Victoria Uren's profile document
Description for Victoria Uren
Victoria Uren
Victoria Uren
Victoria
Uren
Senior Research Fellow
I worked on knowledge management projects that use semantic web technologies.
The Open University account for Victoria Uren
vsu2
Victoria Uren's membership at KMi
Victoria Uren's participation in ScholOnto
ScholOnto
ScholOnto
2001-02-01
2004-01-31
Build and deploy a prototype infrastructure for making scholarly claims
The Scholarly Ontologies project is investigating new ways for distributed research communities to track and interpret their literatures. The ClaiMaker system enables researchers to make and contest 'Claims' by semantically connecting concepts. The resulting network of claims and arguments then supports novel forms of literature search and browsing.
Victoria Uren's participation in AKT
AKT
AKT
2000-10-01
2006-09-30
Advanced Knowledge Technologies
The AKT project aims to develop the next generation of knowledge technologies to support organizational knowledge management. AKT will look at all aspects of knowledge management from acquiring and maintaining knowledge to publishing and sharing it. We intend to address all these closely related issues in an integrated approach, making use of recent developments in artificial intelligence, psychology, linguistics, multimedia and Internet technology. The AKT consortium comprises five UK universities and is funded by a 7M GBP, 6-year EPSRC grant in the context of the Interdisciplinary Research Collaborations programme.
Victoria Uren's participation in Magpie
Magpie
Magpie
The semantic filter
Magpie adds an ontology based semantic layer onto web pages on-the-fly as they are browsed. Magpie automatically highlights key
items of interest, and for each highlighted
term it provides a set of 'services' (e.g. contact details, current projects, related people) when you right-click on the item.
Victoria Uren's participation in DOT.KOM
DOT.KOM
DOT.KOM
2002-10-01
2005-03-31
Designing adaptive infOrmation exTraction from text for KnOwledge Management
DotKom aims to support knowledge management within large corporation through a combination of information extraction and knowledge management technologies. A current problem with both of these technologies is that they are hard-to-use and require extensive expertise. DotKom will provide user-friendly *adaptive* information extraction tools which give instantaneous feedback on the current status of the information extraction learning process and the automatically constructed knowledge acquisition mechanisms.
Victoria Uren's participation in CORDER
CORDER
CORDER
2005-12-22
COmunity Relation Discovery by named Entity Recognition
CORDER (COmmunity Relation Discovery by named Entity Recognition) is an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in a community with their expertise and associates. CORDER discovers relations from the Web pages of the community. Its approach is based on co-occurrences of NEs and the distances between them. For a given NE, there are a number of co-occurring NEs. We assume that NEs that are closely related to each other tend to appear together more often and closer to each other in Web pages. We calculate a relation strength for each co-occurring NE based on its co-occurrences and distances from the given NE. The co-occurring NEs are ranked by their relation strengths.
Victoria Uren's participation in ESpotter
ESpotter
ESpotter
2005-12-22
Adaptive Named Entity Recognition for Web Browsing
Named entity recognition (NER) systems are commonly designed with a "one-size-fits-all" philosophy. Lexicons and patterns manually crafted or learned from a training set of documents are applied to any other document without taking into account its background and user needs. However, when applying NER to Web pages, due to the diversity of these Web pages and user needs, one size frequently does not fit all. We present a system called ESpotter, which improves NER on the Web by adapting lexicons and patterns to domains on the Web and user preferences. Our results show that ESpotter provides more accurate and efficient NER on Web pages from various domains than current NER systems.
Victoria Uren's participation in SemSearch
SemSearch
SemSearch
A Search Engine for the Semantic Web
SemSearch is a semantic search engine, which is designed for naïve users, i.e., ordinary end users who are not necessarily familiar with domain specific semantic data, ontologies, or SQL-like query Languages. It hides the complexity of semantic search from end users by supporting a Google-like query interface and by providing comprehensive means to translate user queries into formal queries.
Victoria Uren's participation in Hypermedia Discourse
Hypermedia Discourse
Hypermedia Discourse
Conceptual foundations and practical tools at the nexus of Deliberation, Argumentation and Software
Our focus is on what we are finding to be a powerful and intruiging intersection: the meeting of Hypermedia and Discourse theory and technology. Our interests are both conceptual, and intensely practical: the co-evolution of digital tools and associated work practices for sensemaking.
Victoria Uren's participation in PowerAqua
PowerAqua
PowerAqua
A Natural Language Interface to the Semantic Web
PowerAqua is a multi-ontology-based Question Answering (QA) system, which takes as input queries expressed in natural language and is able to return answers drawn from the relevant distributed resources on the Semantic Web. In contrast with any other existing natural language front end, PowerAqua is not restricted to a single ontology and therefore provides the first comprehensive attempt at supporting open domain QA on the Semantic Web.