43,134 research outputs found
PAV ontology: provenance, authoring and versioning
Provenance is a critical ingredient for establishing trust of published
scientific content. This is true whether we are considering a data set, a
computational workflow, a peer-reviewed publication or a simple scientific
claim with supportive evidence. Existing vocabularies such as DC Terms and the
W3C PROV-O are domain-independent and general-purpose and they allow and
encourage for extensions to cover more specific needs. We identify the specific
need for identifying or distinguishing between the various roles assumed by
agents manipulating digital artifacts, such as author, contributor and curator.
We present the Provenance, Authoring and Versioning ontology (PAV): a
lightweight ontology for capturing just enough descriptions essential for
tracking the provenance, authoring and versioning of web resources. We argue
that such descriptions are essential for digital scientific content. PAV
distinguishes between contributors, authors and curators of content and
creators of representations in addition to the provenance of originating
resources that have been accessed, transformed and consumed. We explore five
projects (and communities) that have adopted PAV illustrating their usage
through concrete examples. Moreover, we present mappings that show how PAV
extends the PROV-O ontology to support broader interoperability.
The authors strived to keep PAV lightweight and compact by including only
those terms that have demonstrated to be pragmatically useful in existing
applications, and by recommending terms from existing ontologies when
plausible.
We analyze and compare PAV with related approaches, namely Provenance
Vocabulary, DC Terms and BIBFRAME. We identify similarities and analyze their
differences with PAV, outlining strengths and weaknesses of our proposed model.
We specify SKOS mappings that align PAV with DC Terms.Comment: 22 pages (incl 5 tables and 19 figures). Submitted to Journal of
Biomedical Semantics 2013-04-26 (#1858276535979415). Revised article
submitted 2013-08-30. Second revised article submitted 2013-10-06. Accepted
2013-10-07. Author proofs sent 2013-10-09 and 2013-10-16. Published
2013-11-22. Final version 2013-12-06.
http://www.jbiomedsem.com/content/4/1/3
Hypermedia-based discovery for source selection using low-cost linked data interfaces
Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed-even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness
Will this work for Susan? Challenges for delivering usable and useful generic linked data browsers
While we witness an explosion of exploration tools for simple datasets on Web 2.0 designed for use by ordinary citizens, the goal of a usable interface for supporting navigation and sense-making over arbitrary linked data has remained elusive. The purpose of this paper is to analyse why - what makes exploring linked data so hard? Through a user-centered use case scenario, we work through requirements for sense making with data to extract functional requirements and to compare these against our tools to see what challenges emerge to deliver a useful, usable knowledge building experience with linked data. We present presentation layer and heterogeneous data integration challenges and offer practical considerations for moving forward to effective linked data sensemaking tools
The Evolution of myExperiment
The myExperiment social website for sharing scientific workflows, designed according to Web 2.0 principles, has grown to be the largest public repository of its kind. It is distinctive for its focus on sharing methods, its researcher-centric design and its facility to aggregate content into sharable 'research objects'. This evolution of myExperiment has occurred hand in hand with its users. myExperiment now supports Linked Data as a step toward our vision of the future research environment, which we categorise here as '3rd generation e-Research'
A-posteriori provenance-enabled linking of publications and datasets via crowdsourcing
This paper aims to share with the digital library community different opportunities to leverage crowdsourcing for a-posteriori capturing of dataset citation graphs. We describe a practical approach, which exploits one possible crowdsourcing technique to collect these graphs from domain experts and proposes their publication as Linked Data using the W3C PROV standard. Based on our findings from a study we ran during the USEWOD 2014 workshop, we propose a semi-automatic approach that generates metadata by leveraging information extraction as an additional step to crowdsourcing, to generate high-quality data citation graphs. Furthermore, we consider the design implications on our crowdsourcing approach when non-expert participants are involved in the process<br/
Ontology-based patterns for the integration of business processes and enterprise application architectures
Increasingly, enterprises are using Service-Oriented Architecture (SOA) as an approach to Enterprise Application Integration (EAI). SOA has the potential to bridge
the gap between business and technology and to improve the reuse of existing applications and the interoperability with new ones. In addition to service architecture
descriptions, architecture abstractions like patterns and styles capture design knowledge and allow the reuse of successfully applied designs, thus improving the quality of
software. Knowledge gained from integration projects can be captured to build a repository of semantically enriched, experience-based solutions. Business patterns identify the interaction and structure between users, business processes, and data.
Specific integration and composition patterns at a more technical level address enterprise application integration and capture reliable architecture solutions. We use an
ontology-based approach to capture architecture and process patterns. Ontology techniques for pattern definition, extension and composition are developed and their
applicability in business process-driven application integration is demonstrated
Towards a Tool-based Development Methodology for Pervasive Computing Applications
Despite much progress, developing a pervasive computing application remains a
challenge because of a lack of conceptual frameworks and supporting tools. This
challenge involves coping with heterogeneous devices, overcoming the
intricacies of distributed systems technologies, working out an architecture
for the application, encoding it in a program, writing specific code to test
the application, and finally deploying it. This paper presents a design
language and a tool suite covering the development life-cycle of a pervasive
computing application. The design language allows to define a taxonomy of
area-specific building-blocks, abstracting over their heterogeneity. This
language also includes a layer to define the architecture of an application,
following an architectural pattern commonly used in the pervasive computing
domain. Our underlying methodology assigns roles to the stakeholders, providing
separation of concerns. Our tool suite includes a compiler that takes design
artifacts written in our language as input and generates a programming
framework that supports the subsequent development stages, namely
implementation, testing, and deployment. Our methodology has been applied on a
wide spectrum of areas. Based on these experiments, we assess our approach
through three criteria: expressiveness, usability, and productivity
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