Graph Fusion Across Languages using Large Language Models
This paper addresses cross-lingual knowledge graph fusion, where heterogeneous KGs in different languages must be unified without expensive manually-curated seed alignments. The core idea is to use Large Language Models as a universal semantic bridge by linearizing graph triplets into natural language sequences and sequentially agglomerating multiple graphs. This matters because it promises zero-shot alignment capability for low-resource languages where traditional embedding-based methods fail due to lack of training data.
This exploratory study convincingly demonstrates that LLMs can achieve high precision (88% with confidence filtering) in zero-shot entity alignment, but the work remains a preliminary proof-of-concept with severe limitations. The evaluation contradicts the paper's framing: despite claiming coverage of Chinese-English, Japanese-English, and French-English pairs, results are reported only for the Chinese-English subset. With recall at a critically low 23.6% and no quantitative comparison against recent LLM-based baselines like ZeroEA or Seg-Align, the practical utility for N-graph fusion remains unproven.
The modular pipeline architecture is well-designed, particularly the entity-centric partitioning that preserves topological neighborhood context within LLM context windows. The finding that LLM-generated confidence scores correlate strongly with alignment quality is valuable: true positives averaged $\sigma = 0.980$ versus $0.738$ for false positives, enabling effective precision-recall trade-offs via thresholding. The robust response parsing that recovers partial JSON from truncated outputs shows practical engineering awareness.
The primary flaw is the evaluation scope mismatch: the paper frames itself as N-graph fusion but only evaluates binary Chinese-English alignment, completely omitting Japanese and French results promised in the introduction. Recall is prohibitively low at 23.6%, meaning the system misses three-quarters of valid alignments. The 'exhaustive batch pairing' strategy scales quadratically ($k \times k'$ Cartesian product) and requires 5-6 hours for a single language pair, contradicting claims of scalability. Most critically, the paper cites but does not compare against recent LLM-based aligners like ZeroEA or Seg-Align, making it impossible to assess whether the proposed method offers any advantage over simpler prompt-based baselines.
The evidence supports high-precision zero-shot alignment but undermines scalability claims. The paper asserts 'linear computational complexity $O(N)$ relative to the number of graphs' (Section 3.6), but this ignores the dominant cost of exhaustive batch pairing between partitions. The 23.6% recall suggests the system fails to discover most alignments, likely due to the partition-and-pair strategy missing cross-partition correspondences. No numerical comparison is provided against embedding-based methods (MTransE, GCN-Align) or contemporary LLM approaches (ZeroEA, Seg-Align), leaving the reader unable to assess whether the gains justify the API costs and runtime.
The anonymous repository link (https://anonymous.4open.science/r/KG-Fusion-1A7D/README.md) provides code access, but critical experimental details are missing: exact partitioning hyperparameters (batch sizes), full prompt templates with system personas, and per-API-call costs. The use of Gemini 2.5 Flash with temperature 0.0 aids reproducibility but creates vendor lock-in and availability concerns; results may not transfer to open-weight models like Llama 3. The 5-6 hour runtime per dataset pair on unspecified hardware limits accessibility for replication.
Combining multiple knowledge graphs (KGs) across linguistic boundaries is a persistent challenge due to semantic heterogeneity and the complexity of graph environments. We propose a framework for cross-lingual graph fusion, leveraging the in-context reasoning and multilingual semantic priors of Large Language Models (LLMs). The framework implements structural linearization by mapping triplets directly into natural language sequences (e.g., [head] [relation] [tail]), enabling the LLM to map relations and reconcile entities between an evolving fused graph ($G_{c}^{(t-1)}$) and a new candidate graph ($G_{t}$). Evaluated on the DBP15K dataset, this exploratory study demonstrates that LLMs can serve as a universal semantic bridge to resolve cross-lingual discrepancies. Results show the successful sequential agglomeration of multiple heterogeneous graphs, offering a scalable, modular solution for continuous knowledge synthesis in multi-source, multilingual environments.
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