Learning Ontologies by Processing Natural Language Text
This event took place on Wednesday 03 November 2004 at 12:30
Dileep Damle KMi, The Open University
Ontology construction is a costly and slow process requiring ontological engineering skills as well as domain expertise. The semantic web will be greatly facilitated if domain ontologies could be built quickly and cheaply without human expertise of either kind.
One approach to automatic ontology creation is to utilize existing knowledge resources such as database schema. These can be very useful, but are rare and natural language text is more likely to be available for many domains. But complete, unambiguous and accurate interpretation of natural language texts by computers is currently a very major challenge This work is concerned with extracting ontological elements such as concepts, their properties and inter-relationships from natural language corpora in order to grow an ontology for the domain in a semi-automatic way. The hypothesis is that it is not necessary to fully, and unambiguously interpret all sentences in text, but inferences drawn from parts of sentences may be sufficient for the purpose if enough text is available.
The presentation will cover some early results in two of the main areas of this research.
1. Identification of the domain relevant terms in the corpus
2. Some early examples of semantic extractors
This event took place on Wednesday 03 November 2004 at 12:30
Dileep Damle KMi, The Open University
Ontology construction is a costly and slow process requiring ontological engineering skills as well as domain expertise. The semantic web will be greatly facilitated if domain ontologies could be built quickly and cheaply without human expertise of either kind.
One approach to automatic ontology creation is to utilize existing knowledge resources such as database schema. These can be very useful, but are rare and natural language text is more likely to be available for many domains. But complete, unambiguous and accurate interpretation of natural language texts by computers is currently a very major challenge This work is concerned with extracting ontological elements such as concepts, their properties and inter-relationships from natural language corpora in order to grow an ontology for the domain in a semi-automatic way. The hypothesis is that it is not necessary to fully, and unambiguously interpret all sentences in text, but inferences drawn from parts of sentences may be sufficient for the purpose if enough text is available.
The presentation will cover some early results in two of the main areas of this research.
1. Identification of the domain relevant terms in the corpus
2. Some early examples of semantic extractors
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Our research in the Semantic Web area looks at the potentials of fusing together advances in a range of disciplines, and applying them in a systemic way to simplify the development of intelligent, knowledge-based web services and to facilitate human access and use of knowledge available on the web. For instance, we are exploring ways in which tnatural language interfaces can be used to facilitate access to data distributed over different repositories. We are also developing infrastructures to support rapid development and deployment of semantic web services, which can be used to create web applications on-the-fly. We are also investigating ways in which semantic technology can support learning on the web, through a combination of knowledge representation support, pedagogical theories and intelligent content aggregation mechanisms. Finally, we are also investigating the Semantic Web itself as a domain of analysis and performing large scale empirical studies to uncover data about the concrete epistemologies which can be found on the Semantic Web. This exciting new area of research gives us concrete insights on the different conceptualizations that are present on the Semantic Web by giving us the possibility to discover which are the most common viewpoints, which viewpoints are mutually inconsistent, to what extent different models agree or disagree, etc...
Our aim is to be at the forefront of both theoretical and practical developments on the Semantic Web not only by developing theories and models, but also by building concrete applications, for a variety of domains and user communities, including KMi and the Open University itself.
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