Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning (2022)

Abstract: A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to different timestamps. TKG reasoning aims to predict potential facts in the future given the historical KG sequences. One key of this task is to mine and understand evolutional patterns of facts from these sequences. The evolutional patterns are complex in two aspects, length-diversity and time-variability. Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length. Furthermore, these models are all trained offline, which cannot well adapt to the changes of evolutional patterns from then on. Thus, we propose a new model, called Complex Evolutional Network (CEN), which uses a length-aware Convolutional Neural Network (CNN) to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy. Besides, we propose to learn the model under the online setting so that it can adapt to the changes of evolutional patterns over time. Extensive experiments demonstrate that CEN obtains substantial performance improvement under both the traditional offline and the proposed online settings.

## introduction temporal knowledge graph (tkg) @xcite @xcite has emerged as a very active research area over the last few years. each fact in tkgs is a quadruple @xcite . a tkg can be denoted as a sequence of kgs with timestamps, each of which contains all facts at the corresponding timestamp. tkg reasoning aims to answer queries about future facts, such as (covid-19, new medical case occur, ?, 2022-1-9). to predict future facts, one challenge is to dive deep into the related historical facts, which reflect * this work was done while the first author was doing internship at baidu inc. the preferences of the related entities and affect their future behaviors to a certain degree. such facts, usually temporally adjacent, may carry informative sequential patterns, called evolutional patterns in this paper. for example, @xcite , @xcite , @xcite ] is an informative evolutional pattern for the above query implied in historical kgs. there are two kinds of models to model evolutional patterns, namely, query-specific and entire graph based models. the first kind of models @xcite @xcite @xcite extract useful structures (i.e., paths or subgraphs) for each individual query from the historical kg sequence and further predict the future facts by mining evolutional patterns from these structures. this kind of models may inevitably neglect some useful evolutional patterns. therefore, the entire graph based models @xcite take a sequence of entire kgs as the input and encode evolutional patterns among them, which exhibit superiority to the query-specific models. however, they all ignore the length-diversity and time-variability of evolutional patterns. lengthdiversity: the lengths of evolutional patterns are diverse. for example, [(covid-19, infect, a, 2021-12-21), (a, discuss with, b, 2021-12-25), (b, go to, shop, 2021-12-28)] is a useful evolutional pattern of length 3 to predict the query (covid-19, new medical case occur, ?, 2022-1-9) and @xcite , @xcite ] is also a useful evolutional pattern of length 2 for this query. previous models extract evolutional patterns of a fixed length, which cannot handle evolutional patterns of diverse lengths. timevariability: evolutional patterns change over time. for example, (covid-19, infect, a, 2019-12-9) and (covid-19, infect, a, 2022-1-9) may lead to different results due to the wide usage of the covid-19 vaccines. previous models learn from the historical training data, which fail in modeling the time-variability of evolutional patterns after that. upon the above observations, we propose complex evolutional network (cen) to deal with the above two challenges. for length-diversity, cen learns evolutional patterns from historical kg sequences of different lengths via an relational graph neural network (rgcn) based kg sequence encoder and a length-aware convolutional neural network (cnn) based evolutional representation decoder. besides, the model is trained via an easy-to-difficult curriculum learning strategy incrementally according to the length of kg sequences. for time-variability, we learn cen under an online setting and combine cen with a temporal regularization unit to alleviate the catastrophic forgetting problem @xcite . in general, this paper makes the following contributions: ## related work the tkg reasoning task primarily has two settings, interpolation and extrapolation. this paper focus on the extrapolation setting. in what follows, we will introduce related work on both settings: tkg reasoning under the interpolation setting. this setting aims to complete the missing facts at past timestamps @xcite @xcite @xcite . for example, ttranse @xcite extends transe @xcite by adding the temporal constraints; hyte @xcite projects the entities and relations to time-aware hyperplanes to generate representations for different timestamps. above all, they cannot obtain the representations of the unseen timestamps and are not suitable for the extrapolation setting. tkg reasoning under the extrapolation setting this setting aims to predict facts at future timestamps, which can be categorized into two groups: query-specific and entire graph based models. query-specific models focus on modeling the query-specific history. for example, re-net @xcite captures the evolutional patterns implied in the subgraph sequences of a fixed length specific to the query. cygnet @xcite captures repetitive patterns by modeling repetitive facts. xerte @xcite learns to find the query-related subgraphs of a fixed hop number. cluster @xcite and titer @xcite both adopt reinforcement learning to discover evolutional patterns in query-related paths of a fixed length. unlike the query-specific models, entire graph based models encode the latest historical kg sequence of a fixed-length. re-gcn @xcite captures the evolutional patterns into the representations of all the entities by modeling kg sequence of a fixed-length at lastest a few timestamps. glean @xcite introduces event descriptions to enrich the information of the entities. ## methodology we propose cen to deal with the length-diversity and time-variability challenges of evolutional pattern learning for tkg reasoning. specifically, cen consists of a basic model as well as a curriculum learning strategy for the former challenge and an online learning strategy for the latter challenge. ## conclusions in this paper, we proposed complex evolutional network (cen) for tkg reasoning, which deals with two challenges in modeling the complex evolutional patterns: length-diversity and timevariability. for length-diversity, cen adopts a length-aware cnn to learn evolutional patterns of different lengths and is trained under a curriculum learning strategy. for time-variability, we explored a new online setting, where the model is expected to be updated to new evolutional patterns emerging over time. experimental results demonstrate the superiority of the proposed model under both the offline and the online settings.