Intelligent Personal Assistant with Knowledge Navigation
This work addresses the need for more reliable and efficient personal assistants for general users, but it appears incremental as it builds on existing chatbot and knowledge navigation techniques.
The paper tackles the problem of creating an intelligent personal assistant that helps users find information efficiently by using knowledge navigation and responding to queries via multiple input methods, with the goal of providing human-like responses based on multiple SRT files and web scraping.
An Intelligent Personal Agent (IPA) is an agent that has the purpose of helping the user to gain information through reliable resources with the help of knowledge navigation techniques and saving time to search the best content. The agent is also responsible for responding to the chat-based queries with the help of Conversation Corpus. We will be testing different methods for optimal query generation. To felicitate the ease of usage of the application, the agent will be able to accept the input through Text (Keyboard), Voice (Speech Recognition) and Server (Facebook) and output responses using the same method. Existing chat bots reply by making changes in the input, but we will give responses based on multiple SRT files. The model will learn using the human dialogs dataset and will be able respond human-like. Responses to queries about famous things (places, people, and words) can be provided using web scraping which will enable the bot to have knowledge navigation features. The agent will even learn from its past experiences supporting semi-supervised learning.