• SRUTHIMOL A
• Dr Victor S P

Relational databases are step ahead of normal databases in which the process of accessing the data in particular requirement formats are little bit technically advanced when compare it with the normal procedure from users point of view. The relation databases helps the data viewers and handlers i an easier way of accessing the information's in order to save time and space. The accuracy and clarity the primary constraint in relational databases data extraction procedures. Reinforcement learning is the familiar strategy used in neural networks which can handle the signal and data process based on optimal output tightening by using the repeated training or reinforcing strategies to acquire the expected results. This paper deals with the process of extracting data from relational databases use the reinforcement learning approach for the accuracy and time saves strategies towards optimal output attainments. The mixture of neural reinforcement in database extraction procedures is improving the efficiency in terms of accuracy. In near future we will implement the fuzzy logic combined with neural networks approach in relational databases data extraction methodologies.

data mining, data extraction, relational database, reinforcement learning, optimality

"Semantic Data Extraction Towards Relational Databases Using Reinforcement Learning Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 3, page no.2107-2111, March-2020, Available :http://www.jetir.org/papers/JETIR2003300.pdf

"Semantic Data Extraction Towards Relational Databases Using Reinforcement Learning Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 3, page no. pp2107-2111, March-2020, Available at : http://www.jetir.org/papers/JETIR2003300.pdf