Context Derivation for Knowledge Graph Expansion Created by: Haard Rao, Rohit Sanjay, Neel Khakhar Role of Haard Rao: Knowledge graph implementation Role of Rohit Sanjay: Knowledge graph implementation Role of Neel Khakhar: Reference paper implementation and coding Humans use language as a form of communication to think, speak, write, decide, etc; but that’s not a case with computers. Humans have to give some kind of input for it to understand. Through all this, humans can relate to different entities and make connection from different sentences but again, computers cannot do this. So, we decided to use our coding skills and a few open sources to give these abilities to computers. For doing so we used NLP so that computer understands English language and used spaCy LLM which is a cython based library for NLP developments. Here we decided two approaches. First implementation was directly from paper but it was confined to Q&A and very heavy to implement it. We used social IQA dataset for this particular approach. The second implementation was for everyday use and used wiki_sentences v2 dataset. These gave output from the queries passed through it. We get a knowledge graph as an outcome of both the implementations.
SRC has implementation of Dynamic Neuro Symbolic Knowledge Graph generation for Zero-shot question answering and knowledgegraph.ipynb is created by us