Temporal Knowledge Graph Research Articles

Continuous-Time Dynamic Graph Networks Integrated with Knowledge Propagation for Social Media Rumor Detection

The proliferation of the Internet and mobile devices has made it increasingly easy to propagate rumors on social media. Widespread rumors can incite public panic and have detrimental effects on individuals. In recent years, researchers have found that both the spatial structure of rumor diffusion and the temporal features of propagation can be effective in identifying rumors. However, existing methods tend to focus on either spatial structure or temporal information in isolation, and few models can effectively capture both types of information. Additionally, most existing methods treat continuously changing temporal information as static snapshots, neglecting the precise timing of propagation. Moreover, as users repost and comment, background knowledge associated with the posts also evolves dynamically, which is often ignored. To address these limitations, we propose CGNKP (Continuous-time Dynamic Graph Networks integrated with Knowledge Propagation), a model that jointly captures the spatial structure and continuous-time features of post propagation to fully understand the dynamics of background knowledge. Specifically, we introduce a novel method for encoding continuous-time dynamic graphs, modeling the propagation process through two dynamic graphs: a temporal propagation graph (for posts diffusion) and a temporal knowledge graph (for knowledge diffusion). Extensive experiments on real-world datasets demonstrate that CGNKP significantly outperforms multiple strong baselines, achieving accuracies of 0.861 on the Twitter15 dataset and 0.903 on the Twitter16 dataset.

SSQ[formula omitted]: A Subgraph-based Semantic Query Approach for Temporal Knowledge Graph

Real-world knowledge graphs are growing in size with the explosion of data and rapid expansion of knowledge. There are some studies on knowledge graph query, but temporal knowledge graph (TKG) query is still a relatively unexplored field. A temporal knowledge graph is a knowledge graph that contains temporal information and contains knowledge that is likely to change over time. It introduces a temporal dimension that can characterize the changes and evolution of entities and relationships at different points in time. However, in the existing temporal knowledge graph query, the entity labels are one-sided, which cannot accurately reflect the semantic relationships of temporal knowledge graphs, resulting in incomplete query results. For the processing of temporal information in temporal knowledge graphs, we propose a temporal frame filtering approach and measure the acceptability of temporal frames by the new definition simtime based on the proposed three temporal frames and nine rules. For measuring the semantic relationship of predicates between entities, we vectorize the semantic similarity between predicates, i.e., edges, using the knowledge embedding model, and propose the new definition simpre to measure the semantic similarity of predicates. Based on these, we propose a new semantic temporal knowledge graph query method SSQTKG, and perform pruning operations to optimize the query efficiency of the algorithm based on connectivity. Extensive experiments show that SSQTKG can return more accurate and complete results that meet the query conditions in the semantic query and can improve the performance of the querying on the temporal knowledge graph.

TBicomR: Event Prediction in Temporal Knowledge Graphs with Bicomplex Rotation

Temporal knowledge graphs (TKGs) capture relationships and entities evolving over time, making event prediction a challenging task due to the complex temporal and relational dynamics. In this work, we propose BiCoTime, a novel model using bicomplex embeddings to represent entities, relations, and time. While quaternions capture asymmetric relations through non-commutativity, bicomplex numbers provide a commutative algebraic structure, ideal for modeling both symmetric and asymmetric relations. Unlike quaternions, bicomplex embeddings maintain interpretability in symmetric relations while preserving key algebraic properties like distributivity. Temporal rotations further enhance BiCoTime's ability to model the interaction between relations and time, capturing how entities and relationships evolve. This combination of bicomplex embeddings and temporal rotations ensures a more interpretable and accurate modeling of TKGs. Our experiments show that TBiComR achieved a 21% improvement in Mean Reciprocal Rank (MRR) on the ICEWS14 dataset, which emphasizes time points, and a 15% improvement on the YAGO11k dataset, which focuses on time spans. The choice of bicomplex numbers balances computational complexity and expressive power, offering efficient training and better predictive performance compared to models using quaternions or octonions.

Few-Shot Fuzzy Temporal Knowledge Graph Completion via Fuzzy Semantics and Dynamic Attention Network

Few-Shot Fuzzy Temporal Knowledge Graph Completion via Fuzzy Semantics and Dynamic Attention Network

Chronobridge: a novel framework for enhanced temporal and relational reasoning in temporal knowledge graphs

The task of predicting entities and relations in Temporal Knowledge Graph (TKG) extrapolation is crucial and has been studied extensively. Mainstream algorithms, such as Gated Recurrent Unit (GRU) models, primarily focus on encoding historical factual features within TKGs, often neglecting the importance of incorporating entities and relational features during decoding. This bias ultimately leads to loss of detail and inadequate prediction accuracy during the inference process. To address this issue, a novel ChronoBridge framework is proposed that features a dual mechanism of a chronological node encoder and a bridged feature fusion decoder. Specifically, the chronological node encoder employs an advanced recursive neural network with an enhanced GRU in an autoregressive manner to model historical KG sequences, thereby accurately capturing entity changes over time and significantly enhancing the model’s ability to identify and encode temporal patterns of facts across the timeline. Meanwhile, the bridged feature fusion decoder utilizes a new variant of GRU and a multilayer perception mechanism during the prediction phase to extract entity and relation features and fuse them for inference, thereby strengthening the reasoning capabilities of the model for future events. Testing on three standard datasets showed significant improvements, with a 25.21% increase in MRR accuracy and a 39.38% enhancement in relation inference. This advancement not only improves the understanding of temporal evolution in knowledge graphs but also sets a foundation for future research and applications of TKG reasoning.

Temporal Knowledge Graph Reasoning Based on Dynamic Fusion Representation Learning

ABSTRACTRecently, significant progress has been made in completing static knowledge graphs. However, knowledge tends to evolve with time, and static knowledge graph completion (KGC) methods struggle to capture the changes. Therefore, temporal knowledge graph (TKG) reasoning has become a focus of research. Most existing TKG methods incorporate temporal information into triplets and transform them into KGC tasks, ignoring the important influence of time information and implicit relationships between entities. In this paper, we propose a new method called TD‐RKG, which addresses the challenges of temporal variability and implicit entity correlations based on a dynamic fusion representation learning approach. The method consists of four modules: dynamic local recurrent encoding layer, dynamic implicit encoding layer, dynamic global information attention layer and decoding layer. Experimental results on three benchmark datasets demonstrate substantial improvements in TD‐RKG across multiple evaluation metrics.

A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion

Temporal knowledge graph completion (TKGC) is the task of inferring missing facts based on existing ones in a temporal knowledge graph. In recent years, various TKGC methods have emerged, among which deep learning-based methods have achieved state-of-the-art performance. In order to understand the current research status of TKGC methods based on deep learning and promote further development in this field, in this paper, for the first time, we summarize the deep learning-based methods in TKGC research. First, we detail the background of TKGC, including task definition, benchmark datasets, and evaluation protocol. Then, we divide the existing deep learning-based TKGC methods into eight fine-grained categories according to their core technology and summarize them. Finally, we conclude the paper and present three future research directions for TKGC.

MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion.

Temporal knowledge graphs (TKGs) are receiving increased attention due to their time-dependent properties and the evolving nature of knowledge over time. TKGs typically contain complex geometric structures, such as hierarchical, ring, and chain structures, which can often be mixed together. However, embedding TKGs into Euclidean space, as is typically done with TKG completion (TKGC) models, presents a challenge when dealing with high-dimensional nonlinear data and complex geometric structures. To address this issue, we propose a novel TKGC model called multicurvature adaptive embedding (MADE). MADE models TKGs in multicurvature spaces, including flat Euclidean space (zero curvature), hyperbolic space (negative curvature), and hyperspherical space (positive curvature), to handle multiple geometric structures. We assign different weights to different curvature spaces in a data-driven manner to strengthen the ideal curvature spaces for modeling and weaken the inappropriate ones. Additionally, we introduce the quadruplet distributor (QD) to assist the information interaction in each geometric space. Ultimately, we develop an innovative temporal regularization to enhance the smoothness of timestamp embeddings by strengthening the correlation of neighboring timestamps. Experimental results show that MADE outperforms the existing state-of-the-art TKGC models.

QDN: A Quadruplet Distributor Network for Temporal Knowledge Graph Completion.

Temporal knowledge graph completion (TKGC) is an extension of the traditional static knowledge graph completion (SKGC) by introducing the timestamp. The existing TKGC methods generally translate the original quadruplet to the form of the triplet by integrating the timestamp into the entity/relation, and then use SKGC methods to infer the missing item. However, such an integrating operation largely limits the expressive ability of temporal information and ignores the semantic loss problem due to the fact that entities, relations, and timestamps are located in different spaces. In this article, we propose a novel TKGC method called the quadruplet distributor network (QDN), which independently models the embeddings of entities, relations, and timestamps in their specific spaces to fully capture the semantics and builds the QD to facilitate the information aggregation and distribution among them. Furthermore, the interaction among entities, relations, and timestamps is integrated using a novel quadruplet-specific decoder, which stretches the third-order tensor to the fourth-order to satisfy the TKGC criterion. Equally important, we design a novel temporal regularization that imposes a smoothness constraint on temporal embeddings. Experimental results show that the proposed method outperforms the existing state-of-the-art TKGC methods. The source codes of this article are available at https://github.com/QDN for Temporal Knowledge Graph Completion.git.

Historical Trends and Normalizing Flow for One-shot Temporal Knowledge Graph Reasoning

Temporal Knowledge Graphs (TKGs) have garnered significant scholarly interest for their ability to dynamically represent the evolution of real-world events. However, the presence of the long tail effect in knowledge graphs necessitates the exploration of one-shot temporal knowledge graph reasoning. Concurrently, the issues of out-of-distribution and overfitting heavily impact the performance of models. Empirical evidence from real-world cases has demonstrated the significance of historical trend information in accurately predicting future events, and normalizing flows effectively address out-of-distribution and overfitting challenges. Thus, this paper proposes a novel approach named Historical Trends and Normalizing Flow for One-shot Temporal Knowledge Graph Reasoning (HNOT). Specifically, HNOT combine normalizing flows with a historical information aggregator to obtain more complex distributions, thereby resolving out-of-distribution and overfitting problems and capturing global representations of TKGs. In contrast to existing models that focus solely on entity and relationship modeling, we have introduced a novel historical trend information aggregator. This aggregator is designed to extract trend information from temporal knowledge subgraphs. By employing trend information as the primary modeling object, it facilitates the exploration of trend variations at different timestamps. This approach serves as an auxiliary source of global information. Subsequently, we integrate these two distinct types of information to achieve predictions of future events. Experimental results demonstrate the superiority of the proposed method over state-of-the-art baselines on three benchmark datasets.

A survey on temporal knowledge graph embedding: Models and applications

Knowledge graph embedding (KGE), as a pivotal technology in artificial intelligence, plays a significant role in enhancing the logical reasoning and management efficiency of downstream tasks in knowledge graphs (KGs). It maps the intricate structure of a KG to a continuous vector space. Conventional KGE techniques primarily focus on representing static data within a KG. However, in the real world, facts frequently change over time, as exemplified by evolving social relationships and news events. The effective utilization of embedding technologies to represent KGs that integrate temporal data has gained significant scholarly interest. This paper comprehensively reviews the existing methods for learning KG representations that incorporate temporal data. It offers a highly intuitive perspective by categorizing temporal KGE (TKGE) methods into seven main classes based on dynamic evolution models and extensions of static KGE. The review covers various aspects of TKGE, including the background, problem definition, symbolic representation, training process, commonly used datasets, evaluation schemes, and relevant research. Furthermore, detailed descriptions of related embedding models are provided, followed by an introduction to typical downstream tasks in temporal KG scenarios. Finally, the paper concludes by summarizing the challenges faced in TKGE and outlining future research directions.

Temporal knowledge graph reasoning based on evolutional representation and contrastive learning

Temporal knowledge graph reasoning based on evolutional representation and contrastive learning

EHPR: Learning evolutionary hierarchy perception representation based on quaternion for temporal knowledge graph completion

Research on temporal knowledge graphs garners attention due to the intricate connection between facts and dynamic temporal factors. However, existing research uses timestamp as auxiliary data for representation learning and directly integrate it into facts, resulting in the inability to capture the intrinsic connections between relations under time evolution. To handle these challenges, we propose the Evolutionary Hierarchy Perception Representation (EHPR), which first leverages the Hamilton product to perform rotational transformations on relation and entity over time, aiming to learn temporal relation and temporal entity with close interactions with time information. Later, EHPR is divided into two modules: (a) Rotating the head entity towards the tail entity using temporal relation through Hamilton product to model complex patterns with quaternion rotation capabilities. (b) Adopting an evolutionary hierarchical factor to capture the differences in modulus distribution between the temporal head entity and the temporal tail entity, aiming to manage the evolutionary hierarchical information between different temporal entities. In this way, EHPR not only utilizes the rich quaternion rotation capabilities to model various relation patterns but also further enables modeling of evolutionary hierarchical patterns through evolutionary hierarchy factors. Experiments show that EHPR achieves remarkable performance on six mature benchmarks compared to state-of-the-art models. Furthermore, we successfully transferred the core idea of EHPR into complex embeddings, showcasing the framework's adaptability. Compared to complex embedding models, EHPR also demonstrates stronger expressive abilities with the Hamilton operator, surpassing the performance of complex Hermitian operator.

Enhancing Temporal Knowledge Graph Representation with Curriculum Learning

Temporal knowledge graph representation approaches encounter significant challenges in handling the complex dynamic relations among entities, relations, and time. These challenges include the high difficulty of training and poor generalization performance, particularly with large datasets. To address these issues, this paper introduces curriculum learning strategies from machine learning, aiming to improve learning efficiency through effective curriculum planning. The proposed framework constructs a high-dimensional filtering model based on graph-based high-order receptive fields and employs a scoring model that uses a curriculum temperature strategy to evaluate the difficulty of temporal knowledge graph data quadruples at each stage. By progressively expanding the receptive field and dynamically adjusting the difficulty of learning samples, the model can better understand and capture multi-level information within the graph structure, thereby improving its generalization capabilities. Additionally, a temperature factor is introduced during model training to optimize parameter gradients, alongside a gradually increasing training strategy to reduce training difficulty. Experiments on the benchmark datasets ICEWS14 and ICEWS05-15 demonstrate that this framework not only significantly enhances model performance on these datasets but also substantially reduces training convergence time.

Coherence mode: Characterizing local graph structural information for temporal knowledge graph

A Temporal Knowledge Graph (TKG) is designed for the effective modelling of the temporal relationships and dynamics of entities, events and concepts. Owing to its temporal attributes, a TKG offers greater benefits for reasoning than a static knowledge graph (KG). However, existing approaches for TKG reasoning do not consider coherent relationships between numerous facts, a term borrowed from specific parlance reflecting the interaction between objectives, such as observation and removal agents. This characteristic suggests that the model can obtain more insights from simultaneous coherent relationships. To address this problem, we develop a label-based process to construct a TKG from temporal event data in these domains. Based on the process, we build a specific TKG called Simulated Agent Interaction Knowledge Graph (SAIKG). In addition, we propose a novel TKG reasoning mechanism, termed the Coherence Mode. It is premised on an event coherence manner, enabling the prediction of unknown facts. Extensive experimental studies on different datasets demonstrate the effectiveness of the Coherence Mode integrated with typical models.

Anomalous behavior detection based on optimized graph embedding representation in social networks

Anomalous behaviors in social networks can lead to privacy leaks and the spread of false information. In this paper, we propose an anomalous behavior detection method based on optimized graph embedding representation. Specifically, the user behavior logs are first extracted into a social network user behavior temporal knowledge graph, based on which the graph embedding representation method is used to transform the network topology and temporal information in the user behavior knowledge graph into structural embedding vectors and temporal information embedding vectors, and the hybrid attention mechanism is used to merge the two types of vectors to obtain the final entity embedding to complete the prediction and complementation of the temporal knowledge graph of user behavior. We use graph neural networks, which use the temporal information of user behaviors as a time constraint and capture both user behavioral and semantic information. It converts the two parts of information into vectors for concatenation and linear transformation to obtain a comprehensive representation vector of the whole subgraph, as well as joint deep learning models to evaluate abnormal behavior. Finally, we perform experiments on the Yelp dataset to validate that our method achieves a 9.56% improvement in the F1-score.

TSA-Net: a temporal knowledge graph completion method with temporal-structural adaptation

TSA-Net: a temporal knowledge graph completion method with temporal-structural adaptation

TELS: Learning time-evolving information and latent semantics using dual quaternion for temporal knowledge graph completion

In temporal knowledge graphs (TKGs), the status of facts is intricately tied to the dynamic and precise nature of temporal factors. Existing research merely treats time as supplementary information, without considering the latent semantic changes caused by the positional changes of entities within specific relations in a temporal context. Furthermore, due to the coarse granularity of timestamps in existing TKGs, the number of multiple relations pattern among entities significantly increases, limiting model performance. This paper proposes a Time-Evolving Information and Latent Semantics model (TELS), which represents facts as dual quaternion embeddings to provide a compact and elegant representation. Specifically, we use timestamp dual quaternions, transforming the entity and relation into temporal entity and temporal relation through dual quaternion multiplication. Besides, we introduce semantic-aware dual quaternion to capture the latent semantics arising from the positional changes of entities within specific relations. Next, TELS consists of two parts: (a) We use semantic-aware dual quaternions to perform transformations on head entity and tail entity respectively through dual quaternion multiplication. Next, we utilize temporal relation to transform head entity to tail entity through dual quaternion multiplication. (b) We adopt an evolutionary hierarchical factor to encapsulate the differences in modulus distribution between the temporal head entity and temporal tail entity. In this way, TELS not only uses dual quaternions to handle key patterns and multiple relations pattern, but also handles evolutionary hierarchical patterns by capturing the modulus distribution differences between temporal entities. Meanwhile, TELS learns semantic-aware dual quaternion embeddings to capture the latent semantics endowed by relations to entities. Empirically, TELS can boost the performance over seven temporal knowledge graph benchmarks.

TaReT: Temporal knowledge graph reasoning based on topology-aware dynamic relation graph and temporal fusion

Previous temporal knowledge graph (TKG) reasoning methods often focus exclusively on evolving representations. However, these methods suffer from the inadequacy of capturing the structural nuances of concurrent facts, the intricate relations in topological subgraphs, and the fusion of temporal information across timestamps. To address these challenges, this paper proposes a TKG reasoning method based on Topology-aware dynamic Relation graph and Temporal fusion (TaReT). First, TaReT proposes an innovative attention-based relational graph model, serving as a structural information encoder that captures the intricate structure of concurrent facts. Then, TaReT designs a topology-aware relational correlation unit to discern topological relation graphs of various patterns via an edge-level correlation network, yielding relation representations. Furthermore, TaReT introduces an inter-timestamp temporal information encoder which applies a dual-gate mechanism to integrate structural and relational information for temporal fusion. Finally, the temporal decoder is applied to output entity and relation predictions. Extensive experiments on four benchmark datasets establish TaReT’s superiority over leading TKG reasoning methods. On the ICEWS14 dataset, the MRR value of TaReT exceeds the reasoning baseline RE-GCN by 14.6%.

Temporal knowledge graph reasoning based on relation graphs and time-guided attention mechanism

Predicting future events through temporal knowledge graph reasoning has attracted widespread attention from researchers in recent years. Since future events are unknown, a comprehensive understanding of the semantic correlations and historical evolution patterns of entities and relations is a prerequisite for accurate reasoning. However, current models frequently overlook the semantic correlations of relations and fail to consider that the impact of historical repetitive facts on the query varies over time. To tackle these issues, a novel temporal knowledge graph reasoning model based on Relation Graphs and Time-Guided Attention mechanism (RG-TGA) within a local–global framework was proposed. Specifically, RG-TGA designed a relation graph construction and aggregation method in the local information encoder to capture the structural characteristics of relations. Additionally, an improved relation-aware graph attention network was performed to aggregate the local neighbors of entities. In the global information encoder, RG-TGA adopted a time-guided attention mechanism to allocate attention weights to historical repetitive facts based on their time spans. Finally, temporal knowledge graph reasoning was executed by integrating the local and global information. Experimental results on five public datasets show that RG-TGA outperforms the state-of-the-art model by 2.63%, 3.95%, 3.49%, and 2.71% in MRR, Hits@1, Hits@3, and Hits@10, respectively.