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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Zepeng Zhai, Minyu Li, Xiao Huang, Rikui Liang, Chenhui Hu, Bin School of Information Science & Engineering Lanzhou University Lanzhou China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350368741

The task of knowledge graph entity typing (KGET) aims to infer the missing types for entities in knowledge graphs, which is a significant subtask of knowledge graph completion (KGC). In despite of its progress, we observe that the sparsity of the dataset greatly affects the task itself as well as downstream tasks. In this paper, we propose a framework to alleviate this problem, which consists of data augmentation and type inference. We introduce a Statistics-based entity Type Data Augmentation (SET-DA) method in data augmentation phase, which calculates a type probability distribution for each entity by statistically determining the global relation-type statistics information, and then employ embedding-based KGET models for type inference. Experimental results on two widely used datasets indicate that the proposed framework can solve the data sparsity problem in the task of KGET and the performance of models trained with data augmentation by SET-DA significantly outperforms previous state-of-the-art methods. © 2025 IEEE.

关键词： Data Augmentation knowledge graph Completion knowledge graph entity typing

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knowledge graph entity typing with Contrastive Learning 2022

Knowledge Graph Entity Typing with Contrastive Learning

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6th International Conference on Machine Learning and Soft Computing (ICMLSC)

作者： Zhu, Guozhen Yao, Shunyu Beijing Univ Posts & Telecommun Beijing Beijing Peoples R China

ISBN: (纸本)9781450387477

knowledge graph entity typing is an important way to complete knowledge graphs (KGs), aims at predicting the associating types of certain given entities. However, previous methods suppose that many (entity, entity type) pairs can be obtained for each entity type, performing poorly on entity types that only have a few associative entities. Besides, these methods cannot fully exploit the inherent correlation and complementarity information across different entities sharing the same entity type. To this end, we propose a novel model named Contrastive entity typing (CET) for KG entity tying. CET can better learn the mutual interactions among the entities with the same entity type and can fully utilize the hierarchical information in entity type trees by two contrastive learning modules. The main benefit of the proposed contrastive learning modules is that they can effectively encourage the consistency of the entity representations with the same type while improving the discriminability of the entity type classifiers. Empirically, our model achieves state-of-the-art results on KG entity typing benchmarks.

关键词： knowledge graph entity typing Contrastive learning graph convolutional network

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A performant and incremental algorithm for knowledge graph entity typing

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2023年第5期26卷 2453-2470页

作者： Li, Zepeng Huang, Rikui Zhai, Minyu Zhang, Zhenwen Hu, Bin Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Gansu Peoples R China Beijing Inst Technol Sch Med Technol Beijing 100081 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci CAS Ctr Excellence Brain Sci & Intelligence Techno Shanghai 200000 Peoples R China

knowledge graph entity typing (KGET) is a subtask of knowledge graph completion, which aims at inferring missing entity types by utilizing existing type knowledge and triple knowledge of the knowledge graph. Previous knowledge graph embedding (KGE) algorithms infer entity types through trained entity embeddings. However, for new unseen entities, KGE models encounter obstacles in inferring their types. In addition, it is also difficult for KGE models to improve the performance incrementally with the increase of added data. In this paper, we propose a statistic-based KGET algorithm which aims to take both performance and incrementality into consideration. The algorithm aggregates the neighborhood information and type co-occurrence information of target entities to infer their types. Specifically, we first compute the type probability distribution of the target entity in the semantic context of given fact triple. Then the probability information of fact triples involved in the target entity is aggregated. In addition to local neighborhood information, we also consider capturing global type co-occurrence information for target entities to enhance inference performance. Extensive experiments show that our algorithm outperforms previous statistics-based KGET algorithms and even some KGE models. Finally, we design an incremental inference experiment, which verifies the superiority of our algorithm in predicting the types of new entities, and the experiment also verifies that our algorithm has excellent incremental property.

关键词： knowledge graph entity typing knowledge graph completion Incremental inference

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Connecting Embeddings Based on Multiplex Relational graph Attention Networks for knowledge graph entity typing

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2023年第5期35卷 4608-4620页

作者： Zhao, Yu Zhou, Han Zhang, Anxiang Xie, Ruobing Li, Qing Zhuang, Fuzhen Southwestern Univ Finance & Econ Financial Intelligence & Financial Engn Key Lab Si Fintech Innovat Ctr Chengdu 610074 Sichuan Peoples R China Baidu Inc Beijing 100085 Peoples R China Carnegie Mellon Univ Shool Comp Sci Pittsburgh PA 15213 USA Tencent Search Prod Ctr WeChat Search Applicat Dept Shenzhen 518054 Peoples R China Beihang Univ Inst Artificial Intelligence Sch Comp Sci Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci SKLSDE Beijing 100191 Peoples R China

knowledge graph entity typing (KGET) aims to infer missing entity typing instances in KGs, which is a significant subtask of KG completion. Despite of its progress, however, we observe that it still faces two non-trivial challenges: (i) most existing KGET methods extract features by encoding the existing entity typing tuples, while underutilizing or even ignoring rich relational knowledge. (ii) they typically treat each entity typing tuple in KGs independently, and thus inevitably fail to take account of the inherent and valuable neighborhood information surrounding a tuple. To address these challenges, we build a novel Heterogeneous Relational graph (HRG), and propose a Multiplex Relational graph Attention Networks (MRGAT) to learn on HRG, and then utilize a Connecting Embeddings model (ConnectE) to make entity type inference. Specifically, the overall framework contains three significant components. First, to effectively integrate the heterogeneous structural information including the entity typing tuples and entity relation triples in KGs, we construct a heterogeneous relational graph that consists of three semantic subgraphs. Second, we employ MRGAT to learn embeddings on HRG. In MRGAT, each subgraph of HRG is fed to its corresponding model that is capable of capturing neighborhood information by aggregating the surrounding nodes' features. Finally, given the learned embeddings, we make entity type prediction by the connecting embeddings method ConnectE. Experimental results demonstrate the effectiveness of our proposed model against various state-of-the-art baselines.

关键词： Multiplexing Task analysis knowledge engineering Semantics Decoding Predictive models Data models knowledge graph entity typing multiplex relational graph attention networks embeddings learning

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AsyncET: Asynchronous Representation Learning for knowledge graph entity typing 24

AsyncET: Asynchronous Representation Learning for Knowledge ...

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30th ACM SIGKDD Conference on knowledge Discovery and Data Mining

作者： Wang, Yun-Cheng Ge, Xiou Wang, Bin Kuo, C. -C. Jay Univ Southern Calif Los Angeles CA 90007 USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798400704901

knowledge graph entity typing (KGET) aims to predict the missing entity types in knowledge graphs (KG). The relationship between entities and their corresponding types is often expressed using a single relation, hasType. However, hasType has a limited capability for modeling diverse entity-type relationships in the embedding space. In this paper, we first introduce multiple auxiliary relations to model the complex entity-type relationship. We propose an efficient and robust algorithm to group similar entity types together and assign a unique auxiliary relation to each group. Then, with the auxiliary relations, we propose an Asynchronous representation learning framework for KGET, named AsyncET, where entity and type embeddings are updated alternatively. Consequently, the quality of entity embeddings is gradually improved during training by infusing type information. In addition, entity types with different granularities and semantics can be properly modeled in the embedding space. Experimental results show that AsyncET can substantially improve the performance of embedding-based methods on the KGET task and has a significant advantage over state-of-the-art neural network-based methods in terms of model sizes and inference time.

关键词： knowledge graph Embedding knowledge graph entity typing Representation Learning Taxonomy

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Few-Shot knowledge graph entity typing 1

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26th Pacific-Asia Conference on knowledge Discovery and Data Mining (PAKDD)

作者： Zhu, Guozhen Zhang, Zhongbao Su, Sen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (数字)9783031059339

ISBN: (纸本)9783031059339;9783031059322

knowledge graph entity typing, which is an important way to complete knowledge graphs (KGs), aims at predicting the associating type of certain given entities without any external knowledge. However, previous methods suppose that many (entity, entity type) pairs (ETPs) can be obtained for each entity type, performing poorly on entity types that only have a few associative entities and do not fully utilize the internal information in KGs. In this work, we propose a novel model named Meta entity typing (MET) for few-shot knowledge graph entity typing. In MET, we achieve knowledge graph entity typing by meta-learning with three sub-tasks formed by the hierarchical entity type tree in its meta-training stage. In this way, MET can focus on transferring type-specific meta information to learn the most important knowledge for entity typing. Besides, to fully employ the internal information in KGs given limited ETPs, inspired by Factorization Machines, we design a novel Relation To Relation graph Convolutional Networks (R2R-GCN), in which we consider different relation combinations could have distinct influence on its corresponding entity, R2R-GCN can explicitly model the interactions between different relations. Empirically, our model achieves state-of-the-art results on few-shot entity typing KG benchmarks.

关键词： knowledge graph entity typing Few-shot learning graph convolutional networks

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A GPT-assisted Multi-Granularity Contrastive Learning approach for knowledge graph entity typing

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ACM Transactions on Intelligent Systems and Technology 1000年

作者： Hongbin Zhang Tao Wang Zhuowei Wang Nankai Lin Chong Chen Lianglun Cheng Guangdong University of Technology China

knowledge graph entity typing (KGET) is an efficient way to infer possible missing types for entities, which has become a key instrument to enhance the construction of knowledge graphs (KGs). Existing models to KGET have mainly focused on a single granularity information such as distinct entity information, but other granularity information including entity-to-type-clusters, the same cluster and interaction information have not been fully explored, resulting in inferring incorrect types in KGs. To address this, we propose a GPT-assisted Multi-Granularity Contrastive Learning (GMGCL) approach to acquire entity-to-type-clusters, entity, type-cluster and relation information by GPT-assisted entity-to-type-clusters clustering, entity-based, cluster-based and relation-based contrastive learning, respectively. Our approach is evaluated on FB15kET and YAGO43kET datasets, outperforming other baselines and obtaining a 1.35% average improvement at least on MRR.

关键词： knowledge graph entity typing Generative Pre-trained Transformer Contrastive Learning knowledge Representation

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