Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge

Runhao Zhao; Weixin Zeng; Wentao Zhang; Chong Chen; Zhengpin Li; Xiang Zhao; Lei Chen

cs.AIJan 15, 2026

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

Organizations: National Key Laboratory of Big Data and Decision, National University of Defense Technology, Changsha, China · The Center for machine learning research, Peking University, Beijing, China · Tsinghua University, Beijing, China · The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China

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 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 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.

Figures & tables

Figure 1. Examples of domain-specific knowledge graph fusion. Figure 1 (b) demonstrates the fusion of highly relevant entities from GKG into DKG. Figure 1 (c) shows the augmented DKG.

Figure 2. The framework of ExeFuse for DKGF.

Figure 3. Comprehensive evaluation across diverse domain-specific knowledge graphs on DKGF(W-Drug) , DKGF(W-UMLS) , DKGF(D-Alex) and DKGF(D-Crunch) .

Models	Split	Seen		Unseen		All
Models	Split	ACC	F1	ACC	F1	ACC	F1
DistMult-F	S1	0.563	0.623	0.491	0.340	0.554	0.585
	S2	0.548	0.602	0.485	0.321	0.536	0.570
	Avg.	0.556	0.613	0.488	0.331	0.545	0.578
Self-RAG-F	S1	0.565	0.570	0.594	0.372	0.576	0.544
	S2	0.552	0.565	0.581	0.366	0.544	0.545

Table 4. Transferability and resource robustness on DKGF(W-I) . We report performance on Seen and Unseen entities across different resource splits. “ Avg. ” denotes the average performance across S1 and S2.

Variant	DKGF(W-I) -S1			$\Delta$
Variant	ACC	F1	Avg.	$\Delta$
ExeFuse (Full Model)	0.680	0.690	0.685	-
I. Complexity Validation (Is the complex pipeline necessary?)
w/o Rules $\mathcal{L}$ (Single Affine Projection)	0.605	0.612	0.609	-11.1%
w/o Reasoning (Direct Classification)	0.577	0.583	0.580	-15.3%
II. Module Necessity (Do all components contribute?)

Table 5. Ablation study and design verification on DKGF(W-I) -S1 . “ Avg. ”: average of ACC and F1. “

\Delta

”: relative performance drop in Avg.

Settings	DKGF(W-I) -S1 (Accuracy)			Cost (Efficiency and Effectiveness)
Settings	ACC	F1	Avg.	Avg. tokens	Avg. time (seconds)
Self-Consistency-F	0.592	0.561	0.577	41,561,586	178,041.6
ChatEA-F	0.596	0.566	0.581	48,162,276	254,417.4
TransH-F	0.640	0.591	0.583	0	55.7
SimpleHHEA-F	0.490	0.499	0.495	0	2628.2
ExeFuse (Ours)	0.680	0.690	0.685	0	42.6

Table 6. Accuracy and cost analysis of ExeFuse and benchmark configurations on DKGF(W-I) -S1. Avg.tokens / Avg.time : the average tokens/time cost of the model.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Models	Split	Seen		Unseen		All
Models	Split	ACC	F1	ACC	F1	ACC	F1
DistMult-F	S1	0.531	0.564	0.472	0.315	0.525	0.536
	S2	0.582	0.591	0.490	0.342	0.575	0.562
	Avg.	0.557	0.578	0.481	0.329	0.550	0.549
Self-RAG-F	S1	0.602	0.585	0.582	0.334	0.594	0.540
	S2	0.585	0.550	0.601	0.355	0.577	0.532

Appendix

Table 7. Transferability and resource robustness on DKGF(Y-I) . Metrics are averaged across splits to demonstrate fusion stability under varying resource constraints.

Figure 4. Transferability results (F1) across multiple domains (Biomedical, Academic, and Business).

Figure 5. The venn diagram of correct predictions on DKGF(W-I) -S1. Intersections show shared same predictions.

Models	DKGF(W-I) -S1					DKGF(Y-I) -S1
Models	ACC	P	R	F1	Avg.	ACC	P	R	F1	Avg.
DistMult-F	0.975	0.830	0.840	0.835	0.905	0.946	0.853	0.730	0.787	0.866
Self-RAG-F	0.970	0.849	0.731	0.785	0.878	0.936	0.897	0.603	0.721	0.829
ChatEA-F	0.973	0.888	0.737	0.806	0.889	0.938	0.937	0.583	0.719	0.828
ExeFuse	0.982	0.968	0.791	0.871	0.927	0.958	0.958	0.721	0.823	0.890

Appendix

Table 8. Performance analysis of the new task “Relevant Entity Finding (REF)”.

Knowledge Graphs

Knowledge graphs represent entities and relationships as structured, queryable networks for integrating data and supporting reasoning.

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Multi-domain knowledge graph completion (MKGC) aims to improve missing triple prediction in a target KG by transferring knowledge from other support KGs. Existing methods typically enforce consistency constraints on equivalent entities across KGs to transfer knowledge, which risks suppressing domain-specific contextual information of entities. This design can also compromise entity representation information from all KG domains, impeding performance improvements, especially in low-resource data scenarios. To address this, we pioneer a generation-based paradigm for MKGC and propose DMKGC, a conditional diffusion-guided knowledge transfer framework. Our key insight is to treat each KG as a partial view of the entity entire information, and generate informative domain-general entity embeddings through diffusion models conditioned on support KGs. Particularly, we first initialize domain-agnostic entity embeddings as prior entity embeddings, and then encode them within individual KGs. Afterward, we fuse equivalent entities from support KGs as the conditional diffusion generation guidance. We leverage the prior entity embeddings as the proxy generation objective, which ensures this conditional generation to be unbiased towards any conditioned KGs. Simultaneously, we also train the generated embeddings to be predictive across KGs, thus preserving domain-specific information. Extensive experiments on 14 KGs in 3 benchmarks demonstrate a 4.3% average MRR improvement in tail entity prediction over state-of-the-art methods, with sustained gains in low-resource data settings.

Abstract

Knowledge Graphs

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