Beyond Predefined Queries: Open-Domain Forecasting on Temporal Knowledge Graphs

Forecasting future interactions and relationships has broad applications in domains such as supply chains, corporate activities, and international relations. Temporal knowledge graphs (TKGs) represent time-dependent relational facts, but conventional TKG forecasting typically predicts a missing field in a predefined future query. For example, a model may predict which company a given supplier will supply on a specified future date, while the supplier, relation, and timestamp are provided. In many applications, these fields are unknown and must be predicted together. To address this gap, we are the first to formulate the Open-Domain Forecasting problem on TKGs and propose an Open-Domain forecasting (ODCAST) framework to address it. ODCAST predicts a variable-size set of complete future relational facts from a historical TKG and a forecast horizon without predefined target queries. ODCAST uses prediction slots to form initial hypotheses and Self-Instantiated Queries to retrieve historical evidence under a shared graph-access budget. A set decoder combines neural proposals with recurrence modeling and selects complete quadruples through branch-specific thresholds, while an independent temporal trajectory expert reranks recurrent candidates. Experiments on three benchmark datasets show that ODCAST consistently outperforms the best-performing baseline in Strict F1 across all evaluated forecast horizons, achieving improvements of up to 43.15%, 110.93%, and 25.04% on ICEWS14, ICEWS05–15, and tkgl-polecat, respectively.