Thezoo: un thesaurus de zoologie ancienne et médiévale pour l’annotation de sources de données hétérogènes

International audienceThis paper presents a thesaurus of ancient and medieval zoological knowledge, called THEZOO, constructed in the framework of the International Research Group Zoomathia. It aims at integrating heterogeneous data sources on zoology in Antiquity and Middle Ages: mainly texts, but also images, archaeological objects and archaeozoological material. The development process of THEZOO combines 1) the manual annotation of books VIII-XI of Pliny the Elder’s Natural History, chosen as a reference dataset to elicit the concepts to be integrated in the thesaurus, and 2) the definition and hierarchical organization of the elicited concepts in the thesaurus. THEZOO is formalized in SKOS, the W3C standard to represent knowledge organization systems on the Web of data, and it is created with the Opentheso editor. Our final aim is to publish the thesaurus THEZOO as well as the corpus of annotated texts, to support a semantic search in the corpus in different languages

Towards a Semantic-based Approach for Modeling Regulatory Documents in Building Industry

Regulations in the Building Industry are becoming increasingly complex and involve more than one technical area. They cover products, components and project implementation. They also play an important role to ensure the quality of a building, and to minimize its environmental impact. In this paper, we are particularly interested in the modeling of the regulatory constraints derived from the Technical Guides issued by CSTB and used to validate Technical Assessments. We first describe our approach for modeling regulatory constraints in the SBVR language, and formalizing them in the SPARQL language. Second, we describe how we model the processes of compliance checking described in the CSTB Technical Guides. Third, we show how we implement these processes to assist industrials in drafting Technical Documents in order to acquire a Technical Assessment; a compliance report is automatically generated to explain the compliance or noncompliance of this Technical Documents

Synthetic Large-Scale Galactic Filaments -- on their Formation, Physical Properties, and Resemblance to Observations

Using a population of large-scale filaments extracted from an AREPO simulation of a Milky Way-like galaxy, we seek to understand the extent to which observed large-scale filament properties (with lengths $\gtrsim 100$ pc) can be explained by galactic dynamics alone. From an observer's perspective in the disk of the galaxy, we identify filaments forming purely due to galactic dynamics, without the effects of feedback or local self-gravity. We find that large-scale Galactic filaments are intrinsically rare, and we estimate that at maximum approximately one filament per $\rm kpc^{2}$ should be identified in projection, when viewed from the direction of our Sun in the Milky Way. In this idealized scenario, we find filaments in both the arm and interarm regions, and hypothesize that the former may be due to gas compression in the spiral-potential wells, with the latter due to differential rotation. Using the same analysis pipeline applied previously to observations, we analyze the physical properties of large-scale Galactic filaments, and quantify their sensitivity to projection effects and galactic environment (i.e. whether they lie in the arm or interarm regions). We find that observed "Giant Molecular Filaments" are consistent with being non-self-gravitating structures dominated by galactic dynamics. Straighter, narrower, and denser "Bone-like" filaments, like the paradigmatic Nessie filament, have similar column densities, velocity gradients, and Galactic plane heights ($z\approx$ 0 pc) to those in our simple model, but additional physical effects (such as feedback and self-gravity) must be invoked to explain their lengths and widths.Comment: Accepted for publication in The Astrophysical Journa

Mapping-based SPARQL access to a MongoDB database

Accessing legacy data as virtual RDF stores is a key issue in the building of the Web of Data. In recent years, the MongoDB database has become a leader in the NoSQL market and the management of very large datasets, making it a significant potential contributor to the Web of Linked Data. Therefore, in this paper we address the research question of how to access arbitrary MongoDB documents with SPARQL.We propose a two-step method to (i) translate a SPARQL query into a pivot abstract query under MongoDB-to-RDF mappings represented in the xR2RML language, then (ii) translate the pivot query into a concrete MongoDB query. We elaborate on the discrepancy between the expressiveness of SPARQL and the MongoDB query language, and we show that we can always come up with a rewriting that shall produce all certain answers

A Generic RDF Transformation Software and its Application to an Online Translation Service for Common Languages of Linked Data

International audienceIn this article we present a generic template and software solution for developers to support the many cases where we need to transform RDF. It relies on the SPARQL Template Transformation Language (STTL) which enables Semantic Web developers to write specific yet compact RDF transformers toward other languages and formats. We first briefly recall the STTL principles and software features. We then demonstrate the support it provides to programmers by presenting a selection of STTL-based RDF transformers for common languages. The software is available online as a Web service and all the RDF transformers presented in this paper can be tested online

A survey of RDB to RDF translation approaches and tools

ISRN I3S/RR 2013-04-FR 24 pagesRelational databases scattered over the web are generally opaque to regular web crawling tools. To address this concern, many RDB-to-RDF approaches have been proposed over the last years. In this paper, we propose a detailed review of seventeen RDB-to-RDF initiatives, considering end-to-end projects that delivered operational tools. The different tools are classified along three major axes: mapping description language, mapping implementation and data retrieval method. We analyse the motivations, commonalities and differences between existing approaches. The expressiveness of existing mapping languages is not always sufficient to produce semantically rich data and make it usable, interoperable and linkable. We therefore briefly present various strategies investigated in the literature to produce additional knowledge. Finally, we show that R2RML, the W3C recommendation for describing RDB to RDF mappings, may not apply to all needs in the wide scope of RDB to RDF translation applications, leaving space for future extensions

Personalizing and Improving Resource Recommendation by Analyzing Users Preferences in Social Tagging Activities

Collaborative tagging which is the keystone of the social practices of web 2.0 has been highly developed in the last few years. In this paper, we propose a new method to analyze user profiles according to their tagging activity in order to improve resource recommendation. We base upon association rules which is a powerful method to discover interesting relationships among large datasets on the web. Focusing on association rules we can find correlations between tags in a social network. Our aim is to recommend resources annotated with tags suggested by association rules, in order to enrich user profiles. The effectiveness of the recommendation depends on the resolution of social tagging drawbacks. In our recommender process, we demonstrate how we can reduce tag ambiguity and spelling variations problems by taking into account social similarities calculated on folksonomies, in order to personalize resource recommendation. We surmount also the lack of semantic links between tags during the recommendation process. Experiments are carried out with two different scenarios: the first one is a proof of concept over two baseline datasets and the second one is a real world application for diabetes disease