Automatic Ontology Learning from Unstructured Amharic Text
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Date
2013-03
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Addis Ababa University
Abstract
This research proposes a method, Amharic ontology learner, which helps to
automatically learn or extract ontology from an unstructured Amharic text.
Amharic ontology learner handles the ontology learning process through
distinct process layers, concept extraction, taxonomy building, and nontaxonomic
relations mining.
Once all potential concepts are extracted a concept hierarchy (taxonomy) is
formed, which is then supplemented by non-taxonomic relations to evolve the
taxonomy into a full ontology. Different methods have been used to implement
each layer.
Amharic ontology learner is based on both single-word and multi-word
concepts, as these make the ontology to be represented by a more solid and
distinctive concepts. A hierarchical agglomerative clustering method is used for
building the domain taxonomy. To identify the non-taxonomic relations a
linguistic method, verbal expressions as a relation indicator, is used and a
method which tries to find out the most appropriate level of generalization for
the relation is also implemented at the top of the non-taxonomic relation
mining module.
To practically test the performance of the methods, modules in Amharic
ontology learner are implemented. Our method can also represent the extracted
ontology in OWL using Jena Semantic Web Framework. Amharic ontology
learner is applied to an already tagged news corpus from WALTA News Agency.
The result shows that Amharic ontology learner can be used as a starting point
for future researches related to Ontologies and Ontology learning from Amharic
text.
Keywords: Ontology, Ontology learning, Concept, taxonomy, Concept
relationship.
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Keywords
Ontology; Ontology Learning; Concept; Taxonomy; Concept Relation ship