Knowledge Graphs (KGs) are a type of knowledge representation that gained a lot of attention due to their ability to store information in a structured format. This structure representation makes KGs naturally suited for search engines and NLP tasks like question-answering (QA) and task-oriented systems; however, KGs are hard to construct. While QA datasets are more available and easier to construct, they lack structural representation. This availability of QA datasets made them a rich resource for machine learning models, but these models benefit from the implicit structure in such datasets. We propose a framework to make this structure more pronounced and extract KG from QA datasets in an end-to-end manner, allowing the system to learn new knowledge in incremental learning with a human-in-the-loop (HITL) when needed. We test our framework using the SQuAD dataset and our incremental learning approach with two datasets, YAGO3-10 and FB15K237, both of which show promising results.