This research proposes a method, Amharic ontology learner, which helps tornautomatically learn or extract ontology from an unstructured Amharic text.rnAmharic ontology learner handles the ontology learning process throughrndistinct process layers, concept extraction, taxonomy building, and nontaxonomicrnrelations mining.rnOnce all potential concepts are extracted a concept hierarchy (taxonomy) isrnformed, which is then supplemented by non-taxonomic relations to evolve therntaxonomy into a full ontology. Different methods have been used to implementrneach layer.rnAmharic ontology learner is based on both single-word and multi-wordrnconcepts, as these make the ontology to be represented by a more solid andrndistinctive concepts. A hierarchical agglomerative clustering method is used forrnbuilding the domain taxonomy. To identify the non-taxonomic relations arnlinguistic method, verbal expressions as a relation indicator, is used and arnmethod which tries to find out the most appropriate level of generalization forrnthe relation is also implemented at the top of the non-taxonomic relationrnmining module.rnTo practically test the performance of the methods, modules in Amharicrnontology learner are implemented. Our method can also represent the extractedrnontology in OWL using Jena Semantic Web Framework. Amharic ontologyrnlearner is applied to an already tagged news corpus from WALTA News Agency.rnThe result shows that Amharic ontology learner can be used as a starting pointrnfor future researches related to Ontologies and Ontology learning from Amharicrntext.rnKeywords: Ontology, Ontology learning, Concept, taxonomy, Conceptrnrelationship.