While Knowledge Graph Completion (KGC) on static facts is a matured field, Temporal Knowledge Graph Completion (TKGC), that incorporates validity time into static facts is still in its nascent stage. The KGC methods fall into multiple categories including embedding-based, rule-based, GNN-based, pretrained Language Model based approaches. However, such dimensions have not been explored in TKG. To that end, we propose a novel temporal neuro-symbolic model, NeuSTIP, that performs link prediction and time interval prediction in a TKG. NeuSTIP learns temporal rules in the presence of the Allen predicates that ensure the temporal consistency between neighboring predicates in a given rule. We further design a unique scoring function that evaluates the confidence of the candidate answers while performing link prediction and time interval prediction by utilizing the learned rules. Our empirical evaluation on two time interval based TKGC datasets suggests that our model outperforms state-of-the-art models for both link prediction and the time interval prediction task.

A temporal knowledge graph (TKG) stores the events derived from the data involving time. Predicting events is extremely challenging due to the time-sensitive property of events. Besides, the previous TKG completion (TKGC) approaches cannot represent both the timeliness and the causality properties of events, simultaneously. To address these challenges, we propose a Logic and Commonsense-Guided Embedding model (LCGE) to jointly learn the time-sensitive representation involving timeliness and causality of events, together with the time-independent representation of events from the perspective of commonsense. Specifically, we design a temporal rule learning algorithm to construct a rule-guided predicate embedding regularization strategy for learning the causality among events. Furthermore, we could accurately evaluate the plausibility of events via auxiliary commonsense knowledge. The experimental results of TKGC task illustrate the significant performance improvements of our model compared with the existing approaches. More interestingly, our model is able to provide the explainability of the predicted results in the view of causal inference. The source code and datasets of this paper are available at https://github.com/ngl567/LCGE.

Typically, translation algorithms from a symbolic to a connectionist representation and vice-versa are employed to provide either (i) a neural implementation of a logic, (ii) a logical characterisation of a neural system, or (iii) a hybrid learning system that brings together features from connectionism and symbolic artificial intelligence (Holldobler, 1993). Until recently, neural-symbolic systems were not able to fully represent, reason and learn expressive languages other than propositional and fragments of first-order logic (Cloete & Zurada, 2000). However, in (d'Avila Garcez et al., 2002b; d'Avila Garcez et al., 2002c; d'Avila Garcez et al., 2003), a new approach to knowledge representation and reasoning in neural-symbolic systems based on neural networks ensembles has been introduced. This new approach shows that modal logics can be effectively represented in artificial neural networks. In this paper, following the approach introduced in (d'Avila Garcez et al., 2002b; d'Avila Garcez et al., 2002c; d'Avila Garcez et al., 2003), we move one step further and show that temporal logics can be effectively represented in artificial neural o Artur Garcez is partly supported by the Nuffield Foundation. Luis Lamb is partly supported by CNPq. The authors would like to thank the referees for their comments.

Typically, translation algorithms from a symbolic to a connectionist representation and vice-versa are employed to provide either (i) a neural implementation of a logic, (ii) a logical characterisation of a neural system, or (iii) a hybrid learning system that brings together features from connectionism and symbolic artificial intelligence (Holldobler, 1993). Until recently, neural-symbolic systems were not able to fully represent, reason and learn expressive languages other than propositional and fragments of first-order logic (Cloete & Zurada, 2000). However, in (d'Avila Garcez et al., 2002b; d'Avila Garcez et al., 2002c; d'Avila Garcez et al., 2003), a new approach to knowledge representation and reasoning in neural-symbolic systems based on neural networks ensembles has been introduced. This new approach shows that modal logics can be effectively represented in artificial neural networks. In this paper, following the approach introduced in (d'Avila Garcez et al., 2002b; d'Avila Garcez et al., 2002c; d'Avila Garcez et al., 2003), we move one step further and show that temporal logics can be effectively represented in artificial neural o Artur Garcez is partly supported by the Nuffield Foundation. Luis Lamb is partly supported by CNPq. The authors would like to thank the referees for their comments.

Typically, translation algorithms from a symbolic to a connectionist representation and vice-versa are employed to provide either (i) a neural implementation of a logic, (ii) a logical characterisation of a neural system, or (iii) a hybrid learning system that brings together features from connectionism and symbolic artificial intelligence (Holldobler, 1993). Until recently, neural-symbolic systems were not able to fully represent, reason and learn expressive languages other than propositional and fragments of first-order logic (Cloete & Zurada, 2000). However, in (d'Avila Garcez et al., 2002b; d'Avila Garcez et al., 2002c; d'Avila Garcez et al., 2003), a new approach to knowledge representation and reasoning in neural-symbolic systems based on neural networks ensembles has been introduced. This new approach shows that modal logics can be effectively represented in artificial neural networks. In this paper, following the approach introduced in (d'Avila Garcez et al., 2002b; d'Avila Garcez et al., 2002c; d'Avila Garcez et al., 2003), we move one step further and show that temporal logics can be effectively represented in artificial neural o Artur Garcez is partly supported by the Nuffield Foundation. Luis Lamb is partly supported by CNPq. The authors would like to thank the referees for their comments.