fuzzy string matching

To address progressive entity resolution under time-sensitive and resource-constrained settings, this paper proposes the first systematic progressive entity matching framework, decomposing the process into four stages: filtering, weighting, scheduling, and matching. It introduces a unified design space encompassing mainstream approaches and a novel confidence-prioritized dynamic scheduling mechanism, significantly improving early recall and response efficiency. The framework integrates hybrid (rule- and learning-based) filtering, multi-feature weighting, and configurable matching functions, enabling end-to-end, on-demand retrieval of high-confidence results. Extensive experiments across 10 linkage and 8 deduplication datasets demonstrate that the optimal configuration achieves 90% recall of true duplicate pairs 42% earlier on average, while simultaneously improving both F1 score and time efficiency.

This work addresses the poor generalizability of existing self-service entity resolution methods on unseen datasets and their difficulty in balancing precision and recall, which often leads to error propagation and cascading erroneous merges. To overcome these limitations, the authors propose a robust self-service entity resolution system that integrates three key innovations: automated selection among multiple algorithms, decoupled optimization of precision and recall—enforcing precision through rule-based veto mechanisms while enhancing recall via diverse candidate generation—and non-transitive cross-cluster merge validation to prevent error propagation. Extensive experiments on six real-world benchmark datasets, ranging from 864 to 5 million records, demonstrate that the proposed approach significantly reduces incorrect merges, enhances practical utility, and substantially lowers the trial-and-error burden for practitioners.

This study addresses the challenge of person identity matching across heterogeneous records characterized by linguistic and cultural complexity, diverse naming conventions, and high data noise. To tackle this problem, the authors propose the Structure-Guided Entity Resolution (SGER) framework, which introduces a novel two-stage curriculum fine-tuning strategy: first guiding a large language model to learn the syntactic and semantic structures of personal names, followed by optimizing it for binary entity matching. Evaluated on 50,000 real-world Indian identity record pairs, SGER achieves 99.02% accuracy and an F1 score of 0.994, significantly outperforming few-shot prompting with GPT-4o and single-stage fine-tuning baselines. The method has been deployed on the Dream11 platform, serving over 250 million users, and demonstrates enhanced robustness and precision in multilingual, high-noise entity resolution scenarios.

To address confirmation bias induced by noisy pseudo-labels in knowledge graph entity alignment, this paper proposes UPL-EA, a Unified Pseudo-Labeling framework for Entity Alignment. Methodologically, UPL-EA is the first to systematically distinguish and jointly mitigate Type I (false positive) and Type II (false negative) pseudo-label errors. It achieves this through two key components: (i) a theoretically grounded, one-to-one pseudo-label generation criterion based on cross-graph optimal transport; and (ii) a convergence-guaranteed inter-iteration pseudo-label calibration mechanism. Integrated with graph neural networks and cross-KG embedding alignment, UPL-EA significantly improves alignment accuracy under low-resource seed settings. Empirical results show a 32% reduction in pseudo-label error rate and consistent superiority over state-of-the-art methods. Crucially, UPL-EA provides formal theoretical guarantees on the convergence of its iterative pseudo-label refinement process.

Large language models (LLMs) suffer from hallucination and instruction confusion in schema matching (SM) and entity matching (EM) tasks. Method: We propose KcMF, a zero-fine-tuning framework featuring (i) a novel pseudocode-driven unified task decomposition paradigm; (ii) a dual knowledge injection mechanism—“Data-as-Knowledge” (DaK) and “Example-as-Knowledge” (EaK); and (iii) a multi-LLM output fusion strategy integrated with zero-shot prompting and structured output constraints. Contribution/Results: KcMF introduces pseudocode as a universal task orchestration language, enabling cross-task and cross-model knowledge transfer via interpretable, executable representations. Evaluated on five mainstream LLMs, it achieves an average F1-score gain of 17.93% over non-LLM baselines on SM/EM benchmarks, demonstrating substantial improvements in zero-shot generalization capability.

This study addresses the unclear role of distribution alignment in domain-aware entity matching under low-resource and varying supervision conditions. The authors systematically evaluate the BEACON framework across diverse data constraints and algorithmic configurations, offering the first in-depth analysis of the factors influencing distribution alignment in budget-constrained settings. Through controlled experiments, they uncover the synergistic mechanism between domain information and distribution alignment, demonstrating its critical impact on matching performance. The findings provide empirical evidence and practical design guidance for optimizing entity matching systems in low-resource, multi-domain scenarios.

Large language models are prone to generating hallucinations or dubious citations in academic writing, undermining research credibility. This study presents the first systematic evaluation and comparison of mainstream citation verification tools—CheckIfExist, HalluCiteChecker, Hallucinator, HalRef, and RefChecker—on real-world academic documents. The analysis reveals significant limitations in current approaches, particularly concerning citation extraction accuracy, breadth of database coverage, and consistency in verification. While these tools can offer preliminary alerts for potentially fabricated references, their overall effectiveness remains constrained. This work provides an empirical foundation and clear directions for improving the verification of citation authenticity in scholarly communication.

This study addresses the lack of systematic understanding regarding how large language models (LLMs) respond to subtle semantic perturbations in document-level semantic similarity scoring. The authors construct a multifactorial experimental framework, embedding controlled semantic perturbations—such as negation, conjunction substitution, and named entity replacement—across varying positions, topical relevance levels, and document lengths. Through tens of thousands of comparative evaluations across five prominent LLMs, the work uncovers several novel phenomena: a positional bias wherein perturbations near document beginnings elicit stronger sensitivity, a polarization effect induced by topically irrelevant context, and consistent “scoring fingerprints” unique to each model despite a shared hierarchy in tolerance to perturbations. Building on these insights, the study introduces the first LLM-agnostic auditing toolkit for semantic similarity scoring.

This work addresses the pervasive issue of citation hallucinations in scientific text generated by large language models, which often manifest as metadata errors or entirely fabricated references. To tackle this challenge, the authors propose CiteCheck, a novel framework that integrates academic database retrieval, a structured large language model verifier, and calibrated multi-tier decision rules to enable fine-grained detection of citation inaccuracies—ranging from minor deviations to complete fabrications. Evaluated on a benchmark comprising 982 physics citations, CiteCheck achieves a macro F1 score of 88.7 and an accuracy of 88.9%, substantially outperforming mainstream models such as GPT, Claude, and Gemini.

This work addresses the challenges of semantic inconsistency, low matching efficiency, and noise interference arising from numerical attribute discrepancies in multi-table entity matching. To tackle these issues, the paper proposes LLM4MEM, a novel framework that systematically leverages large language models (LLMs) for aligning unlabeled entities across multiple sources—a first in the field. The approach employs multi-style prompt engineering to harmonize attribute semantics, utilizes transitive consistency embeddings to accelerate pre-matching, and incorporates a density-aware pruning mechanism to refine alignment results. Evaluated on six standard multi-table entity matching benchmarks, LLM4MEM achieves an average F1-score improvement of 5.1% over state-of-the-art baselines, demonstrating its effectiveness and innovation.