uhh-lt/path2vec
Learning to represent shortest paths and other graph-based measures of node similarities with graph embeddings
Need to quickly calculate how similar concepts are based on their relationships in a complex network, like a dictionary or knowledge graph? This tool helps you transform those complex relationships into simple numerical representations. You input a network with connections between terms, and it provides fast, numerical 'scores' of how related any two terms are. This is useful for computational linguists, semantic engineers, or anyone working with large-scale semantic networks for tasks like analyzing word meanings.
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Use this if you need to rapidly estimate how semantically similar words or concepts are, based on their connections in a structured knowledge base like WordNet, without the slow computation of direct pathfinding.
Not ideal if your primary goal is to visualize or directly analyze the raw graph structure itself, rather than derive numerical similarity scores.
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33
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Language
Jupyter Notebook
License
Apache-2.0
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Last pushed
Aug 27, 2019
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