Incremental causal network construction over event streams

Acharya, Saurav; Lee, Byung Suk

doi:10.1016/j.ins.2013.09.022

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…The model can be used to express and analyze the meaning of causal hypothesis and strengthen the causal reasoning ability of the model through the interactive calculation of causal correlation generated in the basic network. Acharya and Lee [13] proposed an incremental causality network model to assist in inferring causality by learning time priority. The model infers causality by using an incremental Bayesian network called incremental hill climbing Monte Carlo.…”

Section: Related Workmentioning

confidence: 99%

Joint Event Relation Identification Based on Multiscale Convolutional Neural Network and Sharing Strategy

Zhang

Cao

Wang

et al. 2022

Wireless Communications and Mobile Computing

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At present, most of the event relation identification work mainly focuses on the sequential temporal and explicit causal relation between events. These methods usually ignore the role of synchronous temporal and implicit causal relation in sentences, which makes the semantic understanding of the model deviate from the text. In this paper, we propose a joint event relation identification model. The model uses bidirectional GRU and multiscale convolutional neural network to obtain the context semantic features and multiscale local semantic features of text, respectively. Then, these two kinds of features are fused to fully obtain the semantics of the text itself. In addition, we build encoders and decoders of event temporal and causal relation, respectively, to obtain the event temporal and causal semantic features from text. In this process, considering the correlation between event timing and causality, we use three different parameter sharing strategies to realize the interaction between event temporal and causal semantic features. The experimental results on the legal field dataset we constructed show that our model has made significant improvements compared with the baseline model. Through experimental analysis, our method can effectively improve the identification performance of synchronous temporal and implicit causality relation.

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Section: Related Workmentioning

confidence: 99%

Joint Event Relation Identification Based on Multiscale Convolutional Neural Network and Sharing Strategy

Zhang

Cao

Wang

et al. 2022

Wireless Communications and Mobile Computing

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“…The work can be divided into two categories. On one hand, some work learn the causal relations of two events [1], [4], [16] for prediction [24]. For instance, Radinsky et al [24] extracted generalized causality relations of two events (i.e., ''x causes y'') from past news and applied them to predict the next possible event given a current event.…”

Section: A Event Predictionmentioning

confidence: 99%

“…of news documents, 1 ''Conflict occurred again in Egypt on 22nd, and people plan to hold a million-people march'', and ''Egypt's military will deliver a speech to respond to the conflict between demonstrators and police'', our model generates ''protests'', ''burst'', ''chaos'', ''cause'', ''deaths'', ''injuries'' word by word, which constitutes a possible future subevent. It matches well with a later news report ''The Egyptian protest has caused 32 people dead and more than 2000 people injured''.…”

Section: Introductionmentioning

confidence: 99%

Integrating Hierarchical Attentions for Future Subevent Prediction

2020

IEEE Access

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An Event, containing a sequence of subevents, describes a typical thing that happens at a specific time and place. Predicting next probable subevents based on knowledge acquired from large-scale news documents are very important for many real-world applications, such as disaster warning etc. In this paper, we present a novel hierarchical attention based end-to-end model for future (unknown) subevent prediction using large-scale historical events. Our model automatically produces a short text which describes a possible future subevent after consuming the texts describing previous subevents. To boost the model's understanding towards subevent sequence, we design a hierarchical LSTM model to compress the knowledge in both the word sequence for a subevent and the subevent sequence for an event. In addition, topic information has been exploited to make context-aware prediction for future subevents. To further consider which subevents and words play a critical role in prediction, we propose a hierarchical attention mechanism to stress on the important previous subevents as well as the the critical words within them. Experimental results on a real-world dataset demonstrate the superiority of our model for future subevent prediction over state-of-the-art methods.

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“…Another approach to infer causality is presented by Popper that comprises of three conditions: temporal precedence, dependency, and no hidden variables. This approach [66] has been successfully exploited in the unbounded data streams of events to build causal networks.…”

Section: Causal Relationshipsmentioning

confidence: 99%

Discovering and utilising expert knowledge from security event logs

Khan

Parkinson

2019

Journal of Information Security and Applications

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Vulnerability assessment and security configuration of computer systems is heavily dependent on human experts, which are widely attributed as being in short supply. This can result in a system being left insecure because of the lack of easily accessible experience and specialist resources. While performing security tasks, human experts often revert to a system's event logs to establish security information (configuration changes, errors, etc.). However, finding and exploiting knowledge from event logs is a challenging and time-consuming task for non-experts. Hence there is a strong need to provide mechanisms to make the process easier for security experts, as well as providing tools for those with significantly less security expertise. In this paper, we present a novel technique to process security event logs of a system that have been evaluated and configured by a security expert, extract key domain knowledge indicative of human decision making, and automatically apply acquired knowledge to previously unseen systems by non-experts to propose security improvements. The proposed solution utilises rule mining algorithms to extract security actions from event log entries. The set of identified rules is represented as a domain action model. The domain model and problem instance generated from a previously unseen system can then be used to produce a plan-of-action, which can be exploited by non-professionals to improve their system's security. Empirical analysis is subsequently performed on 21 event logs, where the acquired domain model and identified plans are discussed in terms of accuracy and performance.

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Incremental causal network construction over event streams

Cited by 13 publications

References 7 publications

Joint Event Relation Identification Based on Multiscale Convolutional Neural Network and Sharing Strategy

Joint Event Relation Identification Based on Multiscale Convolutional Neural Network and Sharing Strategy

Integrating Hierarchical Attentions for Future Subevent Prediction

Discovering and utilising expert knowledge from security event logs

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