Knowledge base is a technology used to store complex structured and unstructured data used byrncomputer. Today, most knowledge bases cover just particular domain that is created by a smallrngroup of knowledge engineers because building general domain base knowledge is cost ly andrntime taking to cover a ll domains. Wikipedia has developed into one of the focal knowledgernso urces for everyone and is kept up by a large number of contributors but its structure has somernissue to use as knowledge source. The DBpedia project goes for extracting in formation based onrnsemi-structured information by presenting Wikipedia articles, interlinking it with otherrnknowledge bases, and publishing it as RDF triples openly on the Web. So far, the DBpediarnproject has succeeded in creatin g one of the largest knowledge bases on the web data, which isrnused in many applications and research prototypes.rnDBpedia extraction is extracts structured data (RDF) from Wikipedia. This study describes therneffort to extract Amharic DBpedia. During the extraction process, the extraction design presentrnby considering Amharic language. The tool used to extract Amharic DBpedia is 118n extract ionrnframework. The result shows more than quarter million Amharic RDF trip les extracted. Inrnaddition to this achievement, the improvement of Amharic Wikipedia infoboxes could increasernthe quality of extracting RDF triples. The result also shows extracting Amharic DBpedia isrnapplicable and the language can be a part of the internationalized DBpedia chapter.rnEven if the study shows encouraging results, there are some remaining work needs to be done tornget full Amharic DBpedia chapter. Abstract and homepage extractions must include having a fullrnversion of Amharic DBpedia chapter. Live base DBped ia extraction can be a considerable in thernfuture work because it can get dynamic knowledge from Wikipedia and has a capability torndeliver in stant RDF triples.rnBuilding Amharic knowledge bases, including Amharic DBpedia RDF store helps in order tornfacilitate access and querying structured data. Furthermore, the Amharic triple store can be rnknowledge source for NLP tasks and web applications.rnKeywords: DBpedia, 118n Extraction fram ework, RDF, Semantic Web, Wikiped ia.