Authors:
Kosuke Yano
1
;
Yoshinobu Kitamura
2
and
Kazuhiro Kuwabara
2
Affiliations:
1
Graduate School of Information Science and Engineering, Ritsumeikan University, Ibaraki, Osaka, 567-8570, Japan
;
2
College of Information Science and Engineering, Ritsumeikan University, Ibaraki, Osaka, 567-8570, Japan
Keyword(s):
Function Decomposition Tree, Retrieval-Augmented Generation, Large Language Model.
Abstract:
A search method leveraging Retrieval-Augmented Generation (RAG) for goal-oriented knowledge graphs is proposed, with a specific focus on function decomposition trees. A function decomposition tree represents hierarchically functions of artifacts or actions of human with explicit descriptions of purposes and goals. We developed a schema to convert the trees into RDF, enabling structured and efficient searches. Through RAG technology, a natural language interface converts user’s inputs into SPARQL queries, retrieving relevant data and subsequently presenting them in an accessible and chat-based format. Such a flexible, and purpose-driven searches enhance usability in complex knowledge graphs. We demonstrate the tool effectively retrieves actions, intentions, and dependencies using an illustrative and a real-world example of function decomposition trees.