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18th China National Conference on Computational Linguistics (CCL)

作者： Zhou, Bo Chen, Yubo Liu, Kang Zhao, Jun Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9783030323813;9783030323806

knowledge graph embedding aims at learning low-dimensional representations for entities and relations in knowledge graph. Previous knowledge graph embedding methods use just one score to measure the plausibility of a fact, which can't fully utilize the latent semantics of entities and relations. Meanwhile, they ignore the type of relations in knowledge graph and don't use fact type explicitly. We instead propose a model to fuse different scores of a fact and utilize relation and fact type information to supervise the training process. Specifically, scores by inner product of a fact and scores by neural network are fused with different weights to measure the plausibility of a fact. For each fact, besides modeling the plausibility, the model learns to classify different relations and differentiate positive facts from negative ones which can be seen as a muti-task method. Experiments show that our model achieves better link prediction performance than multiple strong baselines on two benchmark datasets WN18 and FB15k.

关键词： knowledge graph embedding Relation supervised Fact type supervised Weighted scores

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Enhancing Semantic Awareness in knowledge graph embedding 18

Enhancing Semantic Awareness in Knowledge Graph Embedding

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18th IEEE International Conference on Semantic Computing (ICSC)

作者： Xu, Gang Zhang, Wenbo Wang, Tao Chinese Acad Sci Technol Ctr Software Engn Inst Software Beijing Peoples R China

ISBN: (纸本)9798350385359

knowledge graph embedding aims to represent entities and relations as low dimensional vectors. The representative ability of low dimensional vectors is one of the most important aspects of knowledge graph embedding. knowledge graph embedding needs to represent two kinds of information: entity relation structures and entity semantic differences. However, the most existing approaches are good at entity relation structure representation, but fail to represent entity semantic differences which are common in real-world applications. To address the challenge, we propose a novel approach called SeTransE to enhance semantic awareness in knowledge graph embedding and achieve the aim of representing entity semantic differences. It mainly adopts a new method where a relation can rotate to a certain angle, which is different from traditional approaches. Experimental results on multiple benchmark knowledge graphs show our proposed approach not only represents relation structures, but also effectively models semantic structures and distinguish semantic differences.

关键词： semantic awareness knowledge graph knowledge graph embedding

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Weighted knowledge graph embedding 23

Weighted Knowledge Graph Embedding

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46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Zhang, Zhao Guan, Zhanpeng Zhang, Fuwei Zhuang, Fuzhen An, Zhulin Wang, Fei Xu, Yongjun Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Beihang Univ Inst Artificial Intelligence Zhongguancun Lab Beijing Peoples R China

ISBN: (纸本)9781450394086

knowledge graph embedding (KGE) aims to project both entities and relations in a knowledge graph (KG) into low-dimensional vectors. Indeed, existing KGs suffer from the data imbalance issue, i.e., entities and relations conform to a long-tail distribution, only a small portion of entities and relations occur frequently, while the vast majority of entities and relations only have a few training samples. Existing KGE methods assign equal weights to each entity and relation during the training process. Under this setting, long-tail entities and relations are not fully trained during training, leading to unreliable representations. In this paper, we propose WeightE, which attends differentially to different entities and relations. Specifically, WeightE is able to endow lower weights to frequent entities and relations, and higher weights to infrequent ones. In such manner, WeightE is capable of increasing the weights of long-tail entities and relations, and learning better representations for them. In particular, WeightE tailors bilevel optimization for the KGE task, where the inner level aims to learn reliable entity and relation embeddings, and the outer level attempts to assign appropriate weights for each entity and relation. Moreover, it is worth noting that our technique of applying weights to different entities and relations is general and flexible, which can be applied to a number of existing KGE models. Finally, we extensively validate the superiority of WeightE against various state-of-the-art baselines.

关键词： knowledge graph embedding Bilevel Optimization Link Prediction

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A Transformer-based knowledge graph embedding Model Combining graph Paths and Local Neighborhood

A Transformer-based Knowledge Graph Embedding Model Combinin...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhu, Tong Tan, Huobin Chen, Xinyu Ren, Yating Beihang Univ Sch Software Beijing Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Many existing knowledge graph embedding methods achieve outstanding performance by exploiting the graph structure, among which graph neural network-based methods that utilize the local neighborhood are the most representative. However, the shallow network structure of graph neural networks limits the model's expressiveness, and the existing problem of over-smoothing also prevents the model from capturing long-distance information. To address these issues, we propose a Transformer-based knowledge graph embedding method that combines graph paths and local neighborhood (TKGE-PN). First, it samples multiple graph paths by using a biased random walk algorithm starting from the central entity. Then these sampled paths are transformed into vector representations by a Transformer-based graph path encoding module. Finally, the local neighborhood encoding module aggregates all graph path vector representations to score triples. During the graph path encoding process, a masked entity relation prediction task is used to enhance the model's ability to learn long-distance information. Experimental results show that on two standard datasets, FB15k-237 and WN18RR, the performance of TKGE-PN surpasses that of most existing models, demonstrating the effectiveness of our approach.

关键词： knowledge graph knowledge graph completion knowledge graph embedding graph path local neighborhood Transformer

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Ontological Concept Structure Aware knowledge Transfer for Inductive knowledge graph embedding

Ontological Concept Structure Aware Knowledge Transfer for I...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ren, Chao Zhang, Le Fan, Lintao Xu, Tong Wang, Zhefeng Yuan, Senchao Chen, Enhong Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Huawei Technol Shenzhen Peoples R China

ISBN: (纸本)9780738133669

Conventional knowledge graph embedding methods mainly assume that all entities at reasoning stage are available in the original training graph. But in real-world application scenarios, newly emerged entities are always inevitable, which results in the severe problem of out-of-knowledge-graph entities. Existing efforts on this issue mostly either utilize additional resources, e.g., entity descriptions, or simply aggregate in-knowledge-graph neighbors to embed these new entities inductively. However, high-quality additional resources are usually hard to obtain and existing neighbors of new entities may be too sparse to provide enough information for modeling these entities. Meanwhile, they may fail to integrate the rich information of ontological concepts, which provide a general figure of instance entities and usually remain unchanged in knowledge graph. To this end, we propose a novel inductive framework namely CatE to solve the sparsity problem with the enhancement from ontological concepts. Specifically, we first adopt the transformer encoder to model the complex contextual structure of the ontological concepts. Then, we further develop a template refinement strategy for generating the target entity embedding, where the concept embedding is used to form a basic skeleton of the target entity and the individual characteristics of the entity will be enriched by its existing neighbors. Finally, extensive experiments on public datasets demonstrate the effectiveness of our proposed model compared with state-of-the-art baseline methods.

关键词： knowledge graph embedding ontological concepts out-of-knowledge-graph entities

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Multi-hop Question Answering with knowledge graph embedding in a Similar Semantic Space

Multi-hop Question Answering with Knowledge Graph Embedding ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Li, Fengying Chen, Mingdong Dong, Rongsheng Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Multi-hop Question Answering using the knowledge graph (KG) as a data source requires subject entities and relations that are obtained from natural language questions;the answers are then obtained by reasoning through multiple triples in the KG. However, even large KGs are incomplete, and the reasoning often fails to obtain the correct answer due to the lack of relations in the KG. Recently, researchers have proposed introducing KG embedding into the multi-hop knowledge graph question answering (KGQA) field to solve the incompleteness of the KG. However, these methods embed the question and the KG into different semantic spaces, and it is difficult to obtain the correct answers. Furthermore, due to the limitation of the question embedding sequence, the contribution of each word to the question semantics cannot be distinguished. To overcome the above problems, this paper proposes an effective multi-hop KGQA model, TIPNet, using relation embeddings in knowledge graph triples, which uses the idea of translation models to narrow the semantic spatial distance between question embeddings and KG embeddings. At the same time, TIP weighting technology is proposed to distinguish the semantic contribution of words to the question. To validate the performance of TIPNet, experiments were conducted on the WebQSP and CWQ datasets, and the model reached advanced levels under both KG-full and KG-half settings.

关键词： question answering knowledge graph transformer knowledge graph embedding

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Research on Adverse Drug Reaction Prediction Model Combining knowledge graph embedding and Deep Learning 4

Research on Adverse Drug Reaction Prediction Model Combining...

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4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)

作者： Li, Yufeng Zhao, Wenchao Dang, Bo Yan, Xu Gao, Min Wang, Weimin Xiao, Mingxuan Univ Southampton Southampton Hants England Univ Sci & Technol China Hefei Anhui Peoples R China San Francisco Bay Univ Fremont Comp Sci Fremont CA USA Trine Univ Phoenix AZ USA Trine Univ Allen Pk MI USA Hong Kong Univ Sci & Technol Hong Kong Peoples R China SouthWest JiaoTong Univ Chengdu Sichuan Peoples R China

ISBN: (纸本)9798350375084;9798350375077

In clinical treatment, identifying potential adverse reactions of drugs can help assist doctors in making medication decisions. In response to the problems in previous studies that features are high-dimensional and sparse, independent prediction models need to be constructed for each adverse reaction of drugs, and the prediction accuracy is low, this paper develops an adverse drug reaction prediction model based on knowledge graph embedding and deep learning, which can predict experimental results. Unified prediction of adverse drug reactions covered. knowledge graph embedding technology can fuse the associated information between drugs and alleviate the shortcomings of high-dimensional sparsity in feature matrices, and the efficient training capabilities of deep learning can improve the prediction accuracy of the model. This article builds an adverse drug reaction knowledge graph based on drug feature data;by analyzing the embedding effect of the knowledge graph under different embedding strategies, the best embedding strategy is selected to obtain sample vectors;and then a convolutional neural network model is constructed to predict adverse reactions. The results show that under the DistMult embedding model and 400-dimensional embedding strategy, the convolutional neural network model has the best prediction effect;the average accuracy, F-1 score, recall rate and area under the curve of repeated experiments are better than the methods reported in the literature. The obtained prediction model has good prediction accuracy and stability, and can provide an effective reference for later safe medication guidance.

关键词： knowledge graph embedding deep learning convolutional neural network prediction model adverse drug reactions (ADRs)

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Fast variational knowledge graph embedding 5

Fast variational knowledge graph embedding

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2024 International Conference on Quantum Computing and Engineering

作者： Giri, Pulak Ranjan Kurokawa, Mori Saito, Kazuhiro KDDI Res Inc Fujimino Saitama Japan

ISBN: (纸本)9798331541378

embedding of a knowledge graph(KG) entities and relations in the form of vectors is an important aspect for the manipulation of the KG database for several downstream tasks, such as link prediction, knowledge graph completion, and recommendation. Because of the growing size of the knowledge graph databases, it has become a daunting task for the classical computer to train a model efficiently. Quantum computer can help speedup the embedding process of the KGs by encoding the entities into a variational quantum circuit of polynomial depth. Usually, the time complexity for such variational circuit-dependent quantum classical algorithms for each epoch is O(N poly(log M)), where N is number of elements in the knowledge graph and M is the number of features of each entities of the knowledge graph. In this article we exploit additional quantum advantage by training multiple elements of KG in superpositions, thereby reducing the computing time further for the knowledge graph embedding model.

关键词： knowledge graph embedding Link prediction Variational quantum circuit

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GGAT: knowledge graph embedding Model via Global Information

GGAT: Knowledge Graph Embedding Model via Global Information

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Asia Conference on Algorithms, Computing and Machine Learning (CACML))

作者： Wang, Zhe Guo, Zhongwen Ocean Univ China Fac Informat Sci & Engn Qingdao Peoples R China

ISBN: (数字)9781665482905

ISBN: (纸本)9781665482905

Recently, knowledge graph embedding model based on graph Attention Network (GAT) has shown great potential in link prediction task. However, the existing GAT basedmodels ignore the global information in the neighborhood. We propose GGAT, a knowledge graph embedding model based on global information. The encoding ability of GGAT is enhanced by using global information. Meanwhile, we employ multi-head attention mechanism to improve GGAT's perception of the interaction between entities in the neighborhood. In addition, GGAT uses residual structure to improve the stability of the model and the ability to perceive remote semantic connections. Experiments on two link prediction benchmarks demonstrate the proposed key capabilities of GGAT. Moreover, we set a new state-of-the-art on a knowledge graph dataset.

关键词： knowledge graph embedding graph Attention Network global information

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Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics

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CELL GENOMICS 2024年第10期4卷 100655-100655页

作者： Ni, Shengkun Kong, Xiangtai Zhang, Yingying Chen, Zhengyang Wang, Zhaokun Fu, Zunyun Huo, Ruifeng Tong, Xiaochu Qu, Ning Wu, Xiaolong Wang, Kun Zhang, Wei Zhang, Runze Zhang, Zimei Shi, Jiangshan Wang, Yitian Yang, Ruirui Li, Xutong Zhang, Sulin Zheng, Mingyue Chinese Acad Sci Shanghai Inst Mat Med Drug Discovery & Design Ctr State Key Lab Drug Res 555 Zuchongzhi Rd Shanghai 201203 Peoples R China Univ Chinese Acad Sci 19A Yuquan Rd Beijing 100049 Peoples R China Univ Sci & Technol China Sch Life Sci Div Life Sci & Med Hefei 230026 Peoples R China Univ Sci & Technol China Affiliated Hosp USTC 1 Anhui Prov Hosp Div Life Sci & Med Hefei 230001 Peoples R China Nanjing Univ Chinese Med 138 Xianlin Rd Nanjing 210023 Peoples R China East China Univ Sci & Technol Sch Pharm Shanghai 200237 Peoples R China Univ Chinese Acad Sci Hangzhou Inst Adv Study Sch Pharmaceut Sci & Technol Hangzhou 310024 Peoples R China

The emergence of perturbation transcriptomics provides a new perspective for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to deconvolute compound-protein interactions from perturbation transcriptomics with knowledge graph embedding. By considering multi-level regulatory events within biological systems that share the same semantic context, PertKGE significantly improves deconvoluting accuracy in two critical "cold-start"settings: inferring targets for new compounds and conducting virtual screening for new targets. We further demonstrate the pivotal role of incorporating multi-level regulatory events in alleviating representational biases. Notably, it enables the identification of ectonucleotide pyrophosphatase/ phosphodiesterase-1 as the target responsible for the unique anti-tumor immunotherapy effect of tankyrase inhibitor K-756 and the discovery of five novel hits targeting the emerging cancer therapeutic target aldehyde dehydrogenase 1B1 with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery.

关键词： perturbation transcriptomics machine learning knowledge graph embedding drug discovery compound-protein interaction target inference virtual screening

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