Improvement of Chatbots Semantics Using Wit.ai and Word Sequence Kernel: Education Chatbot as a Case Study

Abstract

Designing interactive question-response systems has become an important challenge in Artificial Intelligence which aims to build a computer program, referred to as Chatbot, able to manage an online humancomputer conversation with natural language. The number of chatbots continues to increase these recent years using different languages, tools and platforms and has been used in several domains, such as marketing, education and medicine. One of the most important issues in designing chatbots is its degree of interoperability with human natural language. In this context, we propose in this work a new messenger chatbot design approach based on Wit.ai and Word Sequence kernel in order to improve semantics. Wit.ai is used to detect contexts and concepts while the Word Sequence Kernel is used as a similarity measure between textual conversations taking into account the order of appearance of words in the conversation. A testing educate chatbot has been build which aims to provide FAQBot system for university students and acts as undergraduate advisor in student information desk. The performance of the proposed chatbot was compared to conventional messenger chatbots and showed better results.

Building an Arabic Flight Booking Dialogue System Using a Hybrid Rule-Based and Data Driven Approach

“…Moreover, many DSs have been developed within DS development frameworks [36], [37]. Qaffas [36] attempted to improve chatbot semantics using Wit.ai and word sequence kernel.…”

“…Moreover, many DSs have been developed within DS development frameworks [36], [37]. Qaffas [36] attempted to improve chatbot semantics using Wit.ai and word sequence kernel. In that study, the author designs a new approach that uses Wit.ai to search for entities in a user-entered query and word sequence kernel to compare the entered query's similarity with other queries in the database to return the most suitable response.…”

“…We were expecting such behavior. As Qaffas reported in [36], many people attempt to beat the bot or confuse it with difficult questions.…”

Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums

“…Wit.ai provides SDK in Node.js, Python, Ruby, and HTTP API. The use of Knowledge Graphs and databases is integrated with the use of APIs [101,118,127,[132][133][134]. There is no reference that wit.ai is supporting a distributed and collaborative multiple chatbots architecture, though it cannot be excluded by developers with the use of thirdparty components.…”

“…Another approach is proposing the improvement of chatbot semantics with the use of Wit.ai platform and Word Sequence Kernel techniques. The approach is exploiting Wit.ai NLU techniques by comparing user queries to stored user queries and providing a more accurate response [133]. HOLMes chatbot system is an IBM Watson chatbot on the domain of healthcare recommendation.…”