usc-isi-i2/kgtk

Knowledge Graph Toolkit

What it solves

KGTK addresses the difficulty of creating and analyzing massive, hyper-relational knowledge graphs (KGs) that are often too large to fit in memory. It provides a scalable framework to handle billions of edges on standard hardware, such as a laptop, by using a disk-based approach to storage and processing.

How it works

KGTK represents knowledge graphs as simple tab-separated (TSV) files with four columns: edge-identifier, head, edge-label, and tail. This format allows users to build pipelines of commands that import, transform, filter, and join graphs. The toolkit includes a specialized query language (a variant of Cypher) optimized for disk-based storage, as well as tools for graph analytics (like PageRank) and the generation of text and graph embeddings.

Who it’s for

It is designed for researchers and developers working with large-scale semantic data, particularly those focusing on Wikidata, RDF, or the construction of custom commonsense knowledge graphs.

Highlights

  • Scalable Processing: Capable of processing Wikidata-sized graphs with billions of edges on a laptop.
  • Comprehensive Pipeline: Includes tools for importing from RDF/Wikidata, cleaning, unioning, and exporting to formats like ElasticSearch or JSON.
  • Advanced Analytics: Supports centrality metrics, connected components, shortest paths, and graph embeddings.
  • Integration: Seamlessly integrates with Jupyter notebooks and Pandas for a development-friendly environment.

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