophysical Journal, Accepte

Improving prediction performance of stellar parameters using functional models

This paper investigates the problem of prediction of stellar parameters, based on the star's electromagnetic spectrum. The knowledge of these parameters permits to infer on the evolutionary state of the star. From a statistical point of view, the spectra of different stars can be represented as functional data. Therefore, a two-step procedure decomposing the spectra in a functional basis combined with a regression method of prediction is proposed. We also use a bootstrap methodology to build prediction intervals for the stellar parameters. A practical application is also provided to illustrate the numerical performance of our approach

Limb-Darkened Radiation-Driven Winds from Massive Stars

We calculated the influence of the limb-darkened finite disk correction factor in the theory of radiation-driven winds from massive stars. We solved the 1-D m-CAK hydrodynamical equation of rotating radiation-driven winds for all three known solutions, i.e., fast, \Omega-slow and \delta-slow. We found that for the fast solution, the mass loss rate is increased by a factor \sim 10%, while the terminal velocity is reduced about 10%, when compared with the solution using a finite disk correction factor from a uniformly bright star. For the other two slow solutions the changes are almost negligible. Although, we found that the limb darkening has no effects on the wind momentum luminosity relationship, it would affect the calculation of synthetic line profiles and the derivation of accurate wind parameters.Comment: Accepted for publication in ApJ. 19 pages, 6 figure

LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs

The number of linked data sources and the size of the linked open data graph keep growing every day. As a consequence, semantic RDF services are more and more confronted with various "big data" problems. Query processing in the presence of inferences is one them. For instance, to complete the answer set of SPARQL queries, RDF database systems evaluate semantic RDFS relationships (subPropertyOf, subClassOf) through time-consuming query rewriting algorithms or space-consuming data materialization solutions. To reduce the memory footprint and ease the exchange of large datasets, these systems generally apply a dictionary approach for compressing triple data sizes by replacing resource identifiers (IRIs), blank nodes and literals with integer values. In this article, we present a structured resource identification scheme using a clever encoding of concepts and property hierarchies for efficiently evaluating the main common RDFS entailment rules while minimizing triple materialization and query rewriting. We will show how this encoding can be computed by a scalable parallel algorithm and directly be implemented over the Apache Spark framework. The efficiency of our encoding scheme is emphasized by an evaluation conducted over both synthetic and real world datasets.Comment: 8 pages, 1 figur