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42nd European Conference on IR Research

作者： Jia, Ningning Cheng, Xiang Su, Sen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9783030454388;9783030454395

knowledge graphs' incompleteness has motivated many researchers to propose methods to automatically infer missing facts in knowledge graphs. knowledge graph embedding has been an active research area for knowledge graph completion, with great improvement from the early TransE to the current state-of-the-art ConvKB. ConvKB considers a knowledge graph as a set of triples, and employs a convolutional neural network to capture global relationships and transitional characteristics between entities and relations in the knowledge graph. However, it only utilizes the triple information, and ignores the rich information contained in relation paths. In fact, a path of one relation describes the relation from some aspect in a fine-grained way. Therefore, it is beneficial to take relation paths into consideration for knowledge graph embedding. In this paper, we present a novel convolutional neural network-based embedding model PConvKB, which improves knowledge graph embedding by incorporating relation paths locally and globally. Specifically, we introduce attention mechanism to measure the local importance of relation paths. Moreover, we propose a simple yet effective measure DIPF to compute the global importance of relation paths. Experimental results show that our model achieves substantial improvements against state-of-the-art methods.

关键词： knowledge graph embedding Link prediction Triple classification Convolutional neural network Attention mechanism

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knowledge graph embedding Based On Multiinformation Fusion

Knowledge Graph Embedding Based On Multiinformation Fusion

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第一届计算机科学与教育信息化国际会议

作者： Dehai Zhang Yuncong He Yanpeng Wang Qing Liu Ying Yang Software College Yunnan University

The knowledge graph embedding aims to represent the elements in the knowledge graph as lowdimensional dense distributed representation vectors in the continuous semantic space, which has achieved good results in the knowledge graph completion. However, the existing embedding methods do not make full use of and extract the existing information in the knowledge graph, which will directly affect the quality of knowledge graph embedding. In order to make the embedded model can fuse more reasonable c0 onstraints and rich semantic information, in this paper, the entities in the traditional triples are divided into concepts and entities, and through the conceptual constraint information generated by the entity, the structural information of the triple and the entity description text information encoded by the deep learning, the hidden relationship between the existing entities in the knowledge graph is mined, and a more accurate semantic space representation is obtained. Experiments show that the embedded model proposed in this paper has better performance.

关键词： knowledge graph embedding knowledge graph completion distributed representation link prediction

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N-MlpE: Optimizing Multilayer Perceptron Network-based knowledge graph embedding Model with Neighborhood Information 29

N-MlpE: Optimizing Multilayer Perceptron Network-based Knowl...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Qing Ren, Xiaoli Ren, Kaijun Lin, Jiarun Li, Xiaoyong National University of Defense Technology College of Meteorology and Oceanography Changsha China

ISBN: (纸本)9798350330717

As an effective knowledge organizing and modeling technique, knowledge graph has become a key topic in graph research, but the practical application of KG is limited by its incompleteness. In recent years, many knowledge graph embedding(KGE) methods for knowledge graph completion(KGC) based on graph neural networks(GNN) have been proposed. However, most GNN-based KGC models are still suffer from the encoder-decoder structure of low efficiency in aggregating neighborhood information and the difficulty of model training. This paper present an optimized model that incorporates Neighborhood information into knowledge inference, to improve the performance of KGC models based on multilayer perceptron network(MLP), which is named N-MlpE. We generate an input sequence that includes the query triplet and its neighbor entities and relationships, and then feed it to an adaptive filter module to remove useless neighbors for the inference to improve the accuracy of the inference, and reduce the computational complexity of training the model. The filtered sequence is then fed into a weight calculation module and a feature extraction module simultaneously, the former is designed based on selfattention to model the relevant rule inference, which enhances the interpretability of KGE models, and the latter is based on MLP and used to capture the long-distance interactions between triplets, which can significantly improve the accuracy of inference. Extensive experiments are conducted on two standard KG datasets WN18RR and FB15k237 to verify the effectiveness of N-MlpE, the results show that the accuracy of N-MlpE model outperforms most GNN-based models. © 2023 IEEE.

关键词： distributed representation inference knowledge graph completion knowledge graph embedding link prediction

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Learning knowledge graph embedding with a dual-attention embedding network

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 212卷

作者： Fang, Haichuan Wang, Youwei Tian, Zhen Ye, Yangdong Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China

knowledge graph embedding (KGE) aims to retain the intrinsic structural information of knowledge graphs (KGs) via representation learning, which is critical for various downstream tasks including personalized recommendations, intelligent search, and relation extraction. The graph convolutional network (GCN), due to its remarkable performance in modeling graph data, has recently been studied extensively in the KGE field. However, when learning entity representations, most attention-based GCN approaches treat neighborhoods as a whole to measure their importance without considering the direction information of relations. Additionally, these approaches make relation representations perform self-update via a learnable matrix, resulting in ignoring the impact of neighborhood information on representation learning of relations. To this end, this study presents an innovative framework, namely learning knowledge graph embedding with a dual-attention embedding network (D-AEN), to jointly propagate and update the representations of both relations and entities via fusing neighborhood information. Here the dual attentions consist of a bidirectional attention mechanism and a relation-specific attention mechanism for jointly measuring the importance of neighborhoods in respectively learning entity and relation representations. Thus D-AEN enables elements like relations and entities to interact well semantically, which makes their learned representations retain more effective information of KGs. Extensive experimental results on three standard link prediction datasets demonstrate the superiority of D-AEN over several state-of-the-art approaches.

关键词： knowledge graph embedding knowledge graph graph convolutional network Representation learning Attention mechanism

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A collaborative learning framework for knowledge graph embedding and reasoning

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knowledge-BASED SYSTEMS 2024年 289卷

作者： Wang, Hao Song, Dandan Wu, Zhijing Li, Jia Zhou, Yanru Xu, Jing Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China

knowledge graph embedding (KGE) and knowledge graph reasoning (KGR) aim to automatic completion of knowledge graph (KG). The difference is that most KGE models learn the embedded representation at a triple level. In contrast, KGR models focus more on optimizing decision -making and enhancing the interpretability of reasoning processes with multi -hop paths. As a result, KGE models are better at learning triplet embeddings, whereas KGR models can capture the multihop information between entity pairs. However, KGE and KGR models only focus on one aspect that affects the completion performance. This paper proposes a plug -and -play collaborative learning framework (CLF) for jointly enhancing knowledge graph embedding and reasoning, which can accommodate existing KGR and KGE models. The two models exchange training experiences in this framework to realize mutual learning through a collaborative learning module. In this module, a new distance function is designed to maintain the independence of candidate entities' probabilities and avoid information loss. Furthermore, a knowledge augmentation module is designed to identify missing key triples to assist in the further iterative training of the framework. Extensive experiments on the benchmark datasets demonstrate that our framework significantly improves the performance of existing models.

关键词： knowledge graph completion knowledge graph embedding knowledge graph reasoning Multi-hop reasoning

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Towards the Web of embeddings: Integrating multiple knowledge graph embedding spaces with FedCoder

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JOURNAL OF WEB SEMANTICS 2023年 75卷

作者： Baumgartner, Matthias Dell'Aglio, Daniele Paulheim, Heiko Bernstein, Abraham Univ Zurich Dept Informat Zurich Switzerland Aalborg Univ Dept Comp Sci Aalborg Denmark Univ Mannheim Data & Web Sci Grp Mannheim Germany

The Semantic Web is distributed yet interoperable: Distributed since resources are created and published by a variety of producers, tailored to their specific needs and knowledge;Interoperable as entities are linked across resources, allowing to use resources from different providers in concord. Complementary to the explicit usage of Semantic Web resources, embedding methods made them applicable to machine learning tasks. Subsequently, embedding models for numerous tasks and structures have been developed, and embedding spaces for various resources have been published. The ecosystem of embedding spaces is distributed but not interoperable: Entity embeddings are not readily comparable across different spaces. To parallel the Web of Data with a Web of embeddings, we must thus integrate available embedding spaces into a uniform *** integration approaches are limited to two spaces and presume that both of them were embedded with the same method - both assumptions are unlikely to hold in the context of a Web of embeddings. In this paper, we present FedCoder- an approach that integrates multiple embedding spaces via a latent space. We assert that linked entities have a similar representation in the latent space so that entities become comparable across embedding spaces. FedCoder employs an autoencoder to learn this latent space from linked as well as non-linked *** experiments show that FedCoder substantially outperforms state-of-the-art approaches when faced with different embedding models, that it scales better than previous methods in the number of embedding spaces, and that it improves with more graphs being integrated whilst performing comparably with current approaches that assumed joint learning of the embeddings and were, usually, limited to two sources. Our results demonstrate that FedCoder is well adapted to integrate the distributed, diverse, and large ecosystem of embeddings spaces into an interoperable Web of embeddings.(c) 2022 The Author(

关键词： knowledge graphs knowledge graph embedding embedding space integration Web of embeddings

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Kernel multi-attention neural network for knowledge graph embedding

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knowledge-BASED SYSTEMS 2021年 227卷 107188-107188页

作者： Jiang, Dan Wang, Ronggui Yang, Juan Xue, Lixia Hefei Univ Technol Sch Comp & Informat Hefei 230601 Peoples R China

Link prediction is the problem of predicting missing link between entities and relations for knowledge graph. In recent years, some tasks have achieved great success for link prediction, but these tasks are far from expanding entity relation vectors, and cannot predict missing links more efficiently. In this paper, we propose a novel link prediction method called kernel multi-attention neural network for knowledge graph embedding (KMAE) which is able to extend kernel separately in entity and relation attributes. The kernel function uses Gaussian kernel function to expand into more robust entity kernel and relation kernel. In addition, we constructed a novel multi-attention neural network that acts on the entity kernel and relation kernel which can capture local important characteristics. Experiments on FB15k-237 and WN18RR, show that multi-attention fully reflect excellent performance in the task of knowledge graph embedding. Our proposed KMAE achieves better results than previous state-of-the-art link prediction methods. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Link prediction Entity kernel Relation kernel Multi-attention neural network knowledge graph embedding

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Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

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BMC BIOINFORMATICS 2023年第1期24卷 488-488页

作者： Djeddi, Warith Eddine Hermi, Khalil Ben Yahia, Sadok Diallo, Gayo Univ Tunis Manar Fac Sci Tunis LR11ES14 Campus Univ Tunis 2092 Tunisia Univ Jendouba High Inst Informat Kef Saleh Ayech Jendouba 8189 Tunisia Tallinn Univ Technol Dept Software Sci Ehitajate Tee-5 EE-12618 Tallinn Estonia Univ Bordeaux Bordeaux Populat Hlth Inserm 1219 Rue Leo Saignat F-33000 Bordeaux France Southern Syddansk Univ Maersk Mc Kinney Moller Inst Als 2 DK-6400 Sonderborg Denmark

BackgroundThe pharmaceutical field faces a significant challenge in validating drug target interactions (DTIs) due to the time and cost involved, leading to only a fraction being experimentally verified. To expedite drug discovery, accurate computational methods are essential for predicting potential interactions. Recently, machine learning techniques, particularly graph-based methods, have gained prominence. These methods utilize networks of drugs and targets, employing knowledge graph embedding (KGE) to represent structured information from knowledge graphs in a continuous vector space. This phenomenon highlights the growing inclination to utilize graph topologies as a means to improve the precision of predicting DTIs, hence addressing the pressing requirement for effective computational methodologies in the field of drug *** present study presents a novel approach called DTIOG for the prediction of DTIs. The methodology employed in this study involves the utilization of a KGE strategy, together with the incorporation of contextual information obtained from protein sequences. More specifically, the study makes use of Protein Bidirectional Encoder Representations from Transformers (ProtBERT) for this purpose. DTIOG utilizes a two-step process to compute embedding vectors using KGE techniques. Additionally, it employs ProtBERT to determine target-target similarity. Different similarity measures, such as Cosine similarity or Euclidean distance, are utilized in the prediction procedure. In addition to the contextual embedding, the proposed unique approach incorporates local representations obtained from the Simplified Molecular Input Line Entry Specification (SMILES) of drugs and the amino acid sequences of protein *** effectiveness of the proposed approach was assessed through extensive experimentation on datasets pertaining to Enzymes, Ion Channels, and G-protein-coupled Receptors. The remarkable efficacy of DTIOG was showcased th

关键词： Drug-target interaction prediction knowledge graph embedding COVID-19 Cosine similarity ProtBERT

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Learning knowledge graph embedding with multi-granularity relational augmentation network

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期233卷

作者： Xue, Zengcan Zhang, Zhaoli Liu, Hai Yang, Shuoqiu Han, Shuyun Cent China Normal Univ Natl Engn Res Ctr E Learning Wuhan 430070 Peoples R China

knowledge graph embedding (KGE) aims to complete link prediction tasks effectively by learning the representation of entity and relation. Recently, deep neural networks have achieved prominent results for learning KGE. However, knowledge graphs typically contain massive information about entities and relations, and existing deep neural network-based KGE models exploit semantic information from simple explicit feature concatenation and reshaping without considering bidirectional implicit interactions, which cannot accentuate relevant predictive information. Additionally, these models simply extract nonlinear features from embedded matrices of relations, resulting in neglecting the distinctive semantic connotation of heterogeneous relations. To overcome these two challenges, a multi-granularity relational augmentation network (MRAN) to learn KGE is proposed. Specifically, multi-granularity implicit interaction embeddings are generated to facilitate relevant predictive information selection, which model bidirectional implicit interactions between entities and relations in Euclidean space, complex space, and quaternion space. Moreover, relational augmentation convolution module utilizes relation-aware filters at the convolutional layer to preserve the distinctive relational properties for dealing with heterogeneous relations. Subsequently, the augmentation mechanism is injected to focus on informative features and restrain useless features of neural networks, which boosts the quality of KGE. Experimental results demonstrate the outstanding link prediction results of MRAN on five benchmark datasets, revealing the benefits of considering bidirectional implicit interactions and heterogeneous relations. Our model substantially improves efficiency while achieving excellent performance, which is superior to state-of-the-art baseline models in 2 out of 4 metrics on WN18 and in 3 out of 4 metrics on FB15k-237 and YAGO3-10 datasets.

关键词： knowledge graph embedding Multi -granularity implicit interaction Heterogeneous relation Augmentation feature

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Learning high-order structural and attribute information by knowledge graph attention networks for enhancing knowledge graph embedding

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knowledge-BASED SYSTEMS 2022年 250卷

作者： Liu, Wenqiang Cai, Hongyun Cheng, Xu Xie, Sifa Yu, Yipeng Dukehyzhang Tencent Inc Interact Entertainment Grp Shenzhen Peoples R China

The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding space. Many recent works have demonstrated the benefits of knowledge graph embedding on knowledge graph completion task, such as relation extraction. However, we observe that: (1) existing methods simply take direct relations between entities into consideration and fails to express high-order structural relationships between entities;(2) these methods simply leverage relation triples of knowledge graphs while ignoring a large number of attribute triples that encode rich semantic information. To overcome these limitations, this paper proposes a novel knowledge graph embedding method, named (KANE), which is inspired by the recent developments in graph convolutional networks (GCN). KANE can capture both high-order structural and attribute information of knowledge graphs in an efficient, explicit and unified manner under the graph convolutional networks framework. Empirical results on three datasets show that KANE significantly outperforms seven state-of-the-art methods. Further analysis verify the efficiency of our method and the benefits brought by the attention mechanism. (C) 2022 Elsevier B.V. All rights reserved.

关键词： knowledge graph Representation learning knowledge graph embedding

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