mlsanityData to 7 Oct 2026

xLP: Explainable Link Prediction for Master Data Management

arXiv 2403.09806 ↗First posted 14 Mar 2024cs.AIcited by 1

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Abstract (arXiv)

Explaining neural model predictions to users requires creativity. Especially in enterprise applications, where there are costs associated with users' time, and their trust in the model predictions is critical for adoption. For link prediction in master data management, we have built a number of explainability solutions drawing from research in interpretability, fact verification, path ranking, neuro-symbolic reasoning and self-explaining AI. In this demo, we present explanations for link prediction in a creative way, to allow users to choose explanations they are more comfortable with.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 14 Mar 2024.

Builds on

17 of 29 references.

References · 17

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5 more
  1. 2020GraphLIME: Local Interpretable Model Explanations for Graph Neural Networkscited by 209
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  5. 2020Link Prediction using Graph Neural Networks for Master Data Managementcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Corporate-Family Resolution Is Not a String-Matching Problem: A Public Benchmark Stratified by Name Visibilitycited by 0

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  1. 2025Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning
  2. 2023Emulating the Human Mind: A Neural-symbolic Link Prediction Model with Fast and Slow Reasoning and Filtered Rules
  3. 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective
  4. 2022Towards Benchmarking Explainable Artificial Intelligence Methods
  5. 2025Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations
  6. 2025Explainable artificial intelligence (XAI): from inherent explainability to large language models
  7. 2023LMExplainer: Grounding Knowledge and Explaining Language Models
  8. 2023Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models