Domain-specific knowledge graphs (DKGs) often lack coverage compared to general knowledge graphs (GKGs). To address this, we introduce Domain-specific Knowledge Graph Fusion (DKGF), a novel task that enriches DKGs by integrating relevant facts from GKGs. DKGF faces two key challenges: high ambiguity in domain relevance and misalignment in knowledge granularity across graphs. We propose ExeFuse, a simple yet effective Fact-as-Program paradigm. It treats each GKG fact as a latent semantic program, maps abstract relations to granularity-aware operators, and verifies domain relevance via program executability on the target DKG. This unified probabilistic framework jointly resolves relevance and granularity issues. We construct two benchmarks, DKGF(W-I) and DKGF(Y-I), with 21 evaluation configurations. Extensive experiments validate the task's importance and our model's effectiveness, providing the first standardized testbed for DKGF.

翻译：领域特定知识图谱（DKG）在覆盖范围上通常不及通用知识图谱（GKG）。为解决此问题，我们提出了领域特定知识图谱融合（DKGF）这一新任务，旨在通过整合来自GKG的相关事实来丰富DKG。DKGF面临两个关键挑战：领域相关性的高度模糊性，以及不同图谱间知识粒度的不匹配。我们提出了ExeFuse，一种简单而有效的“事实即程序”范式。它将每个GKG事实视为一个潜在的语义程序，将抽象关系映射到粒度感知的算子，并通过程序在目标DKG上的可执行性来验证领域相关性。这一统一的概率框架共同解决了相关性和粒度问题。我们构建了两个基准测试集DKGF(W-I)和DKGF(Y-I)，包含21种评估配置。大量实验验证了该任务的重要性以及我们模型的有效性，为DKGF提供了首个标准化测试平台。