Terminology server for improved resource discovery: analysis of model and functions

This paper considers the potential to improve distributed information retrieval via a terminologies server. The restriction upon effective resource discovery caused by the use of disparate terminologies across services and collections is outlined, before considering a DDC spine based approach involving inter-scheme mapping as a possible solution. The developing HILT model is discussed alongside other existing models and alternative approaches to solving the terminologies problem. Results from the current HILT pilot are presented to illustrate functionality and suggestions are made for further research and development

Ontology-assisted database integration to support natural language processing and biomedical data-mining

Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for the purposes of making computers understand medical natural language

Enhancing Workflow with a Semantic Description of Scientific Intent

Peer reviewedPreprin

Interoperable subject retrieval in a distributed multi-scheme environment : new developments in the HILT project

The HILT (HIgh-Level Thesaurus) project (http://hilt.cdlr.strath.ac.uk/), based primarily at the Centre for Digital Library Research (CDLR) (http://cdlr.strath.ac.uk/) at Strathclyde University in Glasgow is entering its fourth stage following the completion of Phases I (http://hilt.cdlr.strath.ac.uk/index1.html) and II (http://hilt.cdlr.strath.ac.uk/index2.html) and the Machine to Machine (M2M) Feasibility Study (http://hilt.cdlr.strath.ac.uk/hiltm2mfs/). HILT is funded by the Joint Information Systems Committee (JISC) in the United Kingdom (UK) to examine an issue of global significance - facilitating interoperability of subject descriptions in a distributed, cross-service retrieval environment where different services use different subject and classification schemes to describe content, making cross-searching by subject difficult. HILT Phase I determined that there was a community consensus in the UK in favour of using inter-scheme mapping to achieve interoperability between services using different schemes, an approach followed by several recent projects (Heery et al, 2001; Koch et al, 2001; MACS, 2005; Saeed and Chaudhury 2002). HILT Phase II chose a spine-based approach to mapping and chose the Dewey Decimal Classification (DDC) as the central scheme to which all other schemes would be mapped. It also built an illustrative pilot mapping service, based on an adaptation of the Wordmap (http://www.wordmap.com/) terminology-handling software and made a range of recommendations on issues requiring further research and ongoing development requirements

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining