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[2601.10485] Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge
Summary
The paper proposes a novel approach for enhancing domain-specific knowledge graphs (DKGs) by integrating general knowledge graphs (GKGs) through a framework called ExeFuse, addressing challenges of relevance and granularity.
Why It Matters
This research highlights a significant gap in knowledge graph integration, offering a systematic method to improve DKGs' completeness and utility. By leveraging general knowledge, the findings could enhance various applications in AI and data science, making this work crucial for advancing knowledge representation.
Key Takeaways
- Introduces domain-specific knowledge graph fusion (DKGF) as a new task.
- Presents ExeFuse, a neuro-symbolic framework for effective knowledge integration.
- Addresses challenges of relevance and granularity in knowledge graph fusion.
- Constructs new datasets for standardized evaluation of DKGF.
- Demonstrates superior fusion performance through extensive experiments.
Computer Science > Artificial Intelligence arXiv:2601.10485 (cs) [Submitted on 15 Jan 2026 (v1), last revised 13 Feb 2026 (this version, v3)] Title:Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge Authors:Runhao Zhao, Weixin Zeng, Wentao Zhang, Chong Chen, Zhengpin Li, Xiang Zhao, Lei Chen View a PDF of the paper titled Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge, by Runhao Zhao and 6 other authors View PDF HTML (experimental) Abstract: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 dom...