  Nowadays, the rapid development of mobile economy has promoted the
flourishing of online marketing campaigns, whose success greatly hinges on the
efficient matching between user preferences and desired marketing campaigns
where a well-established Marketing-oriented Knowledge Graph (dubbed as MoKG)
could serve as the critical "bridge" for preference propagation. In this paper,
we seek to carefully prompt a Large Language Model (LLM) with domain-level
knowledge as a better marketing-oriented knowledge miner for marketing-oriented
knowledge graph construction, which is however non-trivial, suffering from
several inevitable issues in real-world marketing scenarios, i.e.,
uncontrollable relation generation of LLMs,insufficient prompting ability of a
single prompt, the unaffordable deployment cost of LLMs. To this end, we
propose PAIR, a novel Progressive prompting Augmented mIning fRamework for
harvesting marketing-oriented knowledge graph with LLMs. In particular, we
reduce the pure relation generation to an LLM based adaptive relation filtering
process through the knowledge-empowered prompting technique. Next, we steer
LLMs for entity expansion with progressive prompting augmentation,followed by a
reliable aggregation with comprehensive consideration of both self-consistency
and semantic relatedness. In terms of online serving, we specialize in a small
and white-box PAIR (i.e.,LightPAIR),which is fine-tuned with a high-quality
corpus provided by a strong teacher-LLM. Extensive experiments and practical
applications in audience targeting verify the effectiveness of the proposed
(Light)PAIR.
