Title: Event causality identification via a method of integrating context-awareness and knowledge-enhancement

Authors: Shaonan Liu; Bo Shen

Addresses: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China ' Key Laboratory of Communication and Information Systems, Beijing Jiaotong University, Beijing, 100044, China

Abstract: In the field of natural language processing, identifying causality between events is conducive to enhance the logical reasoning ability of language models. Due to the limited scale of datasets and the diversity of event causality expressions, mainstream representation-based methods exhibit poor generalisation to unseen causality and struggle to identify implicit causality. To address these issues, we propose a model named CAKE which integrating context-awareness and knowledge-enhancement for event causality identification. Specifically, the model constructs a context-aware reasoner that learns contextual semantics and event-independent global causal patterns through masking operation and adversarial perturbation mechanism, thereby improving the model's generalisation to unseen causality. Additionally, it builds a knowledge-enhanced reasoner by incorporating external knowledge, which aggregates one-hop neighbour information and models multi-hop relational paths to enrich event representations, enhancing the model's capability to identify implicit causality. Experimental results demonstrate that the proposed model significantly outperforms baseline methods on two public datasets.

Keywords: event causality identification; context awareness; knowledge enhancement; natural language processing.

DOI: 10.1504/IJAISC.2025.151485

International Journal of Artificial Intelligence and Soft Computing, 2025 Vol.9 No.4, pp.395 - 419

Received: 27 Aug 2025
Accepted: 23 Nov 2025
Published online: 02 Feb 2026 *

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