International journal of signs and semiotic systems, Jul 1, 2015
The goal of Ontology Learning from Text is to learn ontologies that represent domains or applicat... more The goal of Ontology Learning from Text is to learn ontologies that represent domains or applications that change often. Manually learning and updating such ontologies is too expensive. This is the reason for the Ontology Learning discipline's emergence. The leading approach to Ontology Learning from Text is the Ontology Learning Layer Cake. This approach splits the task into four or five sequential tasks. Each of the tasks may use diverse methods, ranging from uses of Linguistic knowledge to Machine Learning. The authors review the shortcomings of the Ontology Learning Layer Cake approach and conclude that the approach is not viable for Ontology Learning from Text. They suggest alternative approaches that may help learning ontologies in an efficient, effective way.
We analyze the ontology learning objectives, reviewing the type of input one would expect to meet... more We analyze the ontology learning objectives, reviewing the type of input one would expect to meet when learning ontologies-peer-reviewed scientific papers in English, papers that undergo quality control. We analyze the Ontology Learning Layer Cake model and its shortcomings, proposing alternative models for ontology learning based on linguistic knowledge and existing, wide coverage syntactical, lexical and semantic resources, using constructs such as clauses. We conclude, after showing that the Ontology Learning Layer Cake has a low maximum F measure (probably below 0.6), that the alternatives should be explored.
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Papers by Abel Browarnik