Semantic Similarity Through Primitive Heterogeneous Query Classification in Medical Emergency and Remedy Engine

Abstract

In medical emergency situations, providing the remedies and treatment in real time is important for saving someone's life. The target of this research work is to define a methodology, by which a person in need can be benefited. The research work is about processing the queries which are medically related and providing the remedies, immediate treatments and other useful information through which a person's life can be saved. The research project definition focuses on defining the methods and algorithms through which useful information can be retrieve from heterogeneous knowledge database and producing life saving information in real time. The query which will be passed to the engine will be native query. Algorithms will then be applied to this native query to further process the query, and make the decision which query need to be send to which database. Semantic similarities are then applied on the query and generate an output query, which then can be passed to heterogeneous knowledge databases to retrieve the useful data. The data retrieved would be similar to as a medical practitioner uses his judgments to get the remedies to the person. On the broader level we will have contact database, remedy database, current situation database. After receiving the data from different databases the data is processed and structured as defined in medical science, so that the person can take maximum benefit from the information generated. All these task and processing of the query should be done in real time. Present work focuses on optimization of the query and generating the result but no time constraint on the basis of the application domain.

This research work offers study of existing methods and algorithms and overcome their challenges of processing the queries and generates the algorithms which would generate the output in real time.