# Graph based Question Answering System

## TL;DR

This paper presents a graph-based system for extracting and retrieving structured information from unstructured textual data, facilitating faster and more relevant text analytics.

## Contribution

It introduces a novel approach to convert unstructured text into graph structures for improved information retrieval.

## Key findings

- Effective conversion of unstructured text to graph data.
- Enhanced retrieval performance using graph-based methods.
- Potential for scalable text analytics applications.

## Abstract

In today's digital age in the dawning era of big data analytics it is not the information but the linking of information through entities and actions which defines the discourse. Any textual data either available on the Internet off off-line (like newspaper data, Wikipedia dump, etc) is basically connect information which cannot be treated isolated for its wholesome semantics. There is a need for an automated retrieval process with proper information extraction to structure the data for relevant and fast text analytics. The first big challenge is the conversion of unstructured textual data to structured data. Unlike other databases, graph databases handle relationships and connections elegantly. Our project aims at developing a graph-based information extraction and retrieval system.