Building a Temporal Knowledge Graph with Python and NetworkX

Ali Nemati2 days ago30 sec read9 views

The system built a temporal knowledge graph from approximately 20 scientific publication documents, extracting 66 nodes and 230 edges across eight node types and ten edge types. It includes features for historical snapshot queries, time-constrained path finding, and other temporal analyses, offering insights into the evolution of research subjects over time. The graph can be exported in various formats for analysis and visualization using tools like Gephi, Cytoscape, D3.js, and pyvis.

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