Domain-specific knowledge graphs (DKGs) are critical yet often suffer from limited coverage compared to General Knowledge Graphs (GKGs). Existing tasks to enrich DKGs rely primarily on extracting knowledge from external unstructured data or completing KGs through internal reasoning, but the scope and quality of such integration remain limited. This highlights a critical gap: little systematic exploration has been conducted on how comprehensive, high-quality GKGs can be effectively leveraged to supplement DKGs. To address this gap, we propose a new and practical task: domain-specific knowledge graph fusion (DKGF), which aims to mine and integrate relevant facts from general knowledge graphs into domain-specific knowledge graphs to enhance their completeness and utility. Unlike previous research, this new task faces two key challenges: (1) high ambiguity of domain relevance, i.e., difficulty in determining whether knowledge from a GKG is truly relevant to the target domain , and (2) cross-domain knowledge granularity misalignment, i.e., GKG facts are typically abstract and coarse-grained, whereas DKGs frequently require more contextualized, fine-grained representations aligned with particular domain scenarios. To address these, we present ExeFuse, a neuro-symbolic framework based on a novel Fact-as-Program paradigm. ExeFuse treats fusion as an executable process, utilizing neuro-symbolic execution to infer logical relevance beyond surface similarity and employing target space grounding to calibrate granularity. We construct two new datasets to establish the first standardized evaluation suite for this task. Extensive experiments demonstrate that ExeFuse effectively overcomes domain barriers to achieve superior fusion performance.

翻译：领域特定知识图谱（DKG）虽然至关重要，但与通用知识图谱（GKG）相比，其覆盖范围往往有限。现有丰富DKG的任务主要依赖于从外部非结构化数据中提取知识或通过内部推理补全知识图谱，但此类整合的范围和质量仍然受限。这突显了一个关键空白：关于如何有效利用全面、高质量的GKG来补充DKG，尚缺乏系统性的探索。为填补这一空白，我们提出了一个新颖且实用的任务：领域特定知识图谱融合（DKGF），其目标是从通用知识图谱中挖掘并整合相关事实到领域特定知识图谱中，以增强其完整性和实用性。与先前研究不同，这一新任务面临两个关键挑战：（1）领域相关性的高度模糊性，即难以判断来自GKG的知识是否真正与目标领域相关；（2）跨领域知识粒度失配，即GKG事实通常是抽象且粗粒度的，而DKG往往需要与特定领域场景对齐的、更具情境化的细粒度表示。为解决这些问题，我们提出了ExeFuse，一个基于新颖的“事实即程序”范式的神经符号框架。ExeFuse将融合视为一个可执行过程，利用神经符号执行来推断超越表面相似性的逻辑相关性，并采用目标空间落地来校准粒度。我们构建了两个新的数据集，为该任务建立了首个标准化评估套件。大量实验表明，ExeFuse能有效克服领域壁垒，实现卓越的融合性能。