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

通过将小规模专业领域知识图谱与已建立的通用知识图谱进行关联，我们提出了一个框架来丰富领域特定知识图谱的嵌入，以应对大规模知识密集型任务中存在的挑战。实验评估表明，通过与大规模通用知识图谱的链接，采用我们的方法可以显著提升下游任务的性能，Hit@10指标的提升可达44%。这一相对未经探索的研究方向可以促使知识图谱在知识密集型任务中更频繁地应用，从而得到更强大、可靠且不那么虚幻的机器学习实现。

赋能小规模知识图谱：利用通用知识图谱实现丰富的嵌入策略