Disentangled Multi-span Evolutionary Network against Temporal Knowledge Graph Reasoning (2025)

Abstract: Temporal Knowledge Graphs (TKGs) incorporate the temporal feature to express the transience of knowledge by describing when facts occur. TKG extrapolation aims to infer possible future facts based on known history, which has garnered significant attention in recent years. Some existing methods treat TKG as a sequence of independent subgraphs to model temporal evolution patterns, demonstrating impressive reasoning performance. However, they still have limitations: 1) In modeling subgraph semantic evolution, they usually neglect the internal structural interactions between subgraphs, which are actually crucial for encoding TKGs. 2) They overlook the potential smooth features that do not lead to semantic changes, which should be distinguished from the semantic evolution process. Therefore, we propose Disentangled Multi-span Evolutionary Network (DiMNet) for TKG reasoning. Specifically, we design a multi-span evolution strategy that captures local neighbor features while perceiving historical neighbor semantic information, thus enabling internal interactions between subgraphs during the evolution process. To maximize the capture of semantic change patterns, we design a disentangle component that adaptively separates nodes’ active and stable features, used to dynamically control the influence of historical semantics on future evolution. Extensive experiments demonstrate that DiMNet achieves substantial performance in TKG reasoning, outperforming the state-of-the-art up to 22.7% in MRR.

## introduction factual knowledge and entity semantics evolve over time, revealing intricate temporal properties. recent studies have introduced temporal knowledge graphs (tkgs) as an extension of static * corresponding author. knowledge graphs (kgs) to capture time-sensitive facts @xcite . it introduces a time feature to record the timestamps or time intervals related to events and takes quadruples to represent a temporal fact, denoted as (s, r, o, t), such as (edison, bornin, u sa, 02/11/1847). tkgs have a broader range of applications, such as crisis warning @xcite and real-time dialogue @xcite . similar to static kgs, tkgs also face the challenge of incompleteness. the task of reasoning on tkgs aims to complete missing links based on given known temporal facts. this includes two settings: interpolation and extrapolation @xcite . the former is used to predict missing facts within a known historical time range, while the latter predicts future facts based on known historical facts @xcite . in this work, we focus on extrapolation reasoning that presents significant challenges and a wide scope for investigation. a few early works start from the future query and extract relevant semantic information and occurrence patterns from history. re-net @xcite ) is a representative work that encodes historical facts related to the query by rnn to obtain query-related representations. cygnet @xcite directly captures repetitive patterns of facts that share the same entities and relations as the query. although these methods can make the inference distribution at the prediction stage closer to the future, they overlook the structural information and relative temporal characteristics within the historical facts. to make more comprehensive use of historical information and deeply capture the developmental patterns of history, several subgraph evolution methods have been proposed, such as re-gcn @xcite , retia @xcite , daemon @xcite , etc. they view the tkg as a sequence of subgraphs and use gnnbased methods to model local structural dependencies in history, while employing sequence modeling to capture global semantic evolution patterns. however, this approach results in the structural modeling of the constructed subgraphs being independent of each other, hindering the mutual influence of internal structures between subgraphs. titer @xcite connects the sequence of historical subgraphs into a whole by introducing auxiliary edges. tipnn @xcite and xerte @xcite sample the historical facts of the tkg into a single comprehensive graph, utilizing time encoding to capture the dynamic characteristics of facts. although these methods break the isolation of subgraph internal structures, they neglect the features of the semantic evolution pattern of tkg. however, the internal structural interaction among subgraphs and the subgraphs' semantic evolution over sequence are both crucial. therefore, it still remains challenges: to this end, we propose the disentangled multi-span evolutionary network (dimnet) based on an encoder-decoder structure for the tkg reasoning task. the encoder models the evolutionary process of the historical subgraph sequence, and the decoder infers future facts from the evolutionary results. specifically, in the encoding phase, we propose multi-span evolution approach based on graph neural network to enable interaction between the internal structures of subgraphs while preserving the modeling of semantic evolution across historical subgraphs. it is designed to perceive the semantic information of historical neighbors with equal spans during the local subgraph structural modeling stage, thus realizing the perception of the distance features of historical neighbors (first challenge). additionally, during the subgraph evolution process, we design a disentangle component to dynamically and adaptively separate the mutually exclusive active and stable features of nodes as they evolve over time. the stable feature is used to guide the node evolution without deviating from the node's steady-state characteristics and intrinsic properties. the active feature is to guide the influence of multi-span historical neighbors on the current subgraph, thereby maximizing the modeling of change patterns (second challenge). in general, this paper makes the following contributions: traditional kg reasoning methods aim to model static knowledge and have seen significant advancements @xcite . there are some translation-based methods, such as transe @xcite , transh @xcite , and transr @xcite , view relations as translations of subject entities to object entities in the vector space. for the semantic matchingbased methods, such as rescal @xcite , which measures the semantic matching between entities and relations through a tensor-based relational learning approach. neural networkbased methods, such as r-gcn @xcite and compgcn @xcite , learn representations of entities and relations from the perspective of graph structural features. to represent facts with temporal feature, recent literature has increasingly focused on learning tkgs. from the perspective of reasoning tasks, they are usually divided into interpolation and extrapolation reasoning. for interpolation, ttranse @xcite extends transe @xcite by binding timestamps to relations as translation features. hyte @xcite links timestamps to their respective hyperplanes. tntcomplex @xcite , building on complex @xcite , treats tkgs as a 4th-order tensor and learns representations via canonical decomposition. however, these methods are not particularly effective for predicting future facts @xcite @xcite , leading to the development of extrapolation methods. cygnet @xcite proposes a copy-generation mechanism to collect repeated events for query head entities and relations. re-net @xcite utilizes sequence modeling and gcn to capture temporal and structural dependencies in tkg sequences. re-gcn @xcite considers both subgraph structures and static properties, modeling them in an evolving manner. tango @xcite employs neural ordinary differential equations for continuous-time reasoning. xerte @xcite ) constructs an enclosing subgraph around the query through iterative sampling and attention propagation for reasoning. daemon @xcite proposes capturing query-aware temporal path features in sequential subgraphs to complete future missing facts. tipnn @xcite builds a history temporal graph from subgraph sequences and uses query-aware methods to learn relevant reasoning paths for queries. as discussed earlier, there is still significant room for improvement in tkg reasoning methods. the proposed methods provide valuable solutions for learning semantic evolution patterns. ## preliminaries a tkg can be formalized as a sequence of static subgraphs arranged in chronological order, i.e., ## conclusion in this paper, we propose dimnet for tkg reasoning, which models historical subgraph evolution from a semantic change perspective. based on message passing, the multi-span evolution strategy enables interactions among subgraphs while capturing semantic evolution. to enhance modeling of semantic changes, we introduce a disentangle component that separates active and stable features, guiding updates in subsequent subgraphs. we further design an inference strategy using virtual subgraph sampling to address uncertainty in future topology. extensive experiments show that dim-net yields substantial improvements and achieves new state-of-the-art results.