Learning from semantic flora & fauna

Grimnes, Gunnar Astrand, Preece, Alun David ORCID: https://orcid.org/0000-0003-0349-9057 and Edwards, Pete 2004. Learning from semantic flora & fauna. Presented at: AAAI-04 Semantic Web Personalisation Workshop, San Jose, CA, 25–26 July 2004.

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Abstract

We argue that for a personal agent working within a Semantic Web framework, machine learning is essential. We identify two topologies in the Semantic Web, and refer to these as: (1) semantic forests (disjoint trees) and (2) true semantic webs (complex interconnected graphs). An example of (1) is Citeseer BibTeX mapped to RDF; an example of (2) is FOAF, an RDF representation of people and their relationships. In this paper we explore a number of techniques (na¨ive Bayes, K-NN, ILP, clustering) for learning knowledge that is neither explicitly stated nor deducable from such data. The learned knowledge itself consists of firstclass Semantic Web statements, maximizing its usefulness and re-usability. We also discuss the need for preprocessing and fault tolerance when learning from real distributed Semantic Web data.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	Unpublished
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Last Modified:	24 Oct 2022 10:26
URI:	https://orca.cardiff.ac.uk/id/eprint/44394

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