MAKG: Microsoft Academic Knowledge Graph with 8 Billion Triples

The Microsoft Academic Knowledge Graph (MAKG) is a large RDF data set with over eight billion triples containing information about scientific publications and related entities. It is based on the Microsoft Academic Graph and was created by Michael Färber of the Karlsruhe Institute of Technology, with a paper published at ISWC'19 in 2019. The data set includes entity embeddings for all 210 million represented scientific papers.

Use Cases

• Analyzing publication trends and author networks based on linked scholarly entities.
• Training or evaluating entity linking and knowledge graph embedding models based on the provided paper embeddings.
• Building recommendation systems for academic papers based on fields of study and citation links.

Strengths

• Contains over eight billion triples, indicating a large-scale knowledge graph.
• Includes entity embeddings for 210 million scientific papers, providing a machine-readable representation.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Last update date is unknown; freshness unverified.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Microsoft Academic Graph

Collection Method

Converted and published as an RDF knowledge graph.