Elsevier is an academic publishing company that publishes medical and scientific literature. It is a part of the RELX Group. Based in Amsterdam, the company has operations in the United Kingdom, United States, Mexico, Brazil, Spain, and elsewhere. Wikipedia.
Agency: Cordis | Branch: FP7 | Program: CSA | Phase: ICT-2011.4.4 | Award Amount: 1.17M | Year: 2012
LinkedUp aims to push forward the exploitation of the vast amounts of public, open data available on the Web, in particular by educational institutions and organizations. This will be achieved by identifying and supporting highly innovative large-scale Web information management applications through an open competition (the LinkedUp Challenge) and dedicated evaluation framework. The vision of the LinkedUp Challenge is to realise personalised university degree-level education of global impact based on open Web data and information. Drawing on the diversity of Web information relevant to education, ranging from Open Educational Resources metadata to the vast body of knowledge offered by the Linked Data approach (31 Billion RDF statements as part of the Linked Open Data cloud alone), this aim requires overcoming substantial challenges related to Web-scale data and information management involving Big Data, such as performance and scalability, interoperability, multilinguality and heterogeneity problems, to offer personalised and accessible education services. Therefore, the LinkedUp Challenge provides a focused scenario to derive challenging requirements, evaluation criteria, benchmarks and thresholds which are reflected in the LinkedUp evaluation framework. Information management solutions have to apply data and learning analytics methods to provide highly personalised and context-aware views on heterogeneous Web data. Building on the strong alliance of institutions with expertise in areas such as open Web data management, data integration and Web-based education, key outcomes of LinkedUp include a general-purpose evaluation framework for Web-data driven applications, a set of quality-assured educational datasets, innovative applications of large-scale Web information management, community-building and clustering crossing public and private sectors and substantial technology transfer of highly innovative Web information management technologies.
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: EINFRA-7-2014 | Award Amount: 3.46M | Year: 2015
Five years ago, a global infrastructure to uniquely attribute to researchers their scientific artefacts (articles, data, software) appeared technically and socially infeasible. Since then, DataCite has minted over 3.5m unique identifiers for data. ORCID has deployed an open solution for identification of contributors with over 850,000 registrants in less than 2 years. THOR will leverage these emerging global infrastructures to support the H2020 goal to make every researcher digital and increase creativity and efficiency of research, while bridging the R&D divide between developed and less-developed regions. We will establish interoperability between existing resources, linking digital identifiers across platforms and propagating attribution information. We will integrate PID services across the research lifecycle and data publishing workflows in four advanced research communities, and then roll-out core services and service building blocks for the wider community. These open resources will foster an open and sustainable e-infrastructure across stakeholders to avoid duplications, give economies of scale, richness of services and the ability to respond rapidly to opportunities for innovation. THOR is not just relevant to the EINFRA-7-1024 Call, but will become a pervasive element of the EINFRA family of e-Infrastructure resources over the next 3 years. It will allow data-management and curation services to exploit knowledge of data location and attribution; provide robust and persistent mechanism for linking literature and data; enable search and resolving services and generate incentives for Open Science; deliver provenance and attribution mechanisms to underpin data exchange; and provide minting and resolving services for data citation workflows. Its impact will enable third-party services, no-profit and commercial, to leverage the scholarly record.
Elsevier | Date: 2016-08-25
Elsevier | Date: 2016-07-21
An online document management system is disclosed. In one embodiment, the online document management system comprises: one or more editorial computers operated by one or more administrators or editors, the editorial computers send invitations and manage peer review of document submissions; one or more system computers, the system computers maintain journals, records of submitted documents and user profiles, and issue notifications; and one or more user computers; the user computers submit documents or revisions to the document management system; wherein one or more of the editorial computers coordinate with one or more of the system computers to migrate one or more documents between journals maintained by the online document management system.
Agency: NSF | Branch: Contract | Program: | Phase: SCIENCE & ENGINEERNG INDICATRS | Award Amount: 502.94K | Year: 2015
Elsevier | Date: 2016-02-05
Systems and methods for converting a natural language sentence into a computer-readable primitive sentence and extracting information therefrom are disclosed. A method includes identifying, by a processing device, a verbal block in the natural language sentence, splitting, by the processing device, the natural language sentence into one or more logical clauses, determining, by the processing device, a type for each logical clause, where the type indicates whether each logical clause contains an ambiguous verbal block, disambiguating, by the processing device, the ambiguous verbal block within each logical clause, where each verbal block is considered independently of a noun phrase, and constructing, by the processing device, the computer-readable primitive sentence for each ambiguous verbal block by duplicating a shared noun phrase of the ambiguous verbal block. The computer-readable primitive sentence improves functioning of a computing device by allowing the computing device to process the natural language sentence to obtain information therefrom.
Elsevier | Date: 2014-04-18
Methods for converting a natural language sentence into a set of primitive sentences. The method include identifying verbal blocks in the sentence, splitting the sentence into a set of logical clauses, disambiguating ambiguous verbal blocks within each logical clause, and constructing a primitive sentence for each verbal block by duplicating the shared noun phrases of verbal blocks.
Elsevier | Date: 2016-09-06
Computer-program products and methods for automatically annotating terms, such as ambiguous terms, in an electronic text document are disclosed. In one embodiment, a method of annotating a text document includes determining, by a computing device, a term of interest within the text document. The method further includes searching a data structure including incongruous term pairs (t_(x), tt) determined from a controlled vocabulary for the term of interest appearing as a term tt, wherein the term tt is a linguistic head of a term t_(x )of the incongruous term pairs (t_(x), tt). The method further includes annotating the term of interest with a meaning provided by the controlled vocabulary only if a term t_(x )of the incongruous term pairs (t_(x), tt) associated with the term of interest in the data structure is not present within a predetermined textual distance of the term of interest in the text document.
Elsevier | Date: 2014-04-23
Methods of organizing documents by reclassification and clustering are disclosed. In one embodiment, a method of clustering electronic documents of a document corpus includes comparing, by a computer, each individual electronic document in the document corpus with each other electronic document in the document corpus, thereby forming document pairs. The electronic documents of the document pairs are compared by calculating a similarity value with respect to the electronic documents of a document pair, associating the similarity value with both electronic documents of the document pair, and applying a clustering algorithm to the document corpus using the similarity values to create a plurality of hierarchical clusters. The similarity value is based on a plurality of attributes of the electronic documents in the document corpus. The plurality of attributes includes a citation attribute, a text-based attribute and one or more of an author-attribute, a publication-attribute, an institution-attribute, a downloads-attribute, and a clustering-results-attribute.
Elsevier | Date: 2013-11-14
Systems, computer-program products and methods for annotating electronic text documents with multiple entities defined in a controlled vocabulary extracted from a compound noun phrase are disclosed. In one embodiment, a method of annotating an electronic text document includes searching, by a computing device, the electronic text document for instances of congruent compound noun phrases including a head and a modifier. If a congruent compound noun phrase is found, the method further includes determining a preceding word that precedes the modifier of the congruent compound noun phrase, and searching a controlled vocabulary for a second full term having the preceding word and the head of the congruent compound noun phrase. If the second full term is found in the controlled vocabulary, the method further includes annotating the electronic text document with the second full term having the preceding word and the head of the congruent compound noun phrase.