CognTKE: A Cognitive Temporal Knowledge Extrapolation Framework (2412.16557v1)

Abstract: Reasoning future unknowable facts on temporal knowledge graphs (TKGs) is a challenging task, holding significant academic and practical values for various fields. Existing studies exploring explainable reasoning concentrate on modeling comprehensible temporal paths relevant to the query. Yet, these path-based methods primarily focus on local temporal paths appearing in recent times, failing to capture the complex temporal paths in TKG and resulting in the loss of longer historical relations related to the query. Motivated by the Dual Process Theory in cognitive science, we propose a \textbf{Cogn}itive \textbf{T}emporal \textbf{K}nowledge \textbf{E}xtrapolation framework (CognTKE), which introduces a novel temporal cognitive relation directed graph (TCR-Digraph) and performs interpretable global shallow reasoning and local deep reasoning over the TCR-Digraph. Specifically, the proposed TCR-Digraph is constituted by retrieving significant local and global historical temporal relation paths associated with the query. In addition, CognTKE presents the global shallow reasoner and the local deep reasoner to perform global one-hop temporal relation reasoning (System 1) and local complex multi-hop path reasoning (System 2) over the TCR-Digraph, respectively. The experimental results on four benchmark datasets demonstrate that CognTKE achieves significant improvement in accuracy compared to the state-of-the-art baselines and delivers excellent zero-shot reasoning ability. \textit{The code is available at https://github.com/WeiChen3690/CognTKE}.

• The paper introduces CognTKE, a framework that extrapolates future facts from temporal knowledge graphs by integrating cognitive science's Dual Process Theory.
• CognTKE uses a Temporal Cognitive Relation Directed Graph (TCR-Digraph) and a two-level reasoning system combining global shallow and local deep reasoners.
• Experiments show CognTKE significantly improves accuracy on benchmark datasets, demonstrating strong zero-shot reasoning capability for practical applications.

Overview of CognTKE: A Cognitive Temporal Knowledge Extrapolation Framework

The paper presents a Cognitive Temporal Knowledge Extrapolation framework (CognTKE) that advances the field of temporal knowledge graph (TKG) reasoning by proposing a novel method for extrapolating future unknowable facts. This task has significant implications across various domains, including finance, healthcare, and transportation. The proposed CognTKE distinguishes itself by integrating insights from cognitive science's Dual Process Theory, which posits the existence of two cognitive systems: a fast, intuitive System 1 and a slower, more deliberate System 2.

Key Contributions

1. Temporal Cognitive Relation Directed Graph (TCR-Digraph): The paper introduces a TCR-Digraph, designed to capture both local and global temporal paths associated with a query. It improves upon existing path-based methods which primarily focus on recent temporal paths, thus often neglecting longer historical relations. By broadening the scope to include both immediate and historical temporal relations, the TCR-Digraph provides a richer context for reasoning.
2. Two-level Reasoning System: CognTKE deploys a dual-process reasoning structure by combining a global shallow reasoner and a local deep reasoner. The global shallow reasoner mimics the fast, system 1 reasoning by performing one-hop temporal relation reasoning, targeting broader yet shallower historical patterns. On the other hand, the local deep reasoner models the slower system 2, conducting multi-hop path reasoning over the TCR-Digraph, which allows for detailed exploration of complex relationships.
3. Improved Reasoning Accuracy: Experimental results across four benchmark datasets—ICE14, ICE18, ICE05-15, and WIKI—demonstrate that CognTKE delivers substantial improvements in accuracy over existing baselines, notably in its zero-shot reasoning capabilities. This implies that CognTKE not only excels with known patterns but also adapts effectively to previously unseen scenarios, which is crucial for real-world application.

Implications and Future Directions

The introduction of CognTKE has significant implications for both the theoretical and practical domains of AI-driven temporal reasoning. Theoretically, it enhances the understanding of how historical paths can be utilized for accurate prediction by capturing the interplay between local dynamics and global trends. Practically, the framework's high accuracy and zero-shot capabilities make it suitable for applications such as predictive analytics in stock markets, medical prognosis, and traffic flow forecasting.

CognTKE's reliance on cognitive theories suggests a promising direction for future AI developments, where integration with cognitive science could yield systems that better mimic human thought processes. Further exploration could involve refining the TCR-Digraph to include additional temporal and contextual elements, potentially offering even more finely tuned predictions.

The paper establishes a foundation for using cognitive principles to guide the development of temporal reasoning models, providing a robust, interpretable, and adaptable approach to TKG extrapolation. Future research might focus on expanding this framework to accommodate multimodal or dynamic knowledge graphs, incorporating additional sources of data and relationships. Through its innovative design, CognTKE showcases how interdisciplinary approaches can drive advancements in AI reasoning systems.