Towards a Goal Driven Learner for the Semantic Web


In our recent research [1] we have argued that when the Semantic Web (SW) becomes a reality, there will be a need for machine learning methods that can operate in a Semantic Web framework. Although the SW will deliver ontology based structured information, we argue that not every piece of useful knowledge will be explicit or inferable; and that learning will help to identify this knowledge. In addition, such an interconnected knowledge-web should be easy to learn from, compared to the current unstructured World Wide Web, so we also hypothesise that SW learning should outperform learning from unstructured text.

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Cite this paper

@inproceedings{GrimnesTowardsAG, title={Towards a Goal Driven Learner for the Semantic Web}, author={Gunnar AA. Grimnes and Alun D. Preece and Pete Edwards} }