All It Takes is 20 Questions!: A Knowledge Graph Based Approach

TL;DR

This paper introduces a knowledge graph-based system for a 20 Questions game focused on Bollywood movies, using probabilistic learning and evolutionary methods to improve question efficiency and prediction accuracy.

Contribution

It presents a novel knowledge graph approach combined with probabilistic and evolutionary models for efficient question asking in a movie guessing game.

Findings

Predicted the correct movie in less than 10 questions over 50% of the time

Utilized a weighted knowledge graph that updates during gameplay

Demonstrated potential applications in disease detection and recommendation systems

Abstract

20 Questions (20Q) is a two-player game. One player is the answerer, and the other is a questioner. The answerer chooses an entity from a specified domain and does not reveal this to the other player. The questioner can ask at most 20 questions to the answerer to guess the entity. The answerer can reply to the questions asked by saying yes/no/maybe. In this paper, we propose a novel approach based on the knowledge graph for designing the 20Q game on Bollywood movies. The system assumes the role of the questioner and asks questions to predict the movie thought by the answerer. It uses a probabilistic learning model for template-based question generation and answers prediction. A dataset of interrelated entities is represented as a weighted knowledge graph, which updates as the game progresses by asking questions. An evolutionary approach helps the model to gain a better understanding of user choices and predicts the answer in fewer questions over time. Experimental results show that our model was able to predict the correct movie in less than 10 questions for more than half of the times the game was played. This kind of model can be used to design applications that can detect diseases by asking questions based on symptoms, improving recommendation systems, etc.