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.

翻译：在投资行业中，为实现市场映射、竞争对手分析及并购等一系列目标，通常需对公司间相似度进行精细化量化。我们提出并发布了一个名为CompanyKG的知识图谱，用于表示和学习多样化的公司特征及关系。具体而言，该图谱包含117万个节点（每个节点通过公司描述嵌入进行丰富表示）及15种不同的公司间关系（形成5106万条加权边）。为全面评估公司相似度量化方法，我们设计并整合了三项带标注测试集的评估任务：相似度预测、竞争对手检索及相似度排序。我们提供了11种可复现预测方法的广泛基准测试结果，这些方法分为三类：仅节点、仅边及节点+边。据我们所知，CompanyKG是首个源自真实投资平台、专为公司间相似度量化定制的大规模异构图数据集。