The Semantic Web and Linked Data Research Programme: Enabling, Creating and Using Networked Knowledge. The Semantic Web and Linked Data Research Programme in Insight (SemanticWeb@Insight) aims to investigate the transition from raw data into large scale networks of semantic data and then into an interconnected Network of Knowledge, which assists people, organisations and systems with problem solving and enabling innovation and increased productivity, and aims to be as usable as knowledge is in the human brain.
Insight researchers have extensive experience in mainstream research on machine learning and statistics that includes, supervised and unsupervised learning, dimension reduction, Bayesian methods and the application of these methods in a variety of domains.
Modern technology has facilitated a huge growth in the amount, and variety of media types with which we can communicate information. Text, image, moving video, 3D, as well as data from real-time feeds, used in isolation or more likely, in combination are now commonly used to encode information digitally.
Optimisation and decision analytics technologies are ubiquitous in the modern world. These technologies are applied in many real-world contexts such as telecommunications, transportation, logistics, internet commerce, life sciences, transportation, network management, supply chain management, energy efficiency, environmental sustainability, and many others. Making decisions in many settings can offer too many choices, many of which are incompatible, few of which are optimal.
Recent years have provided us with unprecedented access to a wide range of inexpensive sensing, aggregation and communication technologies that can transform society through provision of access to data relating to human behaviour and performance in health and sport. However, this ever-advancing sensor web needs to be carefully matched to relevant data analytics and domain expertise to unlock the inherent value in the complex datasets that it produces.
Today's data-deluge motivates the need for new ways to provide people and organisations with access to the right information in the right time. In short, we need to develop more personalised forms of information access and discovery that are capable of understanding the needs of individuals and responding to these needs in a more targeted way. Recommender systems represent one approach to developing more personalised information systems that have gained considerable traction online, particularly in an e-commerce context. Today, services like Amazon, iTunes, and Netflix help millions of people find what they are looking for by automatically recommending relevant items from a long tail of almost infinite possibilities.
In this research program we are developing the necessary techniques for the next generation of Web-based data analytics by bringing together ideas and approaches from text-mining and NLP, stream processing, reasoning, graph analytics and social Web systems, to enable a more comprehensive platform for data analytics systems. As a result new, large sources of information will become available in a timely fashion enabling better and new forms of data analytics.