Ontology population and enrichment: state of the art
Knowledge-driven multimedia information extraction and ontology evolution
Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes
The Journal of Machine Learning Research
Learning concept hierarchies from textual resources for ontologies construction
Expert Systems with Applications: An International Journal
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This paper presents a method along with a set of measures for evaluating learned ontologies against gold ontologies. The proposed method transforms the ontology concepts and their properties into a vector space representation to avoid the common string matching of concepts and properties at the lexical layer. The proposed evaluation measures exploit the vector space representation and calculate the similarity of the two ontologies (learned and gold) at the lexical and relational levels. Extensive evaluation experiments are provided, which show that these measures capture accurately the deviations from the gold ontology. The proposed method is tested using the Genia and the Lonely Planet gold ontologies, as well as the ontologies in the benchmark series of the Ontology Alignment Evaluation Initiative.