Tech Reports
Tech Report kmi-01-08 Abstract
Template-Driven Information Extraction for Populating Ontologies
Techreport ID: kmi-01-08
Date: 2001
Author(s): Maria Vargas-Vera, John Domingue, Yannis Kalfoglou, Enrico Motta and Simon Buckingham-Shum
We address the integration of information extraction (IE) and ontologies. In particular, using an ontology to aid the IE process, and using the IE results to help populate the ontology. We perform IE by means of domain specific templates and the lightweight use of Natural Languages Processing techniques (NLP). Our main goal is to learn information from text by the use of templates and in this way to alleviate the main bottleneck in creating knowledge-base systems that is ``the extraction of knowledge''. Our domain of study is ``KMi Planet'', a Web-based news server that helps to communicate relevant information between members in our institute [Domingue and Scott, 1999]. The raw input consists of e-mailed stories written by members of the laboratory. The main goals of our system are to classify the story, obtain the relevant objects within the story, deduce the relationships between them, and to populate the ontology. Furthermore, we aim to do this with minimal help from the user.
Publication(s):
Submitted to the IJCAI'01 Workshop on Ontology Learning (OL-2001), Seattle, USA, August 4, 2001.
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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