Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

Text mining meets community curation: a newly designed curation platform to improve author experience and participation at WormBase

Biological knowledgebases rely on expert biocuration of the research literature to maintain up-to-date collections of data organized in machine-readable form. To enter information into knowledgebases, curators need to follow three steps: (i) identify papers containing relevant data, a process called triaging; (ii) recognize named entities; and (iii) extract and curate data in accordance with the underlying data models. WormBase (WB), the authoritative repository for research data on Caenorhabditis elegans and other nematodes, uses text mining (TM) to semi-automate its curation pipeline. In addition, WB engages its community, via an Author First Pass (AFP) system, to help recognize entities and classify data types in their recently published papers. In this paper, we present a new WB AFP system that combines TM and AFP into a single application to enhance community curation. The system employs string-searching algorithms and statistical methods (e.g. support vector machines (SVMs)) to extract biological entities and classify data types, and it presents the results to authors in a web form where they validate the extracted information, rather than enter it de novo as the previous form required. With this new system, we lessen the burden for authors, while at the same time receive valuable feedback on the performance of our TM tools. The new user interface also links out to specific structured data submission forms, e.g. for phenotype or expression pattern data, giving the authors the opportunity to contribute a more detailed curation that can be incorporated into WB with minimal curator review. Our approach is generalizable and could be applied to additional knowledgebases that would like to engage their user community in assisting with the curation. In the five months succeeding the launch of the new system, the response rate has been comparable with that of the previous AFP version, but the quality and quantity of the data received has greatly improved

A new knowledge sourcing framework for knowledge-based engineering: an aerospace industry case study

New trends in Knowledge-Based Engineering (KBE) highlight the need for decoupling the automation aspect from the knowledge management side of KBE. In this direction, some authors argue that KBE is capable of effectively capturing, retaining and reusing engineering knowledge. However, there are some limitations associated with some aspects of KBE that present a barrier to deliver the knowledge sourcing process requested by industry. To overcome some of these limitations this research proposes a new methodology for efficient knowledge capture and effective management of the complete knowledge life cycle. The methodology proposed in this research is validated through the development and implementation of a case study involving the optimisation of wing design concepts at an Aerospace manufacturer. The results obtained proved the extended KBE capability for fast and effective knowledge sourcing. This evidence was provided by the experts working in the development of the case study through the implementation of structured quantitative and qualitative analyses

A Formal Architecture-Centric Model-Driven Approach for the Automatic Generation of Grid Applications

This paper discusses the concept of model-driven software engineering applied to the Grid application domain. As an extension to this concept, the approach described here, attempts to combine both formal architecture-centric and model-driven paradigms. It is a commonly recognized statement that Grid systems have seldom been designed using formal techniques although from past experience such techniques have shown advantages. This paper advocates a formal engineering approach to Grid system developments in an effort to contribute to the rigorous development of Grids software architectures. This approach addresses quality of service and cross-platform developments by applying the model-driven paradigm to a formal architecture-centric engineering method. This combination benefits from a formal semantic description power in addition to model-based transformations. The result of such a novel combined concept promotes the re-use of design models and facilitates developments in Grid computing.Comment: 11 pages, 9 figures. Proc of the 8th International Conference on Enterprise Information Systems (ICEIS06) Paphos, Cyprus. May 200

Report of the Stanford Linked Data Workshop

The Stanford University Libraries and Academic Information Resources (SULAIR) with the Council on Library and Information Resources (CLIR) conducted at week-long workshop on the prospects for a large scale, multi-national, multi-institutional prototype of a Linked Data environment for discovery of and navigation among the rapidly, chaotically expanding array of academic information resources. As preparation for the workshop, CLIR sponsored a survey by Jerry Persons, Chief Information Architect emeritus of SULAIR that was published originally for workshop participants as background to the workshop and is now publicly available. The original intention of the workshop was to devise a plan for such a prototype. However, such was the diversity of knowledge, experience, and views of the potential of Linked Data approaches that the workshop participants turned to two more fundamental goals: building common understanding and enthusiasm on the one hand and identifying opportunities and challenges to be confronted in the preparation of the intended prototype and its operation on the other. In pursuit of those objectives, the workshop participants produced:1. a value statement addressing the question of why a Linked Data approach is worth prototyping;2. a manifesto for Linked Libraries (and Museums and Archives and …);3. an outline of the phases in a life cycle of Linked Data approaches;4. a prioritized list of known issues in generating, harvesting & using Linked Data;5. a workflow with notes for converting library bibliographic records and other academic metadata to URIs;6. examples of potential “killer apps” using Linked Data: and7. a list of next steps and potential projects.This report includes a summary of the workshop agenda, a chart showing the use of Linked Data in cultural heritage venues, and short biographies and statements from each of the participants

White Paper on implementing the FAIR principles for data in the social, behavioural, and economic sciences

The FAIR principles formulate guidelines for the sustainable reusability of research data. FAIR stands for Findability, Accessibility, Interoperability, and Reusability of data and metadata. While there is a growing body of general implementation guidelines, so far there is a lack of specific recommendations on how to apply the FAIR principles to the specific needs of social, behavioural and economic science data. These disciplines work with highly diverse data types that often contain confidential information on individuals, companies, or institutions. These features pose some challenges to the useful implementation of the FAIR principles - especially regarding the machine-actionability of data and metadata that is at the core of the FAIR principles. This White Paper defines the FAIR principles for the social, behavioural and economic sciences. For each of the 15 FAIR (sub)principles, the paper proposes minimum requirements and provides a vision for a full-implementation of the FAIR principles by repositories and data centres. The paper was authored by members of the Economic and Social Sciences goINg FAIR Implementation Network (EcoSoc-IN) and addresses research data centres and other stakeholders who strive for a FAIR research data infrastructure in the disciplines of KonsortSWD