Knowledge-intensive tasks pose a significant challenge for Machine Learning (ML) techniques. Commonly adopted methods, such as Large Language Models (LLMs), often exhibit limitations when applied to such tasks. Nevertheless, there have been notable endeavours to mitigate these challenges, with a significant emphasis on augmenting LLMs through Knowledge Graphs (KGs). While KGs provide many advantages for representing knowledge, their development costs can deter extensive research and applications. Addressing this limitation, we introduce a framework for enriching embeddings of small-scale domain-specific Knowledge Graphs with well-established general-purpose KGs. Adopting our method, a modest domain-specific KG can benefit from a performance boost in downstream tasks when linked to a substantial general-purpose KG. Experimental evaluations demonstrate a notable enhancement, with up to a 44% increase observed in the Hits@10 metric. This relatively unexplored research direction can catalyze more frequent incorporation of KGs in knowledge-intensive tasks, resulting in more robust, reliable ML implementations, which hallucinates less than prevalent LLM solutions. Keywords: knowledge graph, knowledge graph completion, entity alignment, representation learning, machine learning

翻译：知识密集型任务对机器学习技术构成了重大挑战。常见方法，如大型语言模型，在此类任务中常表现出局限性。尽管如此，已有显著努力来缓解这些挑战，重点是通过知识图谱增强大型语言模型。虽然知识图谱在表示知识方面具有诸多优势，但其开发成本可能阻碍广泛的研究与应用。针对这一局限，我们提出了一种框架，利用成熟的通用知识图谱来丰富小规模领域特定知识图谱的嵌入。采用我们的方法，一个中等规模的领域特定知识图谱通过与大型通用知识图谱关联，可在下游任务中获得性能提升。实验评估显示显著改进，在Hits@10指标上观察到高达44%的提升。这一相对未充分探索的研究方向可促进知识图谱在知识密集型任务中更频繁地整合，从而产生更鲁棒、更可靠的机器学习实现，其幻觉现象少于主流的大型语言模型解决方案。关键词：知识图谱、知识图谱补全、实体对齐、表示学习、机器学习