  API recommendation methods have evolved from literal and semantic keyword
matching to query expansion and query clarification. The latest query
clarification method is knowledge graph (KG)-based, but limitations include
out-of-vocabulary (OOV) failures and rigid question templates. To address these
limitations, we propose a novel knowledge-guided query clarification approach
for API recommendation that leverages a large language model (LLM) guided by
KG. We utilize the LLM as a neural knowledge base to overcome OOV failures,
generating fluent and appropriate clarification questions and options. We also
leverage the structured API knowledge and entity relationships stored in the KG
to filter out noise, and transfer the optimal clarification path from KG to the
LLM, increasing the efficiency of the clarification process. Our approach is
designed as an AI chain that consists of five steps, each handled by a separate
LLM call, to improve accuracy, efficiency, and fluency for query clarification
in API recommendation. We verify the usefulness of each unit in our AI chain,
which all received high scores close to a perfect 5. When compared to the
baselines, our approach shows a significant improvement in MRR, with a maximum
increase of 63.9% higher when the query statement is covered in KG and 37.2%
when it is not. Ablation experiments reveal that the guidance of knowledge in
the KG and the knowledge-guided pathfinding strategy are crucial for our
approach's performance, resulting in a 19.0% and 22.2% increase in MAP,
respectively. Our approach demonstrates a way to bridge the gap between KG and
LLM, effectively compensating for the strengths and weaknesses of both.
