<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear all, <div><br></div><div>Positions are available for PhD students at the Knowledge Media Institute of the Open University, UK (see <a href="http://kmi.open.ac.uk/studentships/vacancies.php">http://kmi.open.ac.uk/studentships/vacancies.php</a>) in topics relevant to this mailing list. </div><div><br></div><div>The deadline for applications is June 7, 2012.</div><div><br></div><div>The Knowledge Media Institute (KMi) is home to internationally recognised researchers in semantic technologies, educational multimedia, collaboration technologies, artificial intelligence, cognitive science, and human-computer interaction. KMi offers students an intellectually challenging environment with exceptional research and computer facilities. </div><div><br></div><div>The following two PhD projects are in particular related to the use and development of knowledge technologies, ontologies, the Semantic Web and Linked Data. Please contact the relevant members of KMi for more information.</div><div><br></div><div><b>Web Data Mining</b></div><div>Contact: Mathieu d'Aquin (<a href="mailto:m.daquin@open.ac.uk">m.daquin@open.ac.uk</a>)</div><div><a href="http://kmi.open.ac.uk/studentships/web-data-mining.php">http://kmi.open.ac.uk/studentships/web-data-mining.php</a></div><div><br></div><div>The Web is currently being flooded with data, from information reported in documents for human consumption to, more and more, open data available in structured and reusable forms (APIs, linked data, etc.) While a lot of efforts have been dedicated to the integration of data, using conceptual models such as ontologies, we are now facing the unprecedented need for support in "interpreting" data gathered from a large number of distributed, heterogeneous and un-controlled sources on the Web. <br><br>In this PhD studentship, which is inter-disciplinary in nature, the aim is to investigate the combination of ontology-based, top-down approaches to data interpretation, with techniques originating from data mining to make sense of data through the bottom-up emergence of meaningful information patterns. Contributions are therefore expected in the areas of the Semantic Web, linked data, ontology engineering, data mining, data analytics and machine learning. </div><div><br></div><div><b>Visual Exploration of Research Spaces</b></div><div>Contact: Enrico Motta (<a href="mailto:e.motta@open.ac.uk">e.motta@open.ac.uk</a>)</div><div><a href="http://kmi.open.ac.uk/studentships/visual-exploration-of-research-spaces.php">http://kmi.open.ac.uk/studentships/visual-exploration-of-research-spaces.php</a></div><div><br></div><div>A PhD studentship is available at the Knowledge Media Institute of the Open University on the Visual Exploration of Research Spaces. This project is inherently interdisciplinary and the successful candidate will contribute to our established research in one or more areas, including human-computer interaction, semantic, statistical and natural language technologies. The main aim of this project is to investigate methods for improving the ability of users to explore the rich set of data which are now available about scholarly research, by developing i) new knowledge-based methods for aggregating data from both traditional (e.g., bibliographic servers) and other sources (e.g., blogs, tweets, conference web sites, etc), as well as ii) novel visual analytics solutions, able to support a seamless and flexible exploration of this rich set of aggregated types of data. In particular, we are interested in building on the work carried out on the KC-Viz system for ontology navigation and visualization - <a href="http://kmi.open.ac.uk/technologies/name/kc-viz">http://kmi.open.ac.uk/technologies/name/kc-viz</a>, which relies on a novel abstraction mechanism based on the notion of key concepts, which denote the most important elements of an ontology and act as 'islands' to structure the exploration process and abstract from the large space of concepts in an ontology. Thus, we are interested in developing new methods that can employ this navigation metaphor in the context of research spaces, by identifying the appropriate notions and abstraction techniques, which apply in the research domain.</div><div><br></div><p><br>
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