TenderKG

• URL: http://arxiv.org/abs/2608.14066v1
• Date: Fri, 14 Aug 2026 08:24:30 GMT
• Title: TenderKG
• Abstract summary: We introduce TenderKG, a large-scale knowledge graph dataset constructed from French public procurement data covering the period 2021--2023.<n>The dataset models the procurement ecosystem through heterogeneous entities, including companies, tenders, lots, and domain-specific domains.<n>We provide detailed statistics and analyses of the dataset, highlighting its structural properties, sparsity patterns, and domain-specific characteristics.
• Score: 0.9558392439655014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Public procurement represents a major economic activity, where public institutions allocate contracts to companies through competitive tendering processes. Despite its importance, this domain remains underexplored by recommender systems, largely due to the lack of publicly available datasets capturing its complexity. In this paper, we introduce TenderKG, a large-scale knowledge graph dataset constructed from French public procurement data covering the period 2021--2023. The dataset models the procurement ecosystem through heterogeneous entities, including companies, tenders, lots, and domain-specific taxonomies of work domains, connected via rich semantic and structural relations. A key specificity of this setting is that only the awarded companies are visible, resulting in sparse explicit signals of awarded interactions. To overcome this limitation, TenderKG integrates extensive side information on the actors in the French tender market and the tenders, including textual descriptions, hierarchical classifications, and geographical features, enabling the study of knowledge-aware recommendation in a highly constrained and competitive environment. We provide detailed statistics and analyses of the dataset, highlighting its structural properties, sparsity patterns, and domain-specific characteristics. We believe TenderKG opens new research directions in bidder recommendation, knowledge graph-based recommendation, competition-aware matching, and provides a valuable benchmark for evaluating methods in real-world, high-stakes decision-making scenarios.

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