Commonsense knowledge graph acquisition (CKGA) is essential for many knowledge-intensive applications, such as natural language understanding, question answering, and conversational system. Traditional methods that directly use word-level triplet for knowledge generation often lack sufficient context, leading to ambiguous relations and a limited ability to handle complex or abstract concepts. Moreover, they also rely on forward relations and struggle to fully capture the reverse connections between entities, which can lead to the "reversal curse". To address these, we firstly transform pre-defined relations into sentence templates, and introduce a new pipeline that enhances CKGA task via bi-directional relation-enhanced contrastive pretraining from recent large-scale foundation models. Our closed-loop Bi-REACT includes data preprocessing, contrastive pre-training, task-driven instruction tuning, filtering model, and evaluation system. Experiments show Bi-REACT can easily harvest extensive high-quality knowledge (390K), achieving up to 90.02% (ATOMIC) and 84.29% (ConceptNet) accuracy, approaching human-level performance for these resources, and effectively reducing the "reversal curse" issues. Our code is available at https://anonymous.4open.science/r/CKGA-345E.

Conventional commonsense knowledge graph completion (CKGC) methods provide inadequate sequence when fine-tuning or generating stages and incorporate full fine-tuning, which fail to align with the autoregressive model's pre-training patterns and have insufficient parameter efficiency. Moreover, decoding through beam or greedy search produces low diversity and high similarity in generated tail entities. Hence, we resort to prefix-tuning and propose a lightweight, effective pipeline to enhance the quality and diversity of extracted commonsense knowledge. Precisely, we measure head entity similarity to yield and then concatenate top-k tuples before each target tuple for prefix-tuning the source LM, thereby improving the efficiency and speed for pretrained models; then, we design a penalty-tailored diverse beam search (p-DBS) for decoding tail entities, producing a greater quantity and diversity of generated commonsense tuples; besides, a filter strategy is utilized to filter out invalid commonsense knowledge. Through extensive automatic evaluations, including ChatGPT scoring, our method can extract diverse, novel, and accurate commonsense knowledge (CK).