Facts are an important aspect of the web. Some of the popular knowledge bases like DBpedia, Freebase, Wikidata, etc. contain a lot of facts and there are many possible applications using them. In this research, we investigate methods to compare facts in a knowledge graph and analyze these facts using various approaches like combining graph embeddings and large language models.
A fundamental problem in information extraction is to extract relevant information like entities and their relations from unstructured text. This can be split into two seperate tasks: named entity recognition and relation extraction. We are working on the analysis of various existing approaches and also developing new efficient methods.
