{"id":{"repo_id":"lethbridge","oai_identifier":"oai:opus.uleth.ca:10133/7066"},"canonical_url":"https://search.dev.ndltd.org/etd/lethbridge/oai:opus.uleth.ca:10133/7066","repository":{"repo_id":"lethbridge","name":"University of Lethbridge","base_url":"https://opus.uleth.ca/server/oai/request"},"display":{"title":"KG4QG: combining knowledge graph with large language models for multi-hop question generation","abstract":"Question generation is a task of Natural Language Processing where the goal is to generate fluent, grammatically correct, and error-free questions based on a given input context and optionally an answer. Multi-hop question generation is a more complex task compared to traditional single-hop question generation, as it requires reasoning over multiple information from multiple input contexts in generating multi-hop questions. In our work, we have addressed the challenge of building a multi-hop question generation system by combining the knowledge graphs with Large Language Models (LLMs). We have designed a framework KG4QG(Knowledge Graph for Question Generation), where knowledge graphs are generated from the input contexts. For the knowledge graph embedding, we use a Graph Attention Network, and for input texts embedding, we leverage a Sentence Transformer. Finally, we apply the BART and T5 models as Large Language Models to generate multi-hop questions from our proposed model. Using the HotpotQA dataset to evaluate the performance of our KG4QG framework, our proposed methodology shows enhanced performance over the previous methodologies","abstract_html":"Question generation is a task of Natural Language Processing where the goal is to generate fluent, grammatically correct, and error-free questions based on a given input context and optionally an answer. Multi-hop question generation is a more complex task compared to traditional single-hop question generation, as it requires reasoning over multiple information from multiple input contexts in generating multi-hop questions. In our work, we have addressed the challenge of building a multi-hop question generation system by combining the knowledge graphs with Large Language Models (LLMs). We have designed a framework KG4QG(Knowledge Graph for Question Generation), where knowledge graphs are generated from the input contexts. For the knowledge graph embedding, we use a Graph Attention Network, and for input texts embedding, we leverage a Sentence Transformer. Finally, we apply the BART and T5 models as Large Language Models to generate multi-hop questions from our proposed model. Using the HotpotQA dataset to evaluate the performance of our KG4QG framework, our proposed methodology shows enhanced performance over the previous methodologies","abstract_has_math":false,"creators":["Mahamud, Al Hasib","University of Lethbridge. Faculty of Arts and Science"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-27T20:02:26Z","subjects":["Question generation","Large Language Models","Graph Attention Network","Multi-hop question","Knowledge graph"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/7066"],"render_values":[{"text":"hdl:10133/7066","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Question generation","Large Language Models","Graph Attention Network","Multi-hop question","Knowledge graph"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/7066"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["Question generation is a task of Natural Language Processing where the goal is to generate fluent, grammatically correct, and error-free questions based on a given input context and optionally an answer. Multi-hop question generation is a more complex task compared to traditional single-hop question generation, as it requires reasoning over multiple information from multiple input contexts in generating multi-hop questions. In our work, we have addressed the challenge of building a multi-hop question generation system by combining the knowledge graphs with Large Language Models (LLMs). We have designed a framework KG4QG(Knowledge Graph for Question Generation), where knowledge graphs are generated from the input contexts. For the knowledge graph embedding, we use a Graph Attention Network, and for input texts embedding, we leverage a Sentence Transformer. Finally, we apply the BART and T5 models as Large Language Models to generate multi-hop questions from our proposed model. Using the HotpotQA dataset to evaluate the performance of our KG4QG framework, our proposed methodology shows enhanced performance over the previous methodologies"]},{"key":"dc:title","label":"Title","values":["KG4QG: combining knowledge graph with large language models for multi-hop question generation"]}]}],"canonical_facts":{"dc:date.issued":["2025"],"dc:description.other":["Question generation is a task of Natural Language Processing where the goal is to generate fluent, grammatically correct, and error-free questions based on a given input context and optionally an answer. Multi-hop question generation is a more complex task compared to traditional single-hop question generation, as it requires reasoning over multiple information from multiple input contexts in generating multi-hop questions. In our work, we have addressed the challenge of building a multi-hop question generation system by combining the knowledge graphs with Large Language Models (LLMs). We have designed a framework KG4QG(Knowledge Graph for Question Generation), where knowledge graphs are generated from the input contexts. For the knowledge graph embedding, we use a Graph Attention Network, and for input texts embedding, we leverage a Sentence Transformer. Finally, we apply the BART and T5 models as Large Language Models to generate multi-hop questions from our proposed model. Using the HotpotQA dataset to evaluate the performance of our KG4QG framework, our proposed methodology shows enhanced performance over the previous methodologies"],"dc:identifier":["hdl:10133/7066"],"dc:subject":["Question generation","Large Language Models","Graph Attention Network","Multi-hop question","Knowledge graph"],"dc:title":["KG4QG: combining knowledge graph with large language models for multi-hop question generation"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:02:26Z"}