  Novice researchers often face difficulties in understanding a multitude of
academic papers and grasping the fundamentals of a new research field. To solve
such problems, the knowledge graph supporting research survey is gradually
being developed. Existing keyword-based knowledge graphs make it difficult for
researchers to deeply understand abstract concepts. Meanwhile, novice
researchers may find it difficult to use ChatGPT effectively for research
surveys due to their limited understanding of the research field. Without the
ability to ask proficient questions that align with key concepts, obtaining
desired and accurate answers from this large language model (LLM) could be
inefficient. This study aims to help novice researchers by providing a
fish-bone diagram that includes causal relationships, offering an overview of
the research topic. The diagram is constructed using the issue ontology from
academic papers, and it offers a broad, highly generalized perspective of the
research field, based on relevance and logical factors. Furthermore, we
evaluate the strengths and improvable points of the fish-bone diagram derived
from this study's development pattern, emphasizing its potential as a viable
tool for supporting research survey.
