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

2026-01-15

AI Summary

This work addresses the challenge of effectively integrating facts from general-purpose knowledge graphs into domain-specific knowledge graphs, which is often hindered by insufficient coverage, ambiguous domain relevance, and misaligned knowledge granularity. To this end, the paper introduces the Domain Knowledge Graph Fusion (DKGF) task and proposes a novel “Fact-as-Program” paradigm, wherein triples from general knowledge graphs are modeled as semantic programs whose executability over the target domain graph determines both relevance and granularity alignment. The approach employs a probabilistic framework that maps relations to granularity-aware operators, jointly resolving relevance and granularity consistency. Furthermore, the authors construct the first standardized DKGF benchmark, comprising DKGF(W-I) and DKGF(Y-I), which encompasses 21 evaluation configurations to provide a comprehensive and reproducible testbed for this emerging task.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Description Logics
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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 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.

Problem

• Domain-specific Knowledge Graph
• Knowledge Graph Fusion
• Domain Relevance
• Knowledge Granularity
• General Knowledge Graph

Innovation

• Domain-specific Knowledge Graph Fusion
• Fact-as-Program
• Knowledge Graph Alignment
• Granularity-aware Operators
• Program Executability