The development and integration of knowledge graphs and language models has significance in artificial intelligence and natural language processing. In this study, we introduce the BERTologyNavigator -- a two-phased system that combines relation extraction techniques and BERT embeddings to navigate the relationships within the DBLP Knowledge Graph (KG). Our approach focuses on extracting one-hop relations and labelled candidate pairs in the first phases. This is followed by employing BERT's CLS embeddings and additional heuristics for relation selection in the second phase. Our system reaches an F1 score of 0.2175 on the DBLP QuAD Final test dataset for Scholarly QALD and 0.98 F1 score on the subset of the DBLP QuAD test dataset during the QA phase.

翻译：知识图谱与语言模型的开发与融合在人工智能和自然语言处理领域具有重要意义。本研究提出了BERTologyNavigator——一种两阶段系统，它结合关系抽取技术与BERT嵌入，以探索DBLP知识图谱中的关系。我们的方法在第一阶段侧重于提取单跳关系及标记候选对，随后在第二阶段利用BERT的CLS嵌入及额外启发式策略进行关系选择。该系统在DBLP QuAD最终测试数据集（用于学术QALD任务）上达到了0.2175的F1值，而在问答阶段的DBLP QuAD测试数据子集上则取得了0.98的F1值。