However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols.

Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entities over time. Recently, many novel models are proposed for ML on such graphs intensifying the need for robust evaluation and standardized benchmark datasets. However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols. To address these challenges, we introduce Temporal Graph Benchmark 2.0 (TGB 2.0), a novel benchmarking framework tailored for evaluating methods for predicting future links on Temporal Knowledge Graphs and Temporal Heterogeneous Graphs with a focus on large-scale datasets, extending the Temporal Graph Benchmark.

Multi-hop logical reasoning over knowledge graph plays a fundamental role in many artificial intelligence tasks. Recent complex query embedding methods for reasoning focus on static KGs, while temporal knowledge graphs have not been fully explored. Reasoning over TKGs has two challenges: 1. The query should answer entities or timestamps; 2. The operators should consider both set logic on entity set and temporal logic on timestamp set.To bridge this gap, we introduce the multi-hop logical reasoning problem on TKGs and then propose the first temporal complex query embedding named Temporal Feature-Logic Embedding framework (TFLEX) to answer the temporal complex queries. Specifically, we utilize fuzzy logic to compute the logic part of the Temporal Feature-Logic embedding, thus naturally modeling all first-order logic operations on the entity set.

In this paper, we investigate a realistic but underexplored problem, called few-shot temporal knowledge graph reasoning, that aims to predict future facts for newly emerging entities based on extremely limited observations in evolving graphs. It offers practical value in applications that need to derive instant new knowledge about new entities in temporal knowledge graphs (TKGs) with minimal supervision. The challenges mainly come from the few-shot and time shift properties of new entities. First, the limited observations associated with them are insufficient for training a model from scratch. Second, the potentially dynamic distributions from the initially observable facts to the future facts ask for explicitly modeling the evolving characteristics of new entities.

However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols.

We address the task of temporal knowledge graph (TKG) forecasting by introducing a fully explainable method based on temporal rules. Motivated by recent work proposing a strong baseline using recurrent facts, our approach learns four simple types of rules with a confidence function that considers both recency and frequency. Evaluated on nine datasets, our method matches or surpasses the performance of eight state-of-the-art models and two baselines, while providing fully interpretable predictions.

The supplementary material is structured as follows: Section A.1 gives the proof and analysis of Theorem 3.1; Section A.2 introduces the datasets and their statistics in detail; Section A.3 introduces the baselines utilized in experiments; Section A.4 discusses the experimental setup of baseline models as well as MetaTKGR; Section A.5 reports detailed experiment performance with statistical test results; A.1 Statements, Proof and Analysis of Theorem 3.1 Thus, we can improve the generalization ability of our meta-learner over time by the following update step by step, A.2 Datasets Figure 1: Number of entities over time. New entities continuously emerge on three public TKGs. Integrated Crisis Early Warning System (ICEWS18) is the collection of coded interactions between 3 socio-political actors which are extracted from news articles. Y AGO). Figure 1 shows the amount of new entities appearing over time. Figure 2 shows the corresponding distributions.