Improving Question Answering Systems Using Knowledge Graph Integration and NLP Techniques

QA systems have become the best-sought-after for correct and context-relevant information in one or the other domain of interest. Nevertheless, such systems have some drawbacks, for example they experience difficulties in solving multilevel queries, misunderstanding anamolies, and giving correct answers. This work aims at improving QA systems by incorporating KGs with the state-of-the-art Natural Language Processing (NLP) tools. The use of Knowledge Graphs in the system leads to the enhancement of comprehension of the context and relationship of entities increasing the relevance of answers given. There are techniques for mapping the query terms to the appropriate entities in the KG, techniques for identifying relations that associate queried terms, and transformer models, including BERT and GPT, for contextual semiotics. Also, query rewriting and semantic parsing approaches are used for enhancing the user query to get more relevant to the KG data. A class of graph neural networks (GNNs) are used to perform improved comparative analysis and multi-hop traversal of the graph. Question decomposition and attention mechanisms are used to split the given question into separate sub-questions, and also to point at preferable parts of the knowledge graph. The proposed system proves to handle complex and multi-hop queries far much better compared to the existing systems with marked improvements on the accuracy of results, response time and end user satisfaction. The experimental outcomes corroborate the effectiveness of the approach and demonstrate its applicability to development for open-domain and specific domain QA systems.