Accessing Databases Using Amharic Natural Language

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Addis Ababa University


Nowadays, day to day activities of human beings is highly dependent on the information distributed in every part of the world. One major source of the information, which is the collection of related data, is the database. To extract the information from the database, it is required to formulate a structured query language which is understood by the database engine. The SQL query is not known by everyone in the world as it requires studying and remembering its syntax and semantics. Only professionals who study the SQL can formulate the query to access the database. On the other hand, human beings communicate with each other using natural language. It would be easier to access the content of the database using that natural language which in turn contributes to the field of natural language interface to the database. Since in many private and public organizations, peoples are performing day to day activities in Amharic language and many of them are not skilled in formulating structured query language, it would be better if there is a mechanism by which the users can directly extract information from the database using the Amharic language. This research accepts questions that are written in Amharic natural language and converts to its equivalent structured query language. A dataset which consists of an input word that is tagged with the appropriate output variable is prepared. Features which represent the Amharic questions are identified and given to the classifier for training purpose. Stemmer, Morphological analyzer, and pre-processor prepare the input question in the format required by the classifier. To identify appropriate query elements, Query Element Identifier uses the dictionary which is prepared by applying the concept of semantic free grammar. The query constructor constructs the required SQL query using these identified query elements. A prototype called Amharic Database querying system is developed to demonstrate the idea raised by this research. Testers from different departments having different mother tongue language test the performance of the system.



Natural Language Interface to the Database, Stemmer, Morphological Analyzer, Pre-Processor, Query Element Identifier, Semantic Free Grammar, Query Constructor