Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. We consider knowledge transfer and retention of the learning on growing snapshots of a KG without having to learn embeddings from scratch. The proposed model includes a masked KG autoencoder for embedding learning and update, with an embedding transfer strategy to inject the learned knowledge into the new entity and relation embeddings, and an embedding regularization method to avoid catastrophic forgetting. To investigate the impacts of different aspects of KG growth, we construct four datasets to evaluate the performance of lifelong KG embedding. Experimental results show that the proposed model outperforms the state-of-the-art inductive and lifelong embedding baselines.

Entity alignment is to find identical entities in different knowledge graphs (KGs) that refer to the same real-world object. Embedding-based entity alignment techniques have been drawing a lot of attention recently because they can help solve the issue of symbolic heterogeneity in different KGs. However, in this paper, we show that the progress made in the past was due to biased and unchallenging evaluation. We highlight two major flaws in existing datasets that favor embedding-based entity alignment techniques, i.e., the isomorphic graph structures in relation triples and the weak heterogeneity in attribute triples. Towards a critical evaluation of embedding-based entity alignment methods, we construct a new dataset with heterogeneous relations and attributes based on event-centric KGs. We conduct extensive experiments to evaluate existing popular methods, and find that they fail to achieve promising performance. As a new approach to this difficult problem, we propose a time-aware literal encoder for entity alignment. The dataset and source code are publicly available to foster future research. Our work calls for more effective and practical embedding-based solutions to entity alignment.

实体对齐是知识图融合中的至关重要任务。但是，大多数实体对准方法都有可伸缩性问题。最近的方法通过将大型公斤分成小块来解决这个问题，以嵌入和对齐学习。但是，这种分区和学习过程导致结构和对齐过度损失过多。因此，在这项工作中，我们提出了一种可扩展的基于GNN的实体对准方法，以从三个角度降低结构和对齐损失。首先，我们提出一种基于中心性的子图生成算法，以回顾一些具有不同子图之间桥梁的地标实体。其次，我们介绍了自我监督的实体重建，以从不完整的邻里子图中恢复实体表示形式，并设计了跨纸笔负面抽样，以在对齐学习中纳入其他子图中的实体。第三，在推理过程中，我们合并子图的嵌入，以制作一个单个空间进行对齐搜索。基准开放数据集和提议的大型DBPEDIA1M数据集的实验结果验证了我们方法的有效性。

多年来，旨在从已知事实中推断出新结论的知识图（KGS）的推理主要集中在静态KG上。现实生活中知识的不断增长提出了使能够扩大KGS的归纳推理能力的必要性。现有的归纳工作假设新实体都在批处理中一次出现，这过度简化了新实体不断出现的实际情况。这项研究探讨了一个更现实，更具挑战性的环境，新实体分为多批次。我们提出了一个基于步行的归纳推理模型来解决新环境。具体而言，具有自适应关系聚合的图形卷积网络旨在使用其邻近关系编码和更新实体。为了捕捉不同的邻居的重要性，我们在聚合过程中采用了一种查询反馈注意机制。此外，为了减轻新实体的稀疏链接问题，我们提出了一种链接增强策略，以将可信赖的事实添加到KGS中。我们构建了三个新数据集，用于模拟此多批次出现方案。实验结果表明，我们所提出的模型优于基于最先进的基于嵌入的，基于步行的基于步行和基于规则的模型。

知识图（KG）嵌入寻求学习实体和关系的向量表示。传统的模型理由是图形结构，但它们遭受了图形不完整和长尾实体的问题。最近的研究使用了预训练的语言模型根据实体和关系的文本信息来学习嵌入，但它们无法利用图形结构。在论文中，我们从经验上表明，这两种特征是KG嵌入的互补性。为此，我们提出了Cole，Cole是一种用于嵌入KG的共同介绍方法，可利用图形结构和文本信息的互补性。其图形嵌入模型使用变压器从其邻域子图中重建实体的表示。其文本嵌入模型使用预训练的语言模型来从其名称，描述和关系邻居的软提示中生成实体表示。为了让两个模型相互推广，我们提出了共同依据学习，使他们可以从彼此的预测逻辑中提取选择性知识。在我们的共同阶段学习中，每个模型既是老师又是学生。基准数据集上的实验表明，这两个模型的表现优于其相关基线，而与共同介绍学习的集合方法Cole可以推进KG嵌入的最先进。

本文介绍了$ \ mu \ text {kg} $，一个开源python库，用于在知识图上进行表示。 $ \ mu \ text {kg} $支持通过多源知识图（以及单个知识图），多个深度学习库（Pytorch和Tensorflow2），多个嵌入任务（链接预​​测，实体对准，实体键入，实体键入），支持联合表示。 ，以及多源链接预测）以及多个并行计算模式（多进程和多GPU计算）。它目前实现26个流行知识图嵌入模型，并支持16个基准数据集。 $ \ mu \ text {kg} $提供了具有不同任务的简化管道的嵌入技术的高级实现。它还带有高质量的文档，以易于使用。 $ \ mu \ text {kg} $比现有的知识图嵌入库更全面。它对于对各种嵌入模型和任务进行彻底比较和分析非常有用。我们表明，共同学习的嵌入可以极大地帮助知识驱动的下游任务，例如多跳知识图形答案。我们将与相关字段中的最新发展保持一致，并将其纳入$ \ mu \ text {kg} $中。

实体对齐是知识图（kg）集成中的基本且至关重要的技术。多年来，对实体一致性的研究一直存在于KG是静态的假设，该假设忽略了现实世界KG的生长本质。随着KG的成长，先前的一致性结果面临需要重新审视的，而新实体对齐等待被发现。在本文中，我们建议并深入研究现实但未开发的设置，称为持续实体对齐。为了避免在新实体和三元组来时对整个KGS进行整个模型，我们为此任务提供了一种持续的对齐方法。它基于实体邻接，重建实体的表示，使其能够使用其现有邻居快速而有归纳的新实体生成嵌入。它选择并重播部分预先对准的实体对，仅训练一部分KG，同时提取可信赖的知识对准知识增强。由于不可避免地要包含与以前的作品不同的不可匹配的实体，因此所提出的方法采用双向最近的邻居匹配来找到新的实体对齐并更新旧的对齐。此外，我们还通过模拟多语言dbpedia的增长来构建新数据集。广泛的实验表明，我们的持续比对方法比基于再培训或归纳学习的基准更有效。

Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment analysis task that aims to extract triplets of aspect terms, sentiments, and opinion terms from review sentences. Recently, span-level models achieve gratifying results on ASTE task by taking advantage of the predictions of all possible spans. Since all possible spans significantly increases the number of potential aspect and opinion candidates, it is crucial and challenging to efficiently extract the triplet elements among them. In this paper, we present a span-level bidirectional network which utilizes all possible spans as input and extracts triplets from spans bidirectionally. Specifically, we devise both the aspect decoder and opinion decoder to decode the span representations and extract triples from aspect-to-opinion and opinion-to-aspect directions. With these two decoders complementing with each other, the whole network can extract triplets from spans more comprehensively. Moreover, considering that mutual exclusion cannot be guaranteed between the spans, we design a similar span separation loss to facilitate the downstream task of distinguishing the correct span by expanding the KL divergence of similar spans during the training process; in the inference process, we adopt an inference strategy to remove conflicting triplets from the results base on their confidence scores. Experimental results show that our framework not only significantly outperforms state-of-the-art methods, but achieves better performance in predicting triplets with multi-token entities and extracting triplets in sentences contain multi-triplets.

实体对齐是将知识图（KGS）与多个源集成的重要步骤。以前的实体对齐尝试已经探索了不同的kg结构，例如基于邻域和基于路径的上下文，以学习实体嵌入物，但它们受到捕获多上下文特征的限制。此外，大多数方法直接利用嵌入相似性以确定实体对齐，而不考虑实体和关系之间的全局互动。在这项工作中，我们提出了一个明智的多上下文实体对齐（IMEA）模型来解决这些问题。特别是，我们引入变压器以灵活地捕获关系，路径和邻域背景，并根据嵌入相似度和关系/实体功能设计整体推理以估计对齐概率。从整体推理获得的对准证据通过所提出的软标签编辑进一步注入变压器，以通知嵌入学习。与现有的最先进的实体对准方法相比，若干基准数据集上的实验结果证明了IMEA模型的优越性。

This is a brief technical report of our proposed method for Multiple-Object Tracking (MOT) Challenge in Complex Environments. In this paper, we treat the MOT task as a two-stage task including human detection and trajectory matching. Specifically, we designed an improved human detector and associated most of detection to guarantee the integrity of the motion trajectory. We also propose a location-wise matching matrix to obtain more accurate trace matching. Without any model merging, our method achieves 66.672 HOTA and 93.971 MOTA on the DanceTrack challenge dataset.