Semantic Rules Extraction from a Database using Data Mining

(Ahmad Badr al-Din and Mohammed Khader DBAs Hamid and Lina Mohamed Farouk Baaj)

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Abstract

Automatic English text semantic validation is rarely explored, in the recent decade. In this context the target of this paper is to demonstrate a model for generating semantic rules from a database containing the most frequent words in the English to validate the meaning of English sentences. To construct this model we used FP Growth algorithm, where the association rules between words in the database are extracted. The output of this algorithm ignores the sequence of words within the rules. But the proposed system focuses on the sequence of words within the rules and considers it necessary to achieve semantic analysis, so the FP Growth algorithm was modified to get association rules which give importance to the sequence of words. Since the natural languages processing is constantly evolving process, this research makes the achievement of semantic analysis applied by data mining, and it is considered as an important step to display the importance of Informatics researches in building systems which deal with natural languages and simulate the human expert in English. Key Words: Association rules, database, data mining,, FP Growth algorithm, semantic analysis, semantic rules.
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