  In the investment industry, it is often essential to carry out fine-grained
company similarity quantification for a range of purposes, including market
mapping, competitor analysis, and mergers and acquisitions. We propose and
publish a knowledge graph, named CompanyKG, to represent and learn diverse
company features and relations. Specifically, 1.17 million companies are
represented as nodes enriched with company description embeddings; and 15
different inter-company relations result in 51.06 million weighted edges. To
enable a comprehensive assessment of methods for company similarity
quantification, we have devised and compiled three evaluation tasks with
annotated test sets: similarity prediction, competitor retrieval and similarity
ranking. We present extensive benchmarking results for 11 reproducible
predictive methods categorized into three groups: node-only, edge-only, and
node+edge. To the best of our knowledge, CompanyKG is the first large-scale
heterogeneous graph dataset originating from a real-world investment platform,
tailored for quantifying inter-company similarity.
