Extracting and Utilizing templates for Question Answering over Knowledge Graph

Knowledge Graphs have been used in many Artificial Intelligence applications worldwide. In this thesis we firstly see how to map structured and semi-structured data from heterogeneous sources to a Resource Data Framework(RDF) based Knowledge Graph(KG) using a unique generic process. Having built an understanding of the ontologies, we move our focus to one of the applications of Knowledge Graphs to answer real world natural language questions. As the main contribution of this thesis, we establish a template based approach to Question Answering over Wikidata Knowledge Graph. We extract question templates from some common QA benchmark datasets and then build a classification model to fetch resultant template for a user question. This method aids the QA System with more faithful semantic interpretation of the user questions into formal query. The resultant template helps relation linking and query building modules of our system thereby reducing the propagation of error in NLP pipelines that can occur for a non-template based traditional QA System. To the best of our knowledge, we are the first to use this template based approach using LCQuAD2[1] and CFQ[2] datasets, that focus on more complex questions and compositional generalization respectively.