Tech Reports
Tech Report kmi-04-16 Abstract
An Ontology-Driven Similarity Algorithm
Techreport ID: kmi-04-16
Date: 2004
Author(s): Maria Vargas-Vera, Enrico Motta
This paper presents our similarity algorithm between relations in a user query written in FOL (first order logic) and ontological relations. Our similarity algorithm takes two graphs and produces a mapping between elements of the two graphs (i.e. graphs associated to the query, a subsection of ontology relevant to the query). The algorithm assesses structural similarity and concept similarity. An evaluation of our algorithm using the KMi Planet ontology is presented. We also carried out an experiment to test the human judgment about similarity using context and without context. Our similarity algorithm has been mainly used in AQUA, our question answering system, in the query reformulation process.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Social Software is...
Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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