Enormous amount of data has been produced in electronic format in Amharic language which ledrnto information explosion. This has created a major challenge for information managers inrnprocessing information and providing it to users quickly and easily. Therefore, some indexingrnmethods have been proposed for Amharic language by researchers so far. However, thesernmethods are not capable enough to capture the semantics of documents. In this research, an effortrnhas been made to build a semantic indexer for Amharic football news articles by applyingrndomain ontology.rnThe main purpose of the study is to construct an index which is embedded with the ontology sornas to minimize query processing time. Ontology development, Document indexing, and Queryrnprocessing are the core components of the study. Document indexing component is composed ofrnConcept Tagger, Information Extraction, Concept Weighting, and Ontology Population modules.rnThe role of Concept Tagger module is to annotate documents with concepts from the ontologyrnwhereas Information Extraction Module is responsible for identifying new individuals andrndetermining the relationship between concepts in the tagged/annotated documents. The ConceptrnWeighting module involves calculating weights for concepts and individuals using the domainrnontology. The weights computed for the concepts and individuals are added to the ontology byrnusing the Ontology Population module.rnThe query processing component is built with the purpose of testing the performance of thernindexer with user queries. This component has Query Caching, Individual Creator, DocumentrnRetrieval, and Document Ranking modules. Query caching is the process of registering originalrnand tagged query pairs in order to avoid running preprocessing and tagging modules wheneverrnthe same query is posed by users. Individual Creator module is intended to produce newrnindividuals from queries and adding them to the ontology. Finally, the Document Retrieval andrnDocument Ranking modules are used to retrieve and rank documents according to their level ofrnrelevance. Concept reasoning or inferencing is the main task in the document retrieval process.rnThe precision, recall, and F-measure techniques are used to evaluate the performance of thernproposed system and the classical IR based on the relevance information provided by experts.rnThe result shows that the proposed semantic indexer has better performance than the lucenernindexer used in the classical IR.rnXrnKey Words: Semantic indexing, Football domain ontology, Rule-based information extraction,rnSemantic information retrieval, Query processor, Concept tagging.