Temporal Knowledge Graphs (TKGs) are being widely explored to predict the future for they record multi-relational knowledge and the happening time of real-life facts. Existing works learn sequential patterns to infer the future from past facts in TKGs for predictions. Although achieving promising results, they are restricted by the sequential modeling in both efficiency and effectiveness. To resolve these limitations, we propose our efficient and effective non-sequential relational modeling (NoSeq). NoSeq works non-sequentially for temporal patterns where it transforms the happening time into time intervals. Time intervals are the period of time between happening time and prediction time which state the temporal distance clearly. Both time intervals and relations are represented using embeddings and merged non-sequentially into entity embeddings for future predictions. We evaluate NoSeq on four datasets from the perspective of effectiveness, efficiency, sensitivity, and the ability to transfer. Consistent better performances verify our idea.

Temporal Knowledge Graphs (TKGs) represent real-life facts using entities, relational types, and timestamps where relational types state the semantic scenario of facts. Current methods learn embeddings by merging facts of multiple types (e.g. sport and family) for predictions. Such embeddings associate well with relations of multiple types. However, the prediction needs only information of a single type, i.e. embeddings contain irrelevant relational types. In this paper, we explore whether embeddings with irrelevant information confuse predictions and propose RefE to improve embeddings by refining meanings. RefE enhances the ability for predicting links of a specific type while maintaining associations of multiple relational types. RefE consists of general learning and embedding refining modules. General learning embeds facts of multiple types to represent general meanings, and embedding refining emphasizes facts of the single type that matches the prediction. RefE uses both general and refined embeddings for predictions. Experimental results on four datasets verify the effectiveness of RefE.

Knowledge graph completion (KGC) aims to infer missing information in incomplete knowledge graphs (KGs). Most previous works only consider the transductive scenario where entities are existing in KGs, which cannot work effectively for the inductive scenario containing emerging entities. Recently some graph neural network-based methods have been proposed for inductive KGC by aggregating neighborhood information to capture some uncertainty semantics from the neighboring auxiliary triples. But these methods ignore the more general relational semantics underlying all the known triples that can provide richer information to represent emerging entities so as to satisfy the inductive scenario. In this paper, we propose a novel model called CFAG, which utilizes two granularity levels of relational semantics in a coarse-grained aggregator (CG-AGG) and a fine-grained generative adversarial net (FG-GAN), for inductive KGC. The CG-AGG firstly generates entity representations with multiple semantics through a hypergraph neural network-based global aggregator and a graph neural network-based local aggregator, and the FG-GAN further enhances entity representations with specific semantics through conditional generative adversarial nets. Experimental results on benchmark datasets show that our model outperforms state-of-the-art models for inductive KGC.

Group activities have become an essential part of people’s daily life, which stimulates the requirement for intensive research on the group recommendation task, i.e., recommending items to a group of users. Most existing works focus on aggregating users’ interests within the group to learn group preference. These methods are faced with two problems. First, these methods only model the user preference inside a single group while ignoring the collaborative relations among users and items across different groups. Second, they assume that group preference is an aggregation of user interests, and factually a group may pursue some targets not derived from users’ interests. Thus they are insufficient to model the general group preferences which are independent of existing user interests. To address the above issues, we propose a novel dual channel Hypergraph Convolutional network for group Recommendation (HCR), which consists of member-level preference network and group-level preference network. In the member-level preference network, in order to capture cross-group collaborative connections among users and items, we devise a member-level hypergraph convolutional network to learn group members’ personal preferences. In the group-level preference network, the group’s general preference is captured by a group-level graph convolutional network based on group similarity. We evaluate our model on two real-world datasets and the experimental results show that the proposed model significantly and consistently outperforms state-of-the-art group recommendation techniques.

Image captioning is a challenging task that combines computer vision and natural language processing for generating a textual description of the content within an image. Recently, Transformer-based encoder-decoder architectures have shown great success in image captioning, where multi-head attention mechanism is utilized to capture the contextual interactions between object regions. However, such methods regard region features as a bag of tokens without considering the directional relationships between them, making it hard to understand the relative position between objects in the image and generate correct captions effectively. In this paper, we propose a novel Direction Relation Transformer to improve the orientation perception between visual features by incorporating the relative direction embedding into multi-head attention, termed DRT. We first generate the relative direction matrix according to the positional information of the object regions, and then explore three forms of direction-aware multi-head attention to integrate the direction embedding into Transformer architecture. We conduct experiments on challenging Microsoft COCO image captioning benchmark. The quantitative and qualitative results demonstrate that, by integrating the relative directional relation, our proposed approach achieves significant improvements over all evaluation metrics compared with baseline model, e.g., DRT improves task-specific metric CIDEr score from 129.7% to 133.2% on the offline ''Karpathy'' test split.