Proposed an Optimal Search Algorithm to Find the Best Answer in a Question Answering Systems

QA systems extract answers in natural language question from a large set of documents. In this paper, we will design and implement Restricted Domain QA System based on a knowledge database. In this system we will use a genetic algorithm and optimal-genetic algorithm to search in the knowledge base for finding the answers. Web pages are sources of knowledge system. To validate the proposed approach, we will implement these algorithms; results indicate a significant increase in accuracy of the proposed system compare to previous systems

Genetic Algorithms for data-driven Web Question Answering

We present an evolutionary approach for the computation of exact answers to Natural Languages (NL) questions. Answers are extracted directly from the N–best snippets, which have been identified by a standard web search engine using NL questions. The core idea of our evolutionary approach to web question answering is to search for those substrings in the snippets, whose contexts are most similar to contexts of already known answers. This context model together with the words mentioned in the NL question are used to evaluate the fitness of answer candidates, which are actually randomly selected substrings from randomly selected sentences of the snippets. New answer candidates are then created by applying specialised operators for crossover and mutation, which either stretch and shrink the substring of an answer candidate or transpose the span to new sentences. Since we have no predefined notion of patterns, our context alignment methods are very dynamic and strictly data-driven. We assessed our system with seven different data sets of question/answer pairs. The results show that this approach is promising, especially when it deals with specific questions