Scientific Knowledge Object Patterns

Web technology is revolutionizing the way diverse scientific knowledge is produced and disseminated. In the past few years, a handful of discourse representation models have been proposed for the externalization of the rhetoric and argumentation captured within scientific publications. However, there hasn’t been a unified interoperable pattern that is commonly used in practice by publishers and individual users yet. In this paper, we introduce the Scientific Knowledge Object Patterns (SKO Patterns) towards a general scientific discourse representation model, especially for managing knowledge in emerging social web and semantic web. © ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is going to be published in "Proceedings of 15th European Conference on Pattern Languages of Programs", (2011) http://portal.acm.org/event.cfm?id=RE197&CFID=8795862&CFTOKEN=1476113

An MPEG-7 scheme for semantic content modelling and filtering of digital video

Abstract Part 5 of the MPEG-7 standard specifies Multimedia Description Schemes (MDS); that is, the format multimedia content models should conform to in order to ensure interoperability across multiple platforms and applications. However, the standard does not specify how the content or the associated model may be filtered. This paper proposes an MPEG-7 scheme which can be deployed for digital video content modelling and filtering. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user. We present details of the scheme, front-end systems used for content modelling and filtering and experiences with a number of users

Applications of semantic web technology to support learning content development

The Semantic Web is based on ontology technology – a knowledge representation framework – at its core to make meaning explicit and more accessible to automatic processing. We discuss the potential of this technology for the development of content for learning technology systems. We survey seven application types demonstrating different forms of applications of ontologies and the Semantic Web in the development of learning technology systems. Ontology technologies can assist developers, instructors, and learners to organise, personalise, and publish learning content and to discover, generate, and compose learning content. A conceptual content development and deployment architecture allows us to distinguish and locate the different applications and to dis-cuss and assess the potential of the underlying technologies

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

The Semantic Grid: A future e-Science infrastructure

e-Science offers a promising vision of how computer and communication technology can support and enhance the scientific process. It does this by enabling scientists to generate, analyse, share and discuss their insights, experiments and results in an effective manner. The underlying computer infrastructure that provides these facilities is commonly referred to as the Grid. At this time, there are a number of grid applications being developed and there is a whole raft of computer technologies that provide fragments of the necessary functionality. However there is currently a major gap between these endeavours and the vision of e-Science in which there is a high degree of easy-to-use and seamless automation and in which there are flexible collaborations and computations on a global scale. To bridge this practice–aspiration divide, this paper presents a research agenda whose aim is to move from the current state of the art in e-Science infrastructure, to the future infrastructure that is needed to support the full richness of the e-Science vision. Here the future e-Science research infrastructure is termed the Semantic Grid (Semantic Grid to Grid is meant to connote a similar relationship to the one that exists between the Semantic Web and the Web). In particular, we present a conceptual architecture for the Semantic Grid. This architecture adopts a service-oriented perspective in which distinct stakeholders in the scientific process, represented as software agents, provide services to one another, under various service level agreements, in various forms of marketplace. We then focus predominantly on the issues concerned with the way that knowledge is acquired and used in such environments since we believe this is the key differentiator between current grid endeavours and those envisioned for the Semantic Grid