# Internet Wizard for Enhancing Open-Domain Question-Answering Chatbot Knowledge Base in Education
## 2 Related Work  
The field of chatbot development has undergone significant advancements since the inception of the first chatbot, which relied on rule-based knowledge bases with limited scope and simplistic pattern matching. Numerous studies have explored the incorporation of offline and online external knowledge to enhance chatbot knowledge bases.  
In the offline approach, knowledge bases have been created using text corpora, such as didactic textbooks, through semi-automated processes such as Automatic Generation of AIML from Text Acquisition (AGATA). This involves human intervention at certain stages [34].  
Online approaches have also been investigated to expand chatbot knowledge bases. Researchers have explored knowledge extraction from online communities for tasks such as question-answering and summarization such as a knowledge base for a QA system that answers \"how\" questions was developed [35]. Additionally, a method was presented to detect question-answer pairs in email conversations for email summarization [36]. Furthermore, online communities such as forums have been utilized for automatic chatbot knowledge acquisition [37].  
In the context of the semantic web, chatbot systems leverage structured and linked data to facilitate conversations and perform various tasks such as question answering and FAQs. For instance, SOGO [38] is a semi-automatic social dialogue system that combines task-oriented utterances with conversational strategies to engage users in negotiation. Another example is La Liga [39], a social chatbot designed for the football domain, specifically catering to a diverse range of questions related to the Spanish football league. It uses an NLU block trained to extract intents and associated entities from user questions, obtaining information by making real-time SPARQL queries to the Wikidata knowledge base site. Additionally, KBot [40] utilizes semantic web techniques and linked data, making use of large-scale, publicly available knowledge bases such as DBpedia, Wikidata, and myPersonality.  
While these approaches capitalize on the characteristics of their respective corpora and tasks, they are limited by the scope and resources of their specific knowledge bases. Furthermore, although datasets derived from natural or crowdsourced data provide valuable resources for training dialogue generation models, they are static in nature and lack real-time updates or the ability to incorporate evolving information. This can lead to challenges in generating up-to-date and accurate responses, potentially resulting in outdated or inaccurate information.  
Notably, other studies [41; 42; 43] have explored the possibility of knowledge selection from a small set of knowledge, without employing a retrieval step or search engine, as was performed in this study. It is worth mentioning that the utilization of search engines for machine translation tasks has been shown to produce effective results [44]. The Wizard of Wikipedia task [45] involves conversations grounded in Wikipedia and utilizes a TFIDF retrieval model to find relevant information from the database. Similarly, our previous study [46] employed the Wikipedia API to retrieve related knowledge from Wikipedia and Canvas LMS and answer user queries. Those studies are perhaps the closest to this research work. However, this research work adopts a more comprehensive approach, leveraging publicly available internet information and combining data science and machine learning models with Google's search engine features, including feature snippets, knowledge graph, and organic search results, for dynamic chatbot knowledge acquisition, enabling the delivery of up to date and precise responses to user queries. To the best of our knowledge, no published literature comprehensively elucidates the utilization of search engines, specifically Google, and its associated features, in conjunction with data science and machine learning models, for the purpose of dynamic knowledge acquisition in question- answering chatbots.