Template-driven information extraction for populating ontologies
Template-driven information extraction for populating ontologies
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 Language Processing(NLP) techniques. Our main goal is to learn information from texts 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 for communication of stories between members in our institute. The main goals of our system are to classify an incoming 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.
Vargas-Vera, Maria
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Domingue, John
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Kalfoglou, Yannis
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Motta, Enrico
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Buckingham-Shum, Simon
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August 2001
Vargas-Vera, Maria
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Domingue, John
cd25567d-c2c1-4dad-a65c-be86a96f6150
Kalfoglou, Yannis
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Motta, Enrico
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Buckingham-Shum, Simon
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Vargas-Vera, Maria, Domingue, John, Kalfoglou, Yannis, Motta, Enrico and Buckingham-Shum, Simon
(2001)
Template-driven information extraction for populating ontologies.
IJCAI'01 Workshop on Ontology Learning.
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Conference or Workshop Item
(Paper)
Abstract
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 Language Processing(NLP) techniques. Our main goal is to learn information from texts 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 for communication of stories between members in our institute. The main goals of our system are to classify an incoming 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.
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kmi-tr-105.pdf
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Published date: August 2001
Additional Information:
Event Dates: August 2001
Venue - Dates:
IJCAI'01 Workshop on Ontology Learning, 2001-08-01
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 256437
URI: http://eprints.soton.ac.uk/id/eprint/256437
PURE UUID: 85bc7538-5596-4f5c-9d95-b9a5c9ce56b6
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Date deposited: 26 Mar 2002
Last modified: 14 Mar 2024 05:42
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Contributors
Author:
Maria Vargas-Vera
Author:
John Domingue
Author:
Yannis Kalfoglou
Author:
Enrico Motta
Author:
Simon Buckingham-Shum
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