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.

本研究提出并发表了一种名为 CompanyKG 的知识图谱，用于代表和学习各种公司特征和关系，以便对公司相似性进行细粒度的量化，为此我们设计并编制了三个评估任务，包括相似性预测、竞争者检索和相似性排名，并提供了可复制的预测方法来进行广泛的基准测试。