The Temporal Game: A New Perspective on Temporal Relation Extraction

2025-08-29

AI Summary

This paper addresses the challenges of unifying interval- and point-based temporal entities in time relation annotation and ensuring annotation consistency. Methodologically: (1) it decomposes interval relations into endpoint-level comparisons, enabling unified modeling of both interval and instantaneous entities; (2) it integrates temporal closure reasoning with rule-based consistency verification to guarantee logical completeness of annotations; and (3) it introduces a dual-mode framework—Game Mode and Annotation Mode—that combines reinforcement learning with real-time user feedback to support dynamic inference and constraint-driven labeling. The primary contribution is an open-source, reproducible system supporting TempEval-3 annotation and customizable timeline export. Functioning both as a research tool and an annotation platform, it establishes a novel paradigm for fine-grained, high-consistency temporal annotation.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Temporal Planning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Representation, semantic annotation, enhancement, enrichments, access and/or integration of a variety of data on the Web
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

In this paper we demo the Temporal Game, a novel approach to temporal relation extraction that casts the task as an interactive game. Instead of directly annotating interval-level relations, our approach decomposes them into point-wise comparisons between the start and end points of temporal entities. At each step, players classify a single point relation, and the system applies temporal closure to infer additional relations and enforce consistency. This point-based strategy naturally supports both interval and instant entities, enabling more fine-grained and flexible annotation than any previous approach. The Temporal Game also lays the groundwork for training reinforcement learning agents, by treating temporal annotation as a sequential decision-making task. To showcase this potential, the demo presented in this paper includes a Game mode, in which users annotate texts from the TempEval-3 dataset and receive feedback based on a scoring system, and an Annotation mode, that allows custom documents to be annotated and resulting timeline to be exported. Therefore, this demo serves both as a research tool and an annotation interface. The demo is publicly available at https://temporal-game.inesctec.pt, and the source code is open-sourced to foster further research and community-driven development in temporal reasoning and annotation.

Problem

• Decomposes interval relations into point-wise comparisons
• Enables fine-grained annotation for temporal entities
• Trains reinforcement learning agents for annotation

Innovation

• Decomposes interval relations into point-wise comparisons
• Applies temporal closure to infer and enforce consistency
• Treats annotation as sequential decision-making for reinforcement learning