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Human behavior may be one of the most challenging phenomena to model and validate. This paper proposes a method for automatically extracting and compiling evidence on human behavior determinants into a knowledge graph. The method (1) extracts associations of behavior determinants and choice options in relation to study groups and moderators from published studies using Natural Language Processing and Deep Learning, (2) synthesizes the extracted evidence into a knowledge graph, and (3) sub-selects the model components and relationships that are relevant and robust. The method can be used to either (4a) construct a structurally valid simulation model before proceeding with calibration or (4b) to validate the structure of existing simulation models. To demonstrate the feasibility of the method, we discuss an example implementation with mode of transport as behavior choice. We find that including non-frequently studied significant behavior determinants drastically improves the model's explanatory power in comparison to only including frequently studied variables. The paper serves as a proof-of-concept which can be reused, extended or adapted for various purposes. • The structure of behavior models should be validated using existing evidence. • Deep Learning can help automatize behavior evidence extraction and synthesis. • Infrequently studied significant variables improve model performance drastically. [ABSTRACT FROM AUTHOR]

Identifying causality between events is a crucial research task in natural language processing. However, existing methods either ignore background knowledge of events or do not consider interference of knowledge graph noise on event representations. In this paper, we propose a novel Graph Contrast-based Knowledge Augmented Network (GCKAN) for event causality identification task, which integrates comprehensive background knowledge of events from knowledge graphs and alleviates the knowledge graph noise problem. First, a descriptive knowledge augmentation module is proposed to aggregate one-hop neighbor information in knowledge graphs and learn meaningful descriptive knowledge of events. Then, a relational knowledge augmentation module is designed to encode multi-hop path information and learn latent reasoning knowledge between event pairs. In addition, trustworthiness-based and degree-based graph contrastive learning schemas are devised in the two modules respectively, which suppress knowledge graph noise during information aggregation and derive more robust knowledge-aware event representations. Extensive experiments on three public datasets demonstrate the consistent superiority of GCKAN over state-of-the-art knowledge-based techniques. Noise interference experiments and cross-topic adaptation experiments further verify the robustness and generalization of GCKAN. • A graph contrast-based knowledge augmented network for event causality identification is proposed. • The model aggregates descriptive and relational knowledge from knowledge graphs and alleviates labeled data scarcity issue. • Graph contrastive learning schemes are devised to suppress knowledge noise and improve robustness of event representations. • Experimental results demonstrate effectiveness and robustness of the method. [ABSTRACT FROM AUTHOR]