0.0.7

Fixed:

• Issues in block filtering
• Issues in vector based blocking
• Data model set types
• EJoin wrong naming

Added:

• Prioritization algorithms
• Tf-Idf functionality
• More metrics on entity matching
• Optional data cleaning functionalities
• New visualizations
• New stats for the blocking workflows

v0.0.6

Fixed issue in VB.

v0.0.5

Added:

• New evaluation module
• Matching metrics
• Vector based blocking techniques
• Data process methods
• Entity matching plots
• sphinx website
• New tests

Fixed:

• Architecture, abstract data types
• Data bugs in block building
• Bugs in vector based blocking
• Using workflows without gt
• Code runtime

v0.0.4

Python 3.7 and 3.8 are now supported!

New dependencies. pyJedAI supports now older python versions.
Total supported versions:

• 3.7
• 3.8
• 3.9
• 3.10

Also, added tests for all supported python versions and MacOS.

v0.0.3

First official release in PyPI

Contains:

• Tutorials and demos
• Fixed issues

v0.0.2

Optimizations, User-friendly Approach Updates

This is the second release. Project is still under development. In this release we:

• Added WorkFlow module: A high-level method that simplifies all the process. User friendly approach.
• Added comments in the basic methods.
• Performed time optimizations using by utilizing the most python.
• Created automatic tests.
• Created new Block Building Method, by using pre-trained embeddings and Gensim. Similarity search with FAISS framework.
• Uploaded to PyPI.
• Visualization techniques for performance check.

v0.0.1

First pyJedAI release: This release presents the basic structure of the well-known JedAI toolkit into the python environment. Contains:

• Data reading techniques: RDF/OWL, SPARKQL, CSV, JSON, DB
• Block building: Standard Blocking, QGrams & Extended, SuffixArray & Extended
• Block cleaning: Block purging, Block filtering
• Comparison cleaning: Weighted edge/node pruning, Cardinality edge/node pruning, BLAST, etc
• Entity matching: strsimpy
• Entity clustering: Connected component clustering
• Similarity Joins: SchemaAgnosticΕJoin, TopKSchemaAgnosticJoin
• Evaluation through Jupyter notebook