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Learning Expressive Linkage Rules For Entity Matching Using Genetic Programming.pdf

Isele_Dissertation.pdf
Preview of Learning Expressive Linkage Rules for Entity Matching using Genetic Programming
🔗 Source: madoc.bib.uni-mannheim.de
📊 Size: 7.82 MB
📄 Pages: 224 pages
⬇️ Downloads: 235

Summary

This thesis proposes novel methods for entity matching, covering the complete workflow from generating linkage rules using genetic programming algorithms to their efficient execution on distributed systems. A supervised learning algorithm is introduced that generates linkage rules from a gold standard, outperforming previous approaches and achieving similar accuracy to human-written rules. An active learning algorithm is also proposed, which interactively generates a gold standard by asking the user to confirm or decline the equivalence of entity pairs, achieving comparable accuracy with minimal user input. Finally, an efficient execution workflow is introduced, employing a novel multidimensional indexing method that significantly reduces the number of required comparisons.

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Description

This thesis proposes novel methods for entity matching, covering the complete workflow from generating linkage rules using genetic programming algorithms to...

Technical Information

  • File Format: PDF
  • File Size: 7.82 MB
  • Pages: 224
  • Language: EN
  • Total Downloads: 235
  • Last Updated: 2 hours ago

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This PDF document about Learning Expressive Linkage Rules for Entity Matching using Genetic Programming provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Learning Expressive Linkage Rules for Entity Matching using Genetic Programming.

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Learning guideLearning tutorialExpressive guideExpressive tutorialLinkage guideLinkage tutorialRules guideRules tutorialEntity guideEntity tutorialMatching guideMatching tutorialusing guideusing tutorialGenetic guideGenetic tutorialProgramming guideProgramming tutorial

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