Knowledge graph embedding (KGE) is an increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wide spectrum of applications such as link prediction, knowledge reasoning and knowledge completion. In this article, we provide a systematic review of existing KGE techniques based on representation spaces. Particularly, we build a fine-grained classification to categorise the models based on three mathematical perspectives of the representation spaces: (1) algebraic perspective, (2) geometric perspective and (3) analytical perspective. We introduce the rigorous definitions of fundamental mathematical spaces before diving into KGE models and their mathematical properties. We further discuss different KGE methods over the three categories, as well as summarise how spatial advantages work over different embedding needs. By collating the experimental results from downstream tasks, we also explore the advantages of mathematical space in different scenarios and the reasons behind them. We further state some promising research directions from a representation space perspective, with which we hope to inspire researchers to design their KGE models as well as their related applications with more consideration of their mathematical space properties.

Recommender systems have become an important instrument to connect people to information. Sparse, complex, and rapidly growing data presents new challenges to traditional recommendation algorithms. To overcome these challenges, various deep learning-based recommendation algorithms have been proposed. Among these, Variational AutoEncoder (VAE) -based recommendation methods stand out. VAEs are based on a flexible probabilistic framework, which is robust for data sparsity and compatible with other deep learning-based models for dealing with multimodal data. In addition, the deep generative structure of VAEs helps to perform Bayesian inference in an efficient manner. VAE-based recommendation algorithms have given rise to many novel graphical models, and they have achieved promising performance. In this article, we conduct a survey to systematically summarize recent VAE-based recommendation algorithms. Four frequently used characteristics of VAE-based recommendation algorithms are summarized, and a taxonomy of VAE-based recommendation algorithms is proposed. We also identify future research directions for, advanced perspectives on, and the application of VAEs in recommendation algorithms, to inspire future work on VAEs for recommender systems.

Guanzheng Chen, Fangyu Liu, Zaiqiao Meng, Shangsong Liang. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

Multimodality Representation Learning, as a technique of learning to embed information from different modalities and their correlations, has achieved remarkable success on a variety of applications, such as Visual Question Answering (VQA), Natural Language for Visual Reasoning (NLVR), and Vision Language Retrieval (VLR). Among these applications, cross-modal interaction and complementary information from different modalities are crucial for advanced models to perform any multimodal task, e.g., understand, recognize, retrieve, or generate optimally. Researchers have proposed diverse methods to address these tasks. The different variants of transformer-based architectures performed extraordinarily on multiple modalities. This survey presents the comprehensive literature on the evolution and enhancement of deep learning multimodal architectures to deal with textual, visual and audio features for diverse cross-modal and modern multimodal tasks. This study summarizes the ( i ) recent task-specific deep learning methodologies, ( ii ) the pretraining types and multimodal pretraining objectives, ( iii ) from state-of-the-art pretrained multimodal approaches to unifying architectures, and ( iv ) multimodal task categories and possible future improvements that can be devised for better multimodal learning. Moreover, we prepare a dataset section for new researchers that covers most of the benchmarks for pretraining and finetuning. Finally, major challenges, gaps, and potential research topics are explored. A constantly-updated paperlist related to our survey is maintained at https://github.com/marslanm/multimodality-representation-learning .

MicroRNAs (miRNAs) silence genes by binding to messenger RNAs, whereas long non-coding RNAs (lncRNAs) act as competitive endogenous RNAs (ceRNAs) that can relieve miRNA silencing effects and upregulate target gene expression. The ceRNA association between lncRNAs and miRNAs has been a research hotspot due to its medical importance, but it is challenging to verify experimentally. In this paper, we propose a novel deep learning scheme, i.e. sequence pre-training-based graph neural network (SPGNN), that combines pre-training and fine-tuning stages to predict lncRNA–miRNA associations from RNA sequences and the existing interactions represented as a graph. First, we utilize a sequence-to-vector technique to generate pre-trained embeddings based on the sequences of all RNAs during the pre-training stage. In the fine-tuning stage, we use Graph Neural Network to learn node representations from the heterogeneous graph constructed using lncRNA–miRNA association information. We evaluate our proposed scheme SPGNN on our newly collected animal lncRNA–miRNA association dataset and demonstrate that combining the $k$-mer technique and Doc2vec model for pre-training with the Simple Graph Convolution Network for fine-tuning is effective in predicting lncRNA–miRNA associations. Our approach outperforms state-of-the-art baselines across various evaluation metrics. We also conduct an ablation study and hyperparameter analysis to verify the effectiveness of each component and parameter of our scheme. The complete code and dataset are available on GitHub: https://github.com/zixwang/SPGNN.