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
Multimedia and Information Systems is...
We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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