A few-shot knowledge reasoning method based on three-way partial order structure and prompt learning

As an emerging topic, few-shot knowledge reasoning is of great significance to the advancement of artificial intelligence. KR-POFSA is an approach that employs a three-way partial order structure for knowledge representation, combined with granular computing to achieve few-shot knowledge reasoning. However, it faces a limitation: when using it for knowledge reasoning, if the number of attributes is huge or there are a lot of cross-relationships between the attributes, then it may generate lots of new potential object patterns, most of which are useless. To solve this problem, this paper devises an innovative approach to improve KR-POFSA, which uses LLM to downstream few-shot knowledge reasoning tasks through appropriate prompt design. Specifically, we design a prompt template that guides LLM to output domain knowledge, such as a correlation matrix, and uses thresholds to limit the generation of invalid patterns. Through three experiments—with 8 objects and 9 attributes, 20 objects and 11 attributes, and 23 objects and 12 attributes, respectively—we demonstrate that our method can not only reduce the discovery of invalid attribute granules and object patterns in granular computing by 30 %–50 %, but also may offer practitioners insights into which attributes to prioritize, minimizing empiricism.