pyRDF2Vec VS YassQueenDB

Compare pyRDF2Vec vs YassQueenDB and see what are their differences.

YassQueenDB

Graph database library that allows you to store, analyze, and search through your data in a graph format. By using the Universal Sentence Encoder, it provides an efficient and semantic approach to handle text data. πŸ“šπŸ§ πŸš€ (by nileshkhetrapal)
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pyRDF2Vec YassQueenDB
1 4
240 14
0.8% -
2.9 5.4
10 days ago 11 months ago
Python Python
MIT License -
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pyRDF2Vec

Posts with mentions or reviews of pyRDF2Vec. We have used some of these posts to build our list of alternatives and similar projects.
  • [P] pyRDF2Vec 0.2.0 is out!
    1 project | /r/MachineLearning | 22 Mar 2021
    This release is packed with many new features and optimizations under the hood. An entire overview of what's new can be found in our CHANGELOG (https://github.com/IBCNServices/pyRDF2Vec/releases/tag/0.2.0). An overview of some major updates:

What are some alternatives?

When comparing pyRDF2Vec and YassQueenDB you can also consider the following projects:

text-summarizer - Python Framework for Extractive Text Summarization

grand - Your favorite Python graph libraries, scalable and interoperable. Graph databases in memory, and familiar graph APIs for cloud databases.

gensim - Topic Modelling for Humans

dotmotif - A performant, powerful query framework to search for network motifs

scattertext - Beautiful visualizations of how language differs among document types.

ChatData - ChatData πŸ” πŸ“– brings RAG to real applications with FREE✨ knowledge bases. Now enjoy your chat with 6 million wikipedia pages and 2 million arxiv papers.

jRDF2Vec - A high-performance Java Implementation of RDF2Vec

magnitude - A fast, efficient universal vector embedding utility package.

hub - A library for transfer learning by reusing parts of TensorFlow models.

karateclub - Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

jodie - A PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"