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In the investment industry, it is often essential to carry out fine-grainedcompany similarity quantification for a range of purposes, including marketmapping, competitor analysis, and mergers and acquisitions. We propose andpublish a knowledge graph, named CompanyKG, to represent and learn diversecompany features and relations. Specifically, 1.17 million companies arerepresented as nodes enriched with company description embeddings; and 15different inter-company relations result in 51.06 million weighted edges. Toenable a comprehensive assessment of methods for company similarityquantification, we have devised and compiled three evaluation tasks withannotated test sets: similarity prediction, competitor retrieval and similarityranking. We present extensive benchmarking results for 11 reproduciblepredictive methods categorized into three groups: node-only, edge-only, andnode+edge. To the best of our knowledge, CompanyKG is the first large-scaleheterogeneous graph dataset originating from a real-world investment platform,tailored for quantifying inter-company similarity.

CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification