Abstract: To address the challenges of data scarcity, high annotation costs, and limitations of traditional relation extraction models in imaging logging, a few-shot relation extraction method based on the ConceptFERE(Concept- Enhanced Few-Ehot Relation Extraction) model is proposed. Using the BERT-PAIR(BERT-Paired Sentence Encoding) framework, the ConceptFERE model is improved and the SDG-ConceptFERE(Semantic Difference Gate-ConceptFERE) model is introduced. It includes a semantic difference gate mechanism that dynamically assesses the relevance between support and query instances. By incorporating external entity concepts into the support instances, the classification errors from incorrect semantic enhancement are prevented. Experiments show SDG-ConceptFERE improves accuracy by 3.57% and 2.78% over ConceptFERE in 5-way-1-shot and 5-way-5- shot tasks, proving its effectiveness in providing better text support for logging researchers and advancing intelligent decision-making in exploration and development. 

Key words: imaging logging, ConceptFERE model, few-shot learning, relation extraction, semantic differencegate mechanism