Knowledge graph embedding (KGE) has caught significant interest for its effectiveness in knowledge graph completion (KGC), specifically link prediction (LP), with recent KGE models cracking the LP benchmarks. Despite the rapidly growing literature, insufficient attention has been paid to the cooperation between humans and AI on KG. However, humans' capability to analyze graphs conceptually may further improve the efficacy of KGE models with semantic information. To this effect, we carefully designed a human-AI team (HAIT) system dubbed KG-HAIT, which harnesses the human insights on KG by leveraging fully human-designed ad-hoc dynamic programming (DP) on KG to produce human insightful feature (HIF) vectors that capture the subgraph structural feature and semantic similarities. By integrating HIF vectors into the training of KGE models, notable improvements are observed across various benchmarks and metrics, accompanied by accelerated model convergence. Our results underscore the effectiveness of human-designed DP in the task of LP, emphasizing the pivotal role of collaboration between humans and AI on KG. We open avenues for further exploration and innovation through KG-HAIT, paving the way towards more effective and insightful KG analysis techniques.

翻译：知识图谱嵌入（KGE）因其在知识图谱补全（KGC）中的有效性而备受关注，尤其在链接预测（LP）任务中，近期KGE模型已攻克LP基准测试。尽管相关文献迅速增长，但人类与AI在知识图谱上的协作尚未得到足够重视。然而，人类对图谱的概念性分析能力可能通过语义信息进一步提升KGE模型的效能。为此，我们精心设计了一个名为KG-HAIT的人机协作（HAIT）系统，该系统通过充分利用人类在知识图谱上设计的特定动态规划（DP）方法，提取人类洞察特征（HIF）向量，以捕获子图结构特征与语义相似性。将HIF向量融入KGE模型训练后，我们观察到各项基准测试与评估指标的显著提升，同时模型收敛速度加快。我们的结果凸显了人类设计的DP在链接预测任务中的有效性，强调了人类与AI在知识图谱上协作的关键作用。通过KG-HAIT，我们为知识图谱分析技术的进一步探索与创新开辟了道路，朝着更高效、更具洞察力的分析技术迈进。