Temporal Knowledge Graph (TKG) reasoning aims to predict future facts based on historical data.However, due to the limitations in construction tools and data sources, many important associations between entities may be omitted in TKG.We refer to these missing associations as latent relations.Most of the existing methods have some drawbacks in explicitly capturing intra-time latent relations between co-occurring entities and inter-time latent relations between entities that appear at different times.To tackle these problems, we propose a novel Latent relations Learning method for TKG reasoning, namely L 2 TKG.Specifically, we first utilize a Structural Encoder (SE) to obtain representations of entities at each timestamp.We then design a Latent Relations Learning (LRL) module to mine and exploit the intraand inter-time latent relations.Finally, we extract the temporal representations from the output of SE and LRL for entity prediction.Extensive experiments on four datasets demonstrate the effectiveness of L 2 TKG.

Temporal Knowledge graph (TKG) reasoning aims to predict missing facts based on historical TKG data. Most of the existing methods are incapable of explicitly modeling the long-term time dependencies from history and neglect the adaptive integration of the long- and short-term information. To tackle these problems, we propose a novel method that utilizes a designed Hierarchical Relational Graph Neural Network to learn the Long- and Short-term representations for TKG reasoning, namely HGLS. Specifically, to explicitly associate entities in different timestamps, we first transform the TKG into a global graph. Based on the built graph, we design a Hierarchical Relational Graph Neural Network that executes in two levels: The sub-graph level is to capture the semantic dependencies within concurrent facts of each KG. And the global-graph level aims to model the temporal dependencies between entities. Furthermore, we design a module to extract the long- and short-term information from the output of these two levels. Finally, the long- and short-term representations are fused into a unified one by Gating Integration for entity prediction. Extensive experiments on four datasets demonstrate the effectiveness of HGLS.

Reasoning over Temporal Knowledge Graphs (TKGs) aims to predict future facts based on given history.One of the key challenges for prediction is to learn the evolution of facts.Most existing works focus on exploring evolutionary information in history to obtain effective temporal embeddings for entities and relations, but they ignore the variation in evolution patterns of facts, which makes them struggle to adapt to future data with different evolution patterns.Moreover, new entities continue to emerge along with the evolution of facts over time.Since existing models highly rely on historical information to learn embeddings for entities, they perform poorly on such entities with little historical information.To tackle these issues, we propose a novel Temporal Meta-learning framework for TKG reasoning, MetaTKG for brevity.Specifically, our method regards TKG prediction as many temporal meta-tasks, and utilizes the designed Temporal Meta-learner to learn evolutionary metaknowledge from these meta-tasks.The proposed method aims to guide the backbones to learn to adapt quickly to future data and deal with entities with little historical information by the learned meta-knowledge.Specially, in temporal meta-learner, we design a Gating Integration module to adaptively establish temporal correlations between meta-tasks.Extensive experiments on four widely-used datasets and three backbones demonstrate that our method can greatly improve the performance.

Classification of images based on the feelings generated by each image in its reviewers is becoming more and more popular. Due to the difficulty of gathering training data, this task is intrinsically a small-sample learning problem. Hence, the results produced by most existing solutions are less accurate. In this paper, we propose the semi-supervised hierarchical classification (SSHC) algorithm for emotional classification of color images. We extract three groups of features for each classification task and use those features in a two-level classification model that is based on the support vector machine (SVM) and Adaboost technique. To enlarge the training dataset, we employ each training image to retrieve similar images from the Internet cloud and jointly use the manually labeled small dataset and retrieved large but unlabeled dataset to train a classifier via semi-supervised learning. We have evaluated the proposed algorithm against the fuzzy similarity-based emotional classification (FSBEC) algorithm and another supervised hierarchical classification algorithm that does not learn from online images in three bi-class classification tasks, including “warm vs. cool”, “light vs. heavy” and “static vs. dynamic”. Our pilot results suggest that, by learning from the similar images archived in the Internet cloud, the proposed SSHC algorithm can produce more accurate emotional classification of color images.