Temporal Knowledge Graph Research Articles

Attention-Based Complex Logical Query on Temporal Knowledge Graph via Graph Neural Network

Attention-Based Complex Logical Query on Temporal Knowledge Graph via Graph Neural Network

Joint Multi-Feature Information Entity Alignment for Cross-Lingual Temporal Knowledge Graph with BERT

Joint Multi-Feature Information Entity Alignment for Cross-Lingual Temporal Knowledge Graph with BERT

Learning Dynamic and Static Representations for Extrapolation-Based Temporal Knowledge Graph Reasoning

Learning Dynamic and Static Representations for Extrapolation-Based Temporal Knowledge Graph Reasoning

Wasserstein adversarial learning based temporal knowledge graph embedding

Research on knowledge graph embedding (KGE) has emerged as an active field in which most existing KGE approaches mainly focus on static structural data and ignore the influence of temporal variation involved in time-aware triples. In order to deal with this issue, several temporal knowledge graph embedding (TKGE) approaches have been proposed to integrate temporal and structural information. However, these methods only employ a uniformly random sampling to construct negative facts. As a consequence, the corrupted samples are often too simplistic for training an effective model. In this paper, we propose a new temporal knowledge graph embedding framework by introducing adversarial learning to further refine the performance of traditional TKGE models. In our framework, a generator is utilized to construct high-quality plausible quadruples and a discriminator learns to obtain the embeddings of entities and relations based on both positive and negative samples. Meanwhile, we also apply a Gumbel-Softmax relaxation and the Wasserstein distance to prevent vanishing gradient problems on discrete data; an inherent flaw in traditional generative adversarial networks. Through comprehensive experimentation on temporal datasets, the results indicate that our proposed framework can attain significant improvements based on benchmark models and also demonstrate the effectiveness and applicability of our framework.

Knowledge Representation and Reuse of Ship Block Coating Based on Knowledge Graph

Ship coating, as one of the three pillar processes in the shipbuilding industry, runs through the entire process of ship construction. However, there is currently a lack of effective organization, management methods, and mechanisms for ship coating process data, which not only leads to the dispersion of data but also limits the effective representation and reuse of the coating knowledge. To solve this problem, this paper takes the ship block coating process as the research object and proposes a method for knowledge modeling and reuse of coating knowledge using knowledge graph and question answering technology. Compared with existing strategies, this paper introduces the temporal knowledge graph, which allows for dynamic updating and generation of the knowledge graph specific to ship coating processes. In addition, we apply the knowledge embedding question answering (KEQA) method improved by the analytic hierarchy process (AHP) to facilitate high-quality retrieval and personalized question answering regarding ship block coating knowledge. We validate the proposed method using block coating process data from the 81200DWT bulk carrier and advanced ship coating methods and optimization data. The results demonstrate that the AHP-KEQA (KEQA method improved by the AHP) method improves the accuracy of knowledge question answering compared with KEQA, which further reinforces the effectiveness of the AHP-KEQA method for question answering of ship block coating knowledge.

Hierarchical pattern-based complex query of temporal knowledge graph

The complex query is an essential type of knowledge graph query, which aims to deal with the complex relationships among multiple triples involved in query problems. However, the existing query methods often directly search the target entities in large knowledge graphs, resulting in unsatisfactory query results. In addition, most of the methods focus on static knowledge graphs, ignoring the critical semantic information contained in time elements in query questions. Therefore, this paper proposes a complex query model for temporal knowledge graphs based on the hierarchical pattern. Firstly, aiming at the multi-level semantic relations in complex query problems, we use the entity mapping method to generate the corresponding query graphs. This method can accurately express the semantic relations by learning the constraints, such as time and quantity, in the query problem by manual rules. In addition, to reduce the impact of noisy data on query results, a complex query algorithm based on a hierarchical pattern is proposed. By decomposing the large-scale temporal knowledge graph into smaller knowledge sub-graphs, the search range of target results is effectively reduced. Finally, extensive experiments demonstrate that our model has certain advantages in executing complex queries.

Multi-hop path reasoning over sparse temporal knowledge graphs based on path completion and reward shaping

Multi-hop path reasoning plays a crucial role in temporal knowledge graph reasoning, aiming to infer deep complex relationships and obtain interpretable reasoning results. Previous reasoning models primarily focus on designing temporal knowledge graphs with rich paths between entities but struggle to handle path sparsity, known as sparse temporal knowledge graphs. Sparse temporal knowledge graphs store only essential knowledge information, resulting in missing connection paths between certain entities and encountering issues of sparse rewards and information scarcity. To tackle these challenges, this paper proposes a multi-hop path reasoning model over sparse temporal knowledge graph based on path completion and reward shaping (STKGR-PR). STKGR-PR dynamically completes missing paths using a temporal embedding model to alleviate path sparsity. To tackle the issue of sparse rewards caused by reduced hit rates, we propose semantic composition-based reasoning path embeddings derived from relation embeddings and timestamp embeddings for reward shaping. Considering the limited information contained in sparse temporal knowledge graphs, we incorporate time vectors into the embedding and multi-hop path reasoning models to enhance the accuracy of reasoning paths and results. Experimental results conducted on six sparse temporal datasets sampled from ICEWS14 and ICEWS515–05 demonstrate that STKGR-PR outperforms state-of-the-art multi-hop path reasoning models over temporal knowledge graphs across all evaluation metrics, while ensuring interpretability.

Event-Centric Temporal Knowledge Graph Construction: A Survey

Textual documents serve as representations of discussions on a variety of subjects. These discussions can vary in length and may encompass a range of events or factual information. Present trends in constructing knowledge bases primarily emphasize fact-based common sense reasoning, often overlooking the temporal dimension of events. Given the widespread presence of time-related information, addressing this temporal aspect could potentially enhance the quality of common-sense reasoning within existing knowledge graphs. In this comprehensive survey, we aim to identify and evaluate the key tasks involved in constructing temporal knowledge graphs centered around events. These tasks can be categorized into three main components: (a) event extraction, (b) the extraction of temporal relationships and attributes, and (c) the creation of event-based knowledge graphs and timelines. Our systematic review focuses on the examination of available datasets and language technologies for addressing these tasks. An in-depth comparison of various approaches reveals that the most promising results are achieved by employing state-of-the-art models leveraging large pre-trained language models. Despite the existence of multiple datasets, a noticeable gap exists in the availability of annotated data that could facilitate the development of comprehensive end-to-end models. Drawing insights from our findings, we engage in a discussion and propose four future directions for research in this domain. These directions encompass (a) the integration of pre-existing knowledge, (b) the development of end-to-end systems for constructing event-centric knowledge graphs, (c) the enhancement of knowledge graphs with event-centric information, and (d) the prediction of absolute temporal attributes.

Enhanced Temporal Knowledge Graph Completion via Learning High-Order Connectivity and Attribute Information

Temporal knowledge graph completion (TKGC) refers to the prediction and filling in of missing facts on time series, which is essential for many downstream applications. However, many existing TKGC methods suffer from two limitations: (1) they only consider direct relations between entities and fail to express high-order structural dependencies between entities; and (2) they only leverage relation quadruples of temporal knowledge graphs, ignoring attribute information that contains rich semantic information. This makes them vulnerable to sparsity and incompleteness problems. In response, we propose HCAE, a temporal knowledge graph completion model that includes high-order connectivity and attribute information. This consists mainly of a recursive embedding propagation layer and a multi-head attention aggregation layer. The former leverages a recursive mechanism to update entity embeddings and can learn high-order connectivity information between entities in linear complexity time. The latter leverages an attention mechanism to understand the importance of different attributes for entity representation automatically. Combining high-order connectivity and attribute information can lead to more diverse entity representations and help enhance the model’s ability to infer unknown entities. Comparative experiments on three real-world datasets show that the model’s inference accuracy significantly outperforms other benchmark methods, especially regarding knowledge graphs with many unknown entities or relations.

Learning to compensate for lack of information: Extracting latent knowledge for effective temporal knowledge graph completion

The goal of temporal knowledge graph embedding (TKGE) is to represent the entities and relations in a given temporal knowledge graph (TKG) as low-dimensional vectors (i.e., embeddings), which preserve both semantic information and temporal dynamics of the factual information. In this paper, we posit that the intrinsic difficulty of existing TKGE methods lies in the lack of information in KG snapshots with timestamps, each of which contains the facts that co-occur at a specific timestamp. To address this challenge, we propose a novel self-supervised TKGE approach, THOR (Three-tower grapH cOnvolution netwoRks (GCNs)), which extracts latent knowledge from TKGs by jointly leveraging both temporal and atemporal dependencies between entities and the structural dependency between relations. THOR learns the embeddings of entities and relations, obtained from three-tower GCNs by (1) maximizing the likelihood of the facts in a TKG and (2) addressing the lack of information in a TKG based on the auxiliary supervision signals of each entity. Our experiments on three real-world datasets demonstrate that THOR significantly outperforms 17 competitors in terms of TKG completion tasks. THOR yields up to 9.37% higher accuracy than the best competitor.

Joint framework for tensor decomposition-based temporal knowledge graph completion

Knowledge graphs (KGs) usually contain a lot of missing information, static knowledge graph completion (KGC) is widely used to solve their incompleteness. In recently, since the incompleteness of temporal KGs which typically contain lots of temporal facts, like (Barack Obama, Is-president-of, USA, 2008), temporal knowledge graph completion (TKGC) is proposed to predict the missing part of (?, Is-president-of, USA, 2008) or (Barack Obama, Is-president-of, ?, 2008). In particular, tensor decomposition method has shown excellent performance in KGC and therefore many existing methods extend it to TKGC. One of the key challenges is how to make full use of static information and effectively fuse static (non-temporal) and temporal information for improving the performance of TKGC. The existing models usually concatenate or sum the static and temporal embeddings directly. Moreover, we observe that neighborhood information of temporal facts may be very useful for inferring the missing parts of temporal facts while has not been fully explored and utilized.To address these challenges, we propose a joint framework composed of temporal and static modules for tensor decomposition-based TKGC. In temporal module, we propose a new neighborhood time sharing technology for aggregating richer temporal semantic information. The static module is proposed as an auxiliary model to further learn embedding representation of static information based on our proposed two training strategies (named typed entity strategy and adjacent active entity strategy). Finally, we propose a novel way of fusing static and temporal information through joint learning of two modules based on our proposed entity sharing technology. A series of comparison and ablation experiments show that our model can make better and full use of static information while capturing richer temporal semantic information, coupled with an effective fusion method, thus achieving state-of-the-art (SOTA) performance compared to existing tensor decomposition-based TKGC methods on four benchmark datasets.

Reinforcement learning with time intervals for temporal knowledge graph reasoning

Temporal reasoning methods have been successful in temporal knowledge graphs (TKGs) and are widely employed in many downstream application areas. Most existing TKG reasoning models transform time intervals into continuous time snapshots, with each snapshot representing a subgraph of the TKG. Although such processing can produce satisfactory outcomes, it ignores the integrity of a time interval and increases the amount of data. Meanwhile, many previous works focuses on the logic of sequentially occurring facts, disregarding the complex temporal logics of various time intervals. Consequently, we propose a Reinforcement Learning-based Model for Temporal Knowledge Graph Reasoning with Time Intervals (RTTI), which focuses on time-aware multi-hop reasoning arising from complex time intervals. In RTTI, we construct the time learning part to obtain the time embedding of the current path. It simulates the temporal logic with relation historical encoding and compute the time interval between two facts through the temporal logic feature matrix. Furthermore, we propose a new method for representing time intervals that breaks the original time interval embedding method, and express the time interval using median and embedding changes of two timestamps. We evaluate RTTI on four public TKGs for the link prediction task, and experimental results indicate that our approach can still perform well on more complicated tasks. Meanwhile, our method can search for more interpretable paths in the broader space and improve the reasoning ability in sparse spaces.

A relation enhanced model for temporal knowledge graph alignment

A relation enhanced model for temporal knowledge graph alignment

A survey of few-shot knowledge graph completion

With the continuous development of knowledge graph completion (KGC) technology, the problem of few-shot knowledge graph completion (FKGC) is becoming increasingly prominent. Traditional methods for KGC are not effective in addressing this problem due to the lack of sufficient data samples. Therefore, completing the task of knowledge graph with few-shot data has become an urgent issue that needs to be addressed and solved. This paper first presents a concise introduction to FKGC, which covers relevant definitions and highlights the advantages of FKGC techniques. We then categorize FKGC methods into meta-learning-based, metric-based, and graph neural network-based methods, and analyze the unique characteristics of each model. We also introduced the research on FKGC in a specific domain - Temporal Knowledge Graph Completion (TKGC). Subsequently, we summarized the commonly used datasets and evaluation metrics in existing methods and evaluated the completion performance of different models in TKGC. Finally, we presented the challenges faced by FKGC and provided directions for future research.

Temporal knowledge graph reasoning triggered by memories

Temporal knowledge graph reasoning triggered by memories

Vehicle Trajectory Completion for Automatic Number Plate Recognition Data: A Temporal Knowledge Graph-Based Method

Vehicle trajectories represent an essential information source in intelligent transportation systems. Prior trajectory completion models based on Automatic Number Plate Recognition (ANPR) data have typically depended on vehicle re-identification results or road network information or have employed static knowledge graphs to integrate the two information sources. However, these methods have not taken into account the implicit temporal characteristics of trajectories in ANPR data and have neglected individual vehicle preferences. To address this void, this study proposes a Temporal Knowledge Graph-based Vehicle Trajectory Completion Model (TKG-VTC). The model implementation comprises three stages: first, ANPR data are converted into a temporal trajectory knowledge graph; second, knowledge representation learning is conducted using nontemporal relations, a biased temporal regularizer and multivector embeddings to embed the knowledge on the graph; and finally, the embedded results are employed to perform link prediction for incomplete trajectories, thereby restoring vehicle trajectories in ANPR data. Through model evaluation metrics and dimensionality reduction experiments, TKG-VTC is observed to demonstrate the best performance in completing trajectories when compared to TComplEx, TNTComplEx, and TeLM. This research introduces an innovative application of employing temporal knowledge graphs for trajectory reconstruction, which eliminates dependence on vehicle re-identification and road network information in previous methodologies. This is advantageous for enhancing the performance and dependability of vehicle trajectory data in intelligent transportation systems, as well as facilitating the implementation of trajectory prediction, demand analysis, and accident warning applications.

MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs

Temporal knowledge graphs play an increasingly prominent role in scenarios such as social networks, finance, and smart cities. As such, research on temporal knowledge graphs continues to deepen. In particular, research on temporal knowledge graph reasoning holds great significance, as it can provide abundant knowledge for downstream tasks such as question answering and recommendation systems. Current reasoning research focuses primarily on interpolation and extrapolation. Extrapolation research aims to predict the likelihood of events occurring in future timestamps. Historical events are crucial for predicting future events. However, existing models struggle to fully capture the evolutionary characteristics of historical knowledge graphs. This paper proposes a multi-scale evolutionary network (MSEN) model that leverages Hierarchical Transfer aware Graph Neural Network (HT-GNN) in a local memory encoder to aggregate rich structural semantics from each timestamp’s knowledge graph. It also utilizes Time Related Graph Neural Network (TR-GNN) in a global memory encoder to model temporal-semantic dependencies of entities across the global knowledge graph, mining global evolutionary patterns. The model integrates information from both encoders to generate entity embeddings for predicting future events. The proposed MSEN model demonstrates strong performance compared to several baselines on typical benchmark datasets. Results show MSEN achieves the highest prediction accuracy.

Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation Systems

Traditional movie recommendation systems are increasingly falling short in the contemporary landscape of abundant information and evolving user behaviors. This study introduced the temporal knowledge graph recommender system (TKGRS), a ground-breaking algorithm that addresses the limitations of existing models. TKGRS uniquely integrates graph convolutional networks (GCNs), matrix factorization, and temporal decay factors to offer a robust and dynamic recommendation mechanism. The algorithm’s architecture comprises an initial embedding layer for identifying the user and item, followed by a GCN layer for a nuanced understanding of the relationships and fully connected layers for prediction. A temporal decay factor is also used to give weightage to recent user–item interactions. Empirical validation using the MovieLens 100K, 1M, and Douban datasets showed that TKGRS outperformed the state-of-the-art models according to the evaluation metrics, i.e., RMSE and MAE. This innovative approach sets a new standard in movie recommendation systems and opens avenues for future research in advanced graph algorithms and machine learning techniques.

Tensor decompositions for temporal knowledge graph completion with time perspective

Facts in the real world are often tied to time, such as the spread of diseases, and the state of military affairs. Therefore, knowledge graphs combined with temporal factors have gained growing attention. In the temporal knowledge graph, most researchers focus on the original facts and pay attention to their changes over time. The temporal factors are only used as auxiliary information for representation learning. In this paper, we try to observe from the perspective of time and find some interesting properties of temporal knowledge graph: (1) Simultaneousness. Various facts occur at the same time; (2) Aggregation. The facts may aggregately occur for a certain individual, organization, or location; (3) Associativity. Some specific relations tend to occur at specific times, such as celebrations at festivals. Based on the above three properties, we add a simple time-aware module to the existing tensor decomposition-based temporal knowledge graph model TComplEx (Lacroix et al., 2020), which obtains impressive improvements and achieves state-of-the-art results on four standard temporal knowledge graph completion benchmarks. Specifically, in terms of mean reciprocal rank (MRR), we advance the state-of-the-art by ＋24.0% on ICEWS14, ＋13.2% on ICEWS05-15, ＋31.9% on YAGO15k, and 4.7% on GDELT.

Analysis of the evolution of COVID-19 disease understanding through temporal knowledge graphs.

The COVID-19 pandemic highlighted two critical barriers hindering rapid response to novel pathogens. These include inefficient use of existing biological knowledge about treatments, compounds, gene interactions, proteins, etc. to fight new diseases, and the lack of assimilation and analysis of the fast-growing knowledge about new diseases to quickly develop new treatments, vaccines, and compounds. Overcoming these critical challenges has the potential to revolutionize global preparedness for future pandemics. Accordingly, this article introduces a novel knowledge graph application that functions as both a repository of life science knowledge and an analytics platform capable of extracting time-sensitive insights to uncover evolving disease dynamics and, importantly, researchers' evolving understanding. Specifically, we demonstrate how to extract time-bounded key concepts, also leveraging existing ontologies, from evolving scholarly articles to create a single temporal connected source of truth specifically related to COVID-19. By doing so, current knowledge can be promptly accessed by both humans and machines, from which further understanding of disease outbreaks can be derived. We present key findings from the temporal analysis, applied to a subset of the resulting knowledge graph known as the temporal keywords knowledge graph, and delve into the detailed capabilities provided by this innovative approach.