Knowledge is awareness or familiarity gained by experiences of facts, data, and situations. Knowledge management includes techniques and processes to represent, store, search, integrate, and analyze knowledge that is available in digital form. Ontology is a formal explicit specification of a shared conceptualization of a domain of interest and it is a building block of the semantic web and formal description of knowledge. Ontologies capture the structure and knowledge about some domain of interest by describing the concepts in the domain and also the relationships that hold between those concepts. Even though Ethiopia has potential tourist destinations, the country is not benefited from its resources due to misperception about image of the country; lack of promoting the potential tourism resources of the country to the world; problems with sharing, searching and retrieval of tourist information. Thus, the country is forced to accept smaller number of tourists and not getting the benefits it deserves. The objective of this paper is to build ontology for Ethiopian Tourism so that it makes Ethiopian tourism destinations visible to international visitors. We use OWL language implemented in Protégé with other ontology development activities proposed in METHONTOLOGY to build Ethiopian tourism ontology. We also use OWL …

This paper will report on a research to develop Speaker Independent, Continuous Speech Recognizer for Kafi Noonoo (Afro-Asiatic language that belongs to North Omotic sub family in Ethiopia) using Hidden Markov Modeling technique. The portable and open source toolkit called Hidden Markov Model (HMM) Toolkit is used to perform the experiment. The development of HMM based Automatic Speech Recognition (ASR) requires both text and speech corpus for training and testing the HMM. In order to have a model that incorporates different features of the language, we included the different dialects of Kafi Noonoo in the corpus and then prepared the training and test corpus from the scratch, and after preprocessing we have sampled and performed feature extraction using Mel Frequency Cepstral Coefficients (MFCC) feature extraction technique.