Tech Reports
Tech Report kmi-04-30 Abstract
Adaptive Named Entity Recognition for Social Network Analysis and Domain Ontology Maintenance
Techreport ID: kmi-04-30
Date: 2004
Author(s): Jianhan Zhu, Alexandre L. Goncalves, Victoria Uren
We present a system which unearths relationships between named entities from information in Web pages. We use an adaptive named entity recognition system, ESpotter, which recognizes entities of various types with high precision and recall from various domains on the Web, to generate entity data such as peoples' names. Given an entity, we apply a link analysis algorithm to the entity data for finding other entities which are closely related to it. We present our results to people whose names had been included for them to assess our findings. User feedback is analyzed by a statistical method. The results can be used to maintatin a domain ontology. Our experiments on the Knowledge Media Institute (KMi) domain show that our system can accurately find entities such as organizations, people, projects, and research areas which are closely related to people working in KMi, and the results conform with the existing knowledge in our ontology and suggest new knowledge which can be used to update the ontology.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Social Software is...
Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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