Temporal Extrapolation and Knowledge Transfer for Lifelong Temporal Knowledge Graph Reasoning (2023)

Abstract: Real-world Temporal Knowledge Graphs keep growing with time and new entities and facts emerge continually, necessitating a model that can extrapolate to future timestamps and transfer knowledge for new components. Therefore, our work first dives into this more realistic issue, lifelong TKG reasoning, where existing methods can only address part of the challenges. Specifically, we formulate lifelong TKG reasoning as a temporal-path-based reinforcement learning (RL) framework. Then, we add temporal displacement into the action space of RL to extrapolate for the future and further propose a temporal-rule-based reward shaping to guide the training. To transfer and update knowledge, we design a new edge-aware message passing module, where the embeddings of new entities and edges are inductive. We conduct extensive experiments on three newly constructed benchmarks for lifelong TKG reasoning. Experimental results show the outperforming effectiveness of our model against all well-adapted baselines.

## introduction knowledge graphs (kgs) are constructed to store structured facts about human knowledge or the objective world, and formalize facts as entities e (nodes) and relations r (links) between them. static knowledge graphs (skgs) and temporal knowledge graphs (tkgs) are two typical forms of kgs. skgs store facts in the form of triples (e s , r, e o ) and tkgs extend triples to quadruples (e s , r, e o , t), where t indicates the happening time. since realworld events are usually ever-changing and associated with time, tkgs are naturally confronted with issues of continually emerging entities and facts in the future timestamps throughout their whole lifecycle @xcite . therefore, this paper investigates tkg link prediction task over incomplete tkgs in the lifelong setting, named lifelong *corresponding author tkg reasoning. figure 1 is an example in dataset icews14 for temporally growing tkgs. however, skg reasoning methods @xcite lack the consideration of temporal changing; conventional transductive tkg reasoning methods @xcite need re-train for their closed-world assumption; and the latest inductive tkg reasoning methods @xcite treat emerging entities as simultaneous, oversimplifying the real scenario and thereby leading their genuine performance to be questionable. hence, our proposed lifelong tkg reasoning issue is more challenging and realistic. we formulate the lifelong tkg reasoning as a temporal-path-based rl framework and design the whole pipeline for extrapolating, transferring and updating. in the following, we introduce our targeted solutions and expound on their advantages over existing methods. first, we focus on the temporal displacement between timestamps of candidate edge and its preceding edge and add temporal displacement into rl action space. tkge models @xcite rely on embeddings of absolute timestamps and are only fit for the past timestamps. obviously, they do not meet the requirements of lifelong learning. on the contrary, our used transferable temporal dis-placement in rl can be extrapolated from known timestamps to arbitrary future timestamps based on the magnitude of the displacement. in addition, we further design a reward-shaping module based on temporal rules found by rl, which only have the temporal order constraints of relations. this module makes the reasoning get rid of particular entities and will still be applicable for future timestamps. secondly, lifelong tkg reasoning can be considered as multiple consecutive inductive tkg reasoning processes. recently, inductive tkg reasoning methods @xcite ) can only deal with future time, not new components, not to mention their ability to continuously learn as required in lifelong tkg reasoning. therefore, we design a new edge-aware message passing module, which not only transfers learned relation types to initialize emerging entities, but also updates the embeddings of all entities and edges in new tkgs. we also use the embeddings of specific edges rather than immobile relation types, since we seek to explore the concrete environment for each fact to counteract the influence of rapid tkg growth. we build three new benchmarks based on three popular datasets to simulate the lifelong tkg reasoning scenario. in the experiments, we carefully adapt existing baselines by empowering them with temporal extrapolation or knowledge transfer capabilities. in summary, our main contributions are: ## constructed benchmarks to conduct evaluation for lifelong tkg reasoning, we construct three new tkg benchmarks based on three datasets icews14, icews05-15 (garcía-durán et al., 2018) and icews18 @xcite to simulate their growth situation of entities, named as icews14-lifelong, icews05-15-lifelong and icews18-lifelong. ## conclusion in this work, the lifelong tkg reasoning issue involves continually emerging entities and facts in the future timestamps. to address this new problem, we propose our model under the framework of rl and our model uses temporal displacement in the action space to extrapolate to the future timestamps; uses a new edge-aware message passing module to inductively transfer and update learned knowledge to new entities and facts; and uses a temporal-rule-based reward shaping to guide the training. the experimental results on three newly constructed benchmarks illustrate that our model has the best performance for lifelong tkg reasoning and the strongest knowledge lifelong learning capability.