Chinese causal event extraction using causality‐associated graph neural network

Gao, Jianqi; Luo, Xiangfeng; Wang, Hao

doi:10.1002/cpe.6572

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…(3) Deep learning, such as [32][33][34][35][36]. In [37], a tree-based neural network model is proposed to learn syntactic features automatically.…”

Section: Event Extractionmentioning

confidence: 99%

An APT Event Extraction Method Based on BERT-BiGRU-CRF for APT Attack Detection

2023

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Advanced Persistent Threat (APT) seriously threatens a nation’s cyberspace security. Current defense technologies are typically unable to detect it effectively since APT attack is complex and the signatures for detection are not clear. To enhance the understanding of APT attacks, in this paper, a novel approach for extracting APT attack events from web texts is proposed. First, the APT event types and event schema are defined. Secondly, an APT attack event extraction dataset in Chinese is constructed. Finally, an APT attack event extraction model based on the BERT-BiGRU-CRF architecture is proposed. Comparative experiments are conducted with ERNIE, BERT, and BERT-BiGRU-CRF models, and the results show that the APT attack event extraction model based on BERT-BiGRU-CRF achieves the highest F1 value, indicating the best extraction performance. Currently, there is seldom APT event extraction research, the work in this paper contributes a new method to Cyber Threat Intelligence (CTI) analysis. By considering the multi-stages, complexity of APT attacks, and the data source from huge credible web texts, the APT event extraction method enhances the understanding of APT attacks and is helpful to improve APT attack detection capabilities.

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“…(3) Deep learning, such as [32][33][34][35][36]. In [37], a tree-based neural network model is proposed to learn syntactic features automatically.…”

Section: Event Extractionmentioning

confidence: 99%

An APT Event Extraction Method Based on BERT-BiGRU-CRF for APT Attack Detection

2023

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“…Event schema induction is an essential step in understanding events to adapt to new domains, and it is widely used in various event tasks, such as event detection, 1 event extraction, 1,2 and causal inference 3 . Previous studies rely on manually predefined event schema and corresponding annotations to learn their model.…”

Section: Introductionmentioning

confidence: 99%

Open‐domain event schema induction via weighted attentive hypergraph neural network

Qin,

Wang,

Luo

2024

Concurrency and Computation

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SummaryEvent schema refers to the use of a template to depict similar events, and it is a necessary prerequisite for event causality extractions. The induction of event schemas is a difficult task, especially for texts in the open domain, due to the complex and diverse manifestations of events. Previous models considered participants in event mentions are independent or compositional, ignoring the high‐order correlations among participants, which limit their capability of induce event schema. To remedy this, we propose constructing an Event Structure Hypergraph (ESH) to better utilizes the event structural information for event schema induction. In particular, we first extract event mentions from the open‐domain corpus. and then construct an ESH by representing event mentions as a hyperedges. ESH contains high‐order information between participants in event mention. To, learn event mentions representation based on ESH, we propose a weighted attentive hypergraph neural network (WHGNN) to model event high‐order correlations and then integrate node‐category weight matrix into the training of network by improving event representation. By applying jointly cluster algorithm on the event mentions representation, we can induce reliable event schemas. Experimental results on three datasets demonstrate that our approach can induce salient and high‐quality event schemas on open‐domain corpus.

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“…Alternatively, other methods (Chen et al 2022;Gao et al 2023) aim to directly identify causal data or eliminate confounders to achieve the modeling of causal relationships. Furthermore, there exists a multitude of techniques that center their focus on studying the modeling capability of GNNs for specific causal relationships in practical application scenarios (Cao et al 2023;Gao, Luo, and Wang 2022;Wang et al 2022b). These GNN causal enhancement methods have all demonstrated favorable outcomes, effectively enhancing the robustness and credibility of GNN models.…”

Section: Introductionmentioning

confidence: 99%

Rethinking Causal Relationships Learning in Graph Neural Networks

Gao,

Yao,

et al. 2024

AAAI

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Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustness of GNNs, it becomes exceptionally crucial to bolster their ability to capture causal relationships. However, despite recent advancements that have indeed strengthened GNNs from a causal learning perspective, conducting an in-depth analysis specifically targeting the causal modeling prowess of GNNs remains an unresolved issue. In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels. The rationality of the generated data is further ensured through theoretical foundations. Drawing insights from analyses conducted using our dataset, we introduce a lightweight and highly adaptable GNN module designed to strengthen GNNs' causal learning capabilities across a diverse range of tasks. Through a series of experiments conducted on both synthetic datasets and other real-world datasets, we empirically validate the effectiveness of the proposed module. The codes are available at https://github.com/yaoyao-yaoyao-cell/CRCG.

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Chinese causal event extraction using causality‐associated graph neural network

Cited by 5 publications

References 24 publications

An APT Event Extraction Method Based on BERT-BiGRU-CRF for APT Attack Detection

An APT Event Extraction Method Based on BERT-BiGRU-CRF for APT Attack Detection

Open‐domain event schema induction via weighted attentive hypergraph neural network

Rethinking Causal Relationships Learning in Graph Neural Networks

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