Data Driven Ontology Evaluation

By Christopher Arthur Brewster, Harith Alani, S. Dasmahapatra, Yorick Wilks. International Conference on Language Resources and Evaluation, 2004

479 citations recorded by the source corpus.

The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the ‘fit’ between an ontology and a domain of knowledge. We consider a number of methods for measuring this ‘fit’ and propose a measure to evaluate structural fit, and a probabilistic approach to identifying the best ontology.

Subfields: Artificial Intelligence, Management Science and Operations Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/00094f512b4e23699e0ba388df1d3ff9295e3d89

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Citations in the corpus

Modelling Ontology Evaluation and Validation (2006, background): “Recent work has focused also on discovering class attributes and arbitrary relation between classes through automatic or semi-automatic population of ontology objects (see [1], [5], and [4]).”
Ontology-based semantic modeling of construction safety knowledge: Towards automated safety planning for job hazard analysis (JHA) (2015, background): “However, content-based evaluation is needed to evaluate whether the ontology properly represents the target domain [48].”
Formalizing information security knowledge (2009, background): “Since most ontology evaluation approaches, as described in [9], [29], or [10], are concerned with selecting the most appropriate ontology from a set of existing ontologies, the approach by [39] has been adopted to create an evaluation methodology which is able to check an ontology against its initia”
A SURVEY OF ONTOLOGY EVALUATION TECHNIQUES (2005, methodology): “(1) Given a corpus of documents from the domain of interest, a clustering algorithm based on EM is used to determine, in an unsupervised way, a probabilistic mixture model of hidden “topics” such that each document can be modeled as having been generated by a mixture of topics.”