Confidence is not Timeless: Modeling Temporal Validity for Rule-based Temporal Knowledge Graph Forecasting (2024)

Abstract: Recently, Temporal Knowledge Graph Forecasting (TKGF) has emerged as a pivotal domain for forecasting future events. Unlike black-box neural network methods, rule-based approaches are lauded for their efficiency and interpretability. For this line of work, it is crucial to correctly estimate the predictive effectiveness of the rules, i.e., the confidence. However, the existing literature lacks in-depth investigation into how confidence evolves with time. Moreover, inaccurate and heuristic confidence estimation limits the performance of rule-based methods. To alleviate such issues, we propose a framework named TempValid to explicitly model the temporal validity of rules for TKGF. Specifically, we design a time function to model the interaction between temporal information with confidence. TempValid conceptualizes confidence and other coefficients as learnable parameters to avoid inaccurate estimation and combinatorial explosion. Furthermore, we introduce a rule-adversarial negative sampling and a time-aware negative sampling strategies to facilitate TempValid learning. Extensive experiments show that TempValid significantly outperforms previous state-of-the-art (SOTA) rule-based methods on six TKGF datasets. Moreover, it exhibits substantial advancements in cross-domain and resource-constrained rule learning scenarios.

## introduction representing, acquiring, and applying knowledge have always been hot topics in the field of artificial intelligence research @xcite @xcite . knowledge reasoning @xcite is an invaluable pathway for humanity to understand and acquire new * corresponding author. in addition to the interaction of temporal information with rules, the interaction between rules is also important for reasoning. existing methods perform confidence estimation and aggregating reasoning based on rule-independence assumptions @xcite @xcite , which may lead to an overestimation of incorrect outcomes @xcite . however, delving into the interactions between rules may encounter potential combinatorial explosion of confidence @xcite . while parameterized confidence may alleviate this challenge @xcite , it remains an unsolved problem to model both the interaction of temporal information with rules and the interaction between temporal rules. to address the aforementioned challenges, we propose a framework, named tempvalid, which models the temporal validity of rules for tkgf. specifically, tempvalid assumes that confidence decays over time rather than being timeless. we design a time function with a learnable decay coefficient to regulate the decay rate impacting rule confidence. the confidence and decay coefficients are conceptualized as learnable parameters to avoid inaccurate heuristic estimation and potential combinatorial explosion. to effectively optimizing tempvalid, we design a rule-adversarial negative sampling and a time-aware negative sampling strategies. extensive experiments show that temp-valid significantly outperforms existing rule-based tkgf methods and achieves competitive performance with the state-of-the-art baselines on six tkgf datasets. moreover, tempvalid significantly outperforms existing approaches in cross-dataset and low-resource scenarios. in summary, our contributions are as follows: (1) we propose a framework named tempvalid for modeling the temporal validity of temporal rules. to our best knowledge, this is the first study of investigating the temporal validity of tem-poral rules in the context of temporal knowledge graphs. (2) we design a time function with controlled decay rate, conceptualize confidence and decay coefficients as learnable parameters, and propose two elaborate negative sampling strategies to facilitate tempvalid training. (3) our proposed tempvalid significantly surpasses the existing rule-based tkgf methods and achieves competitive performance with the state-of-the-art baselines on six representative tkgf datasets, including icews14, icews18, icews05-15, yago, wiki and gdelt. ## conclusion in this paper, we propose the tempvalid to model the temporal validity of temporal rules for tkgf. we believe that the predictive effectiveness (confidence) of the rules would decay over time. temp-valid use a machine learning model to learn confidence and decay coefficients for tkgf. in addition, we design a rule-adversarial and a time-aware negative sampling strategies to train tempvalid more efficiently, and obtain competitive results with the baselines on six classical benchmarks. we conclude that learning confidence and decay coefficients as well as bridging the gap between rule quality estimation and aggregate reasoning are necessary for rule-based tkgf. in addition, tempvalid significantly outperforms existing tkgf method in the across datasets and low-resource scenarios.