Vezvaei, Alireza2022-09-162023-01-152022-09-162022-09-06http://hdl.handle.net/10012/18750Knowledge Graphs (KGs) have many applications, specifically in information retrieval and question answering. Community projects are conducted for building large-scale KGs with crowdsourcing, but building KGs with this approach is costly and sometimes infeasible. Considering the rapidly growing amount of unstructured text on the Web, we highly need systems for automatic KG generation. We propose KG-Pipeline, a general-purpose end-to-end pipeline designed for automatically constructing KGs from unstructured text documents. We leverage state-of-the-art NLP models for implementing various components of the pipeline. We also utilize our generated KGs in Question Answering (QA) and evaluate the performance of our system on a QA benchmark, comparing it to previous work and an information retrieval baseline model.enknowledge graphquestion answeringinformation extractionNLPnatural language processingMaster Thesis