The Emerging Field of Semantic Scientific Knowledge Integration

2011

Organizing scientific knowledge in systematic ways becomes increasingly important. However, the creation of intra-and inter-disciplinary knowledge bases is hindered by the heterogeneity and the scale of the information to consider.

2016

By implementing Linked Open Data principles throughout the scientific community, it is possible to make publications more visible and foster collaboration, both between universities, researching groups and partners. However, data should be curated and published, and a mature infrastructure needs to be provided to support it. In this work, we analyse the Linked Data's weaknesses and propose an application prototype to evaluate the state-of-the-art methodologies and tools. As case of study, data from the authors' researching groups will be publicly available in an attempt to integrate the scientific data of the Argentinian community which is an open issue. We plan to use this data to generate bottom-up methodological guidelines and thus enrich ontology-based conceptual models.

Concurrency and Computation: Practice and Experience, 2011

The Tupelo semantic content management middleware implements Knowledge Spaces that enable scientists to integrate information into a comprehensive research record as they work with existing tools and domainspecific applications. Knowledge Spaces combine approaches that have demonstrated success in automating parts of this integration activity, including content management systems for domain-neutral management of data, workflow technologies for management of computation and analysis, and semantic web technologies for extensible, portable, citable management of descriptive information and other metadata. Tupelo's 'Context' facility and its associated semantic operations both allow existing data representations and tools to be plugged in, and also provide a semantic 'glue' of important associative relationships that span the research record, such as provenance, social networks, and annotation. Tupelo has enabled the recent work creating e-Science cyberenvironments to serve distributed, active scientific communities, allowing researchers to develop, coordinate and share datasets, documents, and computational models, while preserving process documentation and other contextual information needed to produce an integrated research record suitable for distribution and archiving.

Wiley Series in Communications Networking & Distributed Systems

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.

2006

Abstract. Data integration across multiple disciplines is a key requirement for virtual observatories. We have designed and implemented a virtual observatory for the areas of solar, solar-terrestrial, and space physics that leverages semantic technologies. Our work provides an implemented demonstration of ontologyenabled semantic interoperability. We have encoded an ontology in OWL covering topics of importance to atmospheric and solar researchers. This ontology is used to inform our virtual observatory services. Our goal was ...

Proceedings of the eighth ACM international workshop on Web information and data management - WIDM '06, 2006

Increasingly, scientists are seeking to collaborate and share data among themselves. Such sharing is can be readily done by publishing data on the World-Wide Web. Meaningful querying and searching on such data depends upon the availability of accurate and adequate metadata that describes the data and the sources of the data. In this paper, we outline the architecture of an implemented cyber-infrastructure for chemistry that provides tools for users to upload datasets and their metadata to a database. Our proposal combines a two level metadata system with a centralized database repository and analysis tools to create an effective and capable data sharing infrastructure.

PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2007

The Virtual Solar-Terrestrial Observatory is a production semantic web data framework providing access to observational datasets from fields spanning upper atmospheric terrestrial physics to solar physics. The observatory allows virtual access to a highly distributed and heterogeneous set of data that appears as if all resources are organized, stored and retrieved/used in a common way. The end-user community comprises scientists, students, data providers numbering over 600 out of an estimated community of 800. We present details on the case study, our technological approach including the semantic web languages, tools and infrastructure deployed, benefits of AI technology to the application, and our present evaluation after the initial nine months of use.