Data for: "Linking Datasets on Organizations Using Half a Billion Open-Collaborated Records"

GraphsTextsGLP-3Introduced 2023-02-06

Source: Linking Datasets on Organizations Using Half-a-Billion Open-Collaborated Records (Description (Markdown and LATEX enabled))

High-Level Explanation of the Dataset

  • Scale and Composition: This repository provides millions of positive and negative name match examples (e.g., “PosMatches_mat.csv,” “NegMatches_mat.csv,” etc.) derived from LinkedIn data, alongside bipartite and Markov network representations of organizational relationships.
  • LinkedIn-Based Training Corpus: By leveraging the open-collaborated records from LinkedIn, the dataset offers a vast repository of string pairs—covering trillions of potential organization name matches—designed to refine and improve the accuracy of fuzzy matching techniques.

Motivations and Summary of Content

  • Overcoming String Matching Challenges: Standard fuzzy matching struggles with highly variable and context-dependent organization names. This dataset was created to provide large-scale, diverse examples for training advanced machine learning models to address these limitations.
  • Network Representation: In addition to direct name-pair examples, the dataset includes bipartite and Markov network files that capture relationships between organizations, recognizing that organizational matching often depends on structural ties (e.g., shared employees or affiliated branches).
  • Examples Folder: The Example* directories illustrate how the data can be integrated into typical merging tasks: each folder includes x and y variables (e.g., by_x and by_y) and a merged z dataset, demonstrating how linkage can be evaluated and improved in applied research settings.

Potential Use Cases

  1. Entity Resolution / Record Linkage: Researchers and data scientists can use the millions of positive and negative organizational name matches to train more robust entity resolution algorithms across various domains (e.g., nonprofit, corporate, government datasets).
  2. Network Analysis: The bipartite and Markov network representations allow for investigations into the structural connectivity of organizations, uncovering patterns in how organizational names (and, by extension, entities) relate to one another.
  3. Benchmarking and Methodological Development: Method developers can employ this corpus to benchmark and stress-test new string matching approaches, harnessing the dataset’s size and diversity to refine algorithmic performance.
  4. Domain-Specific Applications: Researchers studying lobbying firms, nonprofits, or other organizational types can integrate this dataset to improve the reliability of merges between multiple datasets that lack standardized IDs.

For further details or questions, please contact the authors at the email provided in the repository.