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Prompt-tuning with Attribute Guidance for Low-resource Entity Matching

Lihui Liu, Carl Yang

Abstract

Entity Matching (EM) is an important task that determines the logical relationship between two entities, such as Same, Different, or Undecidable. Traditional EM approaches rely heavily on supervised learning, which requires large amounts of high-quality labeled data. This labeling process is both time-consuming and costly, limiting practical applicability. As a result, there is a strong need for low-resource EM methods that can perform well with minimal labeled data. Recent prompt-tuning approaches have shown promise for low-resource EM, but they mainly focus on entity-level matching and often overlook critical attribute-level information. In addition, these methods typically lack interpretability and explainability. To address these limitations, this paper introduces PROMPTATTRIB, a comprehensive solution that tackles EM through attribute-level prompt tuning and logical reasoning. PROMPTATTRIB uses both entity-level and attribute-level prompts to incorporate richer contextual information and employs fuzzy logic formulas to infer the final matching label. By explicitly considering attributes, the model gains a deeper understanding of the entities, resulting in more accurate matching. Furthermore, PROMPTATTRIB integrates dropout-based contrastive learning on soft prompts, inspired by SimCSE, which further boosts EM performance. Extensive experiments on real-world datasets demonstrate the effectiveness of PROMPTATTRIB.

Prompt-tuning with Attribute Guidance for Low-resource Entity Matching

Abstract

Entity Matching (EM) is an important task that determines the logical relationship between two entities, such as Same, Different, or Undecidable. Traditional EM approaches rely heavily on supervised learning, which requires large amounts of high-quality labeled data. This labeling process is both time-consuming and costly, limiting practical applicability. As a result, there is a strong need for low-resource EM methods that can perform well with minimal labeled data. Recent prompt-tuning approaches have shown promise for low-resource EM, but they mainly focus on entity-level matching and often overlook critical attribute-level information. In addition, these methods typically lack interpretability and explainability. To address these limitations, this paper introduces PROMPTATTRIB, a comprehensive solution that tackles EM through attribute-level prompt tuning and logical reasoning. PROMPTATTRIB uses both entity-level and attribute-level prompts to incorporate richer contextual information and employs fuzzy logic formulas to infer the final matching label. By explicitly considering attributes, the model gains a deeper understanding of the entities, resulting in more accurate matching. Furthermore, PROMPTATTRIB integrates dropout-based contrastive learning on soft prompts, inspired by SimCSE, which further boosts EM performance. Extensive experiments on real-world datasets demonstrate the effectiveness of PROMPTATTRIB.
Paper Structure (19 sections, 6 equations, 4 figures, 3 tables)

This paper contains 19 sections, 6 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Problem Definition
  3. Method
  4. Prompt Template
  5. Label Words Set.
  6. Method Details
  7. Entity Level Prompt-tuning
  8. Attribute Level Prompt-tuning
  9. Fuzzy Logic Reasoning
  10. Soft Token Contrastive Learning
  11. Experiment
  12. Entity Matching Performance
  13. Performance of Different Language Models
  14. Ablation Study
  15. Related Work
  16. ...and 4 more sections

Figures (4)

  • Figure 1: Example.
  • Figure 2: The illustration of prompt-tuning. The blue rectangles in the figure are special prompt tokens, whose parameters are initialized and learnable during prompt-tuning.
  • Figure 3: The entity level prompt tuning and the attribute level prompt tuning.
  • Figure 4: Contrastive learning framework. Dropout is applied to the entire input embedding.