mlsanityData to 13 Oct 2026

Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark]

arXiv 2304.12329 ↗First posted 24 Apr 2023cs.DB cs.CLcited by 13

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Abstract (arXiv)

Many recent works on Entity Resolution (ER) leverage Deep Learning techniques involving language models to improve effectiveness. This is applied to both main steps of ER, i.e., blocking and matching. Several pre-trained embeddings have been tested, with the most popular ones being fastText and variants of the BERT model. However, there is no detailed analysis of their pros and cons. To cover this gap, we perform a thorough experimental analysis of 12 popular language models over 17 established benchmark datasets. First, we assess their vectorization overhead for converting all input entities into dense embeddings vectors. Second, we investigate their blocking performance, performing a detailed scalability analysis, and comparing them with the state-of-the-art deep learning-based blocking method. Third, we conclude with their relative performance for both supervised and unsupervised matching. Our experimental results provide novel insights into the strengths and weaknesses of the main language models, facilitating researchers and practitioners to select the most suitable ones in practice.

Key facts. Cited by 13 papers in the map (data through 13 Oct 2026). Builds on 34 of its 71 references that are in the map. First posted on arXiv 24 Apr 2023.

Builds on

34 of 71 references.

References · 34

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 1; 2024: 8; 2025: 3; 2026: 1.

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  1. 2025GFM-RAG: Graph Foundation Model for Retrieval Augmented Generationcited by 40
  2. 2023Entity Matching using Large Language Modelscited by 31
  3. 2024Fine-tuning Large Language Models for Entity Matchingcited by 16
  4. 2024Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Modelscited by 11
  5. 2024KcMF: A Knowledge-compliant Framework for Schema and Entity Matching with Fine-tuning-free LLMscited by 8
  6. 2024Learning from Natural Language Explanations for Generalizable Entity Matchingcited by 6
  7. 2024On Leveraging Large Language Models for Enhancing Entity Resolution: A Cost-efficient Approachcited by 5
  8. 2024Evaluating Blocking Biases in Entity Matchingcited by 3
  9. 2024Towards Universal Dense Blocking for Entity Resolutioncited by 1
  10. 2024Zero-Indexing Internet Search Augmented Generation for Large Language Modelscited by 0
  11. 2025Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolutioncited by 0
  12. 2025Leveraging LLMs for Collaborative Ontology Engineering in Parkinson Disease Monitoring and Alertingcited by 0
1 more
  1. 2026Corporate-Family Resolution Is Not a String-Matching Problem: A Public Benchmark Stratified by Name Visibilitycited by 0