Displaying 1 to 6 from 6 results

dedupe - :id: A python library for accurate and scaleable fuzzy matching, record deduplication and entity-resolution

  •    Python

dedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on structured data. dedupe takes in human training data and comes up with the best rules for your dataset to quickly and automatically find similar records, even with very large databases.

csvdedupe - :id: Command line tool for deduplicating CSV files

  •    Python

Command line tools for using the dedupe python library for deduplicating CSV files. csvdedupe - takes a messy input file or STDIN pipe and identifies duplicates.

conciliator - OpenRefine reconciliation services for VIAF, ORCID, and Open Library + framework for creating more

  •    Java

conciliator is a growing collection of OpenRefine reconciliation services, as well as a Java framework for creating them. A reconciliation service tries to match variant text (usually names of things) to standard IDs for the entity represented by that text. This project supercedes refine_viaf.

whatis - WhatIs.this: simple entity resolution through Wikipedia

  •    Ruby

WhatIs.this is a quick probe for the meaning and metadata of concepts through Wikipedia. gem install whatis or add gem "whatis" to your Gemfile.




dedupe-examples - :id: Examples for using the dedupe library

  •    Python

Example scripts for the dedupe, a library that uses machine learning to perform de-duplication and entity resolution quickly on structured data. We recommend using virtualenv and virtualenvwrapper for working in a virtualized development environment. Read how to set up virtualenv.

rltk - Record Linkage ToolKit (Find and link entities)

  •    Python

The Record Linkage ToolKit (RLTK) is a general-purpose open-source record linkage platform that allows users to build powerful Python programs that link records referring to the same underlying entity. Record linkage is an extremely important problem that shows up in domains extending from social networks to bibliographic data and biomedicine. Current open platforms for record linkage have problems scaling even to moderately sized datasets, or are just not easy to use (even by experts). RLTK attempts to address all of these issues. RLTK supports a full, scalable record linkage pipeline, including multi-core algorithms for blocking, profiling data, computing a wide variety of features, and training and applying machine learning classifiers based on Python’s sklearn library. An end-to-end RLTK pipeline can be jump-started with only a few lines of code. However, RLTK is also designed to be extensible and customizable, allowing users arbitrary degrees of control over many of the individual components. You can add new features to RLTK (e.g. a custom string similarity) very easily.