Narrow Results

Research Information Systems (RIS) play a critical role in the sharing of scientific information and provide researchers, professionals and decision makers with the required data for their activities. Existing RIS standards have proposed data models to represent the main entities for storage and exchange. These account for the needs of multiple stakeholders through a high flexibility based on a formal syntax and declared semantics, but for techno-historical reasons they assume the completeness of information within system boundaries. The distributed nature of research information across systems calls for a mechanism to link the local entities from the closed world of concrete RISs with other possibly underspecified entities exposed through other means, as for example, the Linked Open Data Web. By transformation of a relational model into an open graph model, differences between the two system paradigms are revealed. The main principles and techniques for exposing CERIF-driven relational data as linked data will be provided as a first step demonstrating effective RISs interconnection through the linked open data (LOD) Web.

This paper aims to describe a public procurement information platform which provides a unified pan-European system that exploits the aggregation of tender notices using linking open data and semantic web technologies. This platform requires a step-based method to deal with the requirements of the public procurement sector and the open government data initiative: (1) modeling the unstructured information included in public procurement notices (contracting authorities, organizations, contracts awarded, etc.); (2) enriching that information with the existing product classification systems and the linked data vocabularies; (3) publishing relevant information extracted out of the notices following the linking open data approach; (4) implementing enhanced services based on advanced algorithms and techniques like query expansion methods to exploit the information in a semantic way. Taking into account that public procurement notices contain different kinds of data like types of contract, region, duration, total amount, target enterprise, etc., various methods can be applied to expand user queries easing the access to the information and providing a more accurate information retrieval system. Nevertheless expanded user queries can involve an extra-time in the process of retrieving notices. That is why a performance evaluation is outlined to tune up the semantic methods and the generated queries providing a scalable and time-efficient system. Moreover, this platform is supposed to be especially relevant for SMEs that want to tender in the European Union (EU), easing their access to the information of the notices and fostering their participation in cross-border public procurement processes across Europe. Finally an example of use is provided to evaluate and compare the goodness and the improvement of the proposed platform with regard to the existing ones.

"The new source of power is not money in the hands of a few, but information in the hands of many." The aforementioned quote from John Naisbitt seems to be even more relevant in the world of finance at this very moment. Many financial decisions come from watching the information stream, selecting relevant data, analyzing it and acting accordingly. With the increasing global competition, the need for swift data analysis, high accuracy and quality becomes a must. XBRL (Extensible Business Reporting Language)XBRL: http://www.xbrl.org/ standard was proposed to improve efficiency of data exchange in the financial domain. However; it is still struggling with interoperability problems, not to mention comparability of data or multisource data integration. This paper presents the FLORA intelligent platform: an approach for dealing with current financial information shortcomings and achieving more effective way of processing financial data based on the Linked Data principles. The article also explains the process of data extraction and semantic modeling which are the cornerstones of efficient financial data analysis. As a result, the FLORA architecture facilitates effective, data-driven, financial analyses and Web-scale integration between financial applications and platforms.

Linking named entities to structured knowledge sources paves the way for state-of-the-art Web intelligence applications which assign sentiment to the correct entities, identify trends, and reveal relations between organizations, persons and products. For this purpose this paper introduces Recognyze, a named entity linking component that uses background knowledge obtained from linked data repositories, and outlines the process of transforming heterogeneous data silos within an organization into a linked enterprise data repository which draws upon popular linked open data vocabularies to foster interoperability with public data sets. The presented examples use comprehensive real-world data sets from Orell Füssli Business Information, Switzerland's largest business information provider. The linked data repository created from these data sets comprises more than nine million triples on companies, the companies' contact information, key people, products and brands. We identify the major challenges of tapping into such sources for named entity linking, and describe required data pre-processing techniques to use and integrate such data sets, with a special focus on disambiguation and ranking algorithms. Finally, we conduct a comprehensive evaluation based on business news from the New Journal of Zurich and AWP Financial News to illustrate how these techniques improve the performance of the Recognyze named entity linking component.

Several approaches have been proposed in the literature for offering RDF views over databases. In addition to these, a variety of tools exist that allow exporting database contents into RDF graphs. The approaches in the latter category have often been proved demonstrating better performance than the ones in the former. However, when database contents are exported into RDF, it is not always optimal or even necessary to export, or dump as this procedure is often called, the whole database contents every time. This paper investigates the problem of incremental generation and storage of the RDF graph that is the result of exporting relational database contents. In order to express mappings that associate tuples from the source database to triples in the resulting RDF graph, an implementation of the R2RML standard is subject to testing. Next, a methodology is proposed and described that enables incremental generation and storage of the RDF graph that originates from the source relational database contents. The performance of this methodology is assessed, through an extensive set of measurements. The paper concludes with a discussion regarding the authors' most important findings.

Domain-specific ontologies have become integral components of numerous semantic- and knowledge-based applications. However, creating such ontologies and populating them with correct individuals is a difficult and time-consuming process. Recently, a vast amount of knowledge has become available as part of the Linked Open Data (LOD) project, which includes data sets in multiple areas. In this paper, we present mOntage, a novel ontology design and population framework, which allows a domain expert to easily define a domain ontology schema and specify the ontology's classes and properties in terms of the subsets of the existing LOD data sources. The classes and properties of the ontology being created can be defined either directly, in terms of existing LOD-available classes and properties, or can be newly constructed by the domain expert. The definitions, called maps, are encoded as part of the ontology itself, effectively converting it into a self-populating ontology. Finally, a dedicated software system automatically populates the ontology with instances obtained from the selected LOD sources by executing suitable SPARQL queries. We illustrate our framework by creating Cancer Treatment ontology in the area of biomedicine.

Online media might be a way to introduce as well as preserve cultural heritage. Related to Indonesian culture, there are publication services available which focus only on information sources without metadata properties. This article explains our effort to preserve the Indonesian cultural heritage using semantic web and linked open data for a comprehensive presentation. An OWL-based ontology has been trying to be developed for a repository application consists of 13 ontologies of Indonesian cultural objects based on Description Logic of manually extracted literatures. The development process involved important partners in Special Region of Yogyakarta, i.e. Department of Culture, Regional Library and Archives Agency, Indonesian University Library Forum, as well as cultural practitioners and researchers. The applications of cultural objects are introduced at the end of this article. It is a baby step of a giant dream.