Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings

Re-use of an ontology for modelling urban energy systems

The use of ontologies for the interoperability of software models is widespread, with many applications also in the energy domain. By formulating a shared data structure and a definition of concepts and their properties, a language is created that can be used between modellers and - formalised in an ontology - between model components. When modelling energy systems, connections between different infrastructures are critical, e.g. the interaction between the gas and electricity markets or the need for various infrastructures including power, heat, water and transport in cities. While a commonly shared ontology of energy systems would be highly desirable, the fact is that different existing models or applications already use dedicated ontologies, and have been demonstrated to work well using them. To benefit from linking data sources and connecting models developed with different ontologies, a translation between concepts can be made. In this paper a model of an urban energy system built upon one ontology is initialised using energy transformation technologies defined in another ontology, thus illustrating how this common perspective might benefit researchers in the energy domain. ©2010 IEEE

Building a Disciplinary, World-Wide Data Infrastructure

Sharing scientific data, with the objective of making it fully discoverable, accessible, assessable, intelligible, usable, and interoperable, requires work at the disciplinary level to define in particular how the data should be formatted and described. Each discipline has its own organization and history as a starting point, and this paper explores the way a range of disciplines, namely materials science, crystallography, astronomy, earth sciences, humanities and linguistics get organized at the international level to tackle this question. In each case, the disciplinary culture with respect to data sharing, science drivers, organization and lessons learnt are briefly described, as well as the elements of the specific data infrastructure which are or could be shared with others. Commonalities and differences are assessed. Common key elements for success are identified: data sharing should be science driven; defining the disciplinary part of the interdisciplinary standards is mandatory but challenging; sharing of applications should accompany data sharing. Incentives such as journal and funding agency requirements are also similar. For all, it also appears that social aspects are more challenging than technological ones. Governance is more diverse, and linked to the discipline organization. CODATA, the RDA and the WDS can facilitate the establishment of disciplinary interoperability frameworks. Being problem-driven is also a key factor of success for building bridges to enable interdisciplinary research.Comment: Proceedings of the session "Building a disciplinary, world-wide data infrastructure" of SciDataCon 2016, held in Denver, CO, USA, 12-14 September 2016, to be published in ICSU CODATA Data Science Journal in 201

DRIVER Technology Watch Report

This report is part of the Discovery Workpackage (WP4) and is the third report out of four deliverables. The objective of this report is to give an overview of the latest technical developments in the world of digital repositories, digital libraries and beyond, in order to serve as theoretical and practical input for the technical DRIVER developments, especially those focused on enhanced publications. This report consists of two main parts, one part focuses on interoperability standards for enhanced publications, the other part consists of three subchapters, which give a landscape picture of current and surfacing technologies and communities crucial to DRIVER. These three subchapters contain the GRID, CRIS and LTP communities and technologies. Every chapter contains a theoretical explanation, followed by case studies and the outcomes and opportunities for DRIVER in this field

HILT IV : subject interoperability through building and embedding pilot terminology web services

A report of work carried out within the JISC-funded HILT Phase IV project, the paper looks at the project's context against the background of other recent and ongoing terminologies work, describes its outcome and conclusions, including technical outcomes and terminological characteristics, and considers possible future research and development directions. The Phase IV project has taken HILT to the point where the launch of an operational support service in the area of subject interoperability is a feasible option and where both investigation of specific needs in this area and practical collaborative work are sensible and feasible next steps. Moving forward requires detailed work, not only on terminology interoperability and associated service delivery issues, but also on service and end user needs and engagement, service sustainability issues, and the practicalities of interworking with other terminology services and projects in UK, Europe, and global contexts

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain