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Joint entity-relation knowledge embedding via cost-sensitive learning

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Frontiers of Information Technology & Electronic Engineering 2017年第11期18卷 1867-1873页

作者： Sheng-kang YU Xue-yi ZHAO Xi LI Zhong-fei ZHANG College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China College of Computer Science and Technology Zhejiang University Hangzhou 310027 China

As a joint-optimization problem which simultaneously fulfills two different but correlated embedding tasks （i.e., entity embedding and relation embedding）, knowledge embedding problem is solved in a joint embedding scheme. In this embedding scheme, we design a joint compatibility scoring function to quantitatively evaluate the relational facts with respect to entities and relations, and further incorporate the scoring function into the maxmargin structure learning process that explicitly learns the embedding vectors of entities and relations using the context information of the knowledge base. By optimizing the joint problem, our design is capable of effectively capturing the intrinsic topological structures in the learned embedding spaces. Experimental results demonstrate the effectiveness of our embedding scheme in characterizing the semantic correlations among different relation units, and in relation prediction for knowledge inference.

关键词： knowledge embedding Joint embedding Cost-sensitive learning

Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks

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Theoretical & Applied Mechanics Letters 2024年第2期14卷 90-97页

作者： Hanyu Zhou Haochen Li Yaomin Zhao HEDPS Center for Applied Physics and Technologyand College of EngineeringPeking UniversityBeijing 100871China State Key Laboratory for Turbulence and Complex Systems College of EngineeringPeking UniversityBeijing 100871China

Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable *** the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior ***,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural *** networks are designed to represent explicit functional expressions and optimize them with data *** particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE *** proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.

关键词： PDE discovery Gene Expression Programming Deep Learning knowledge embedding

Future Event Prediction Based on Temporal knowledge Graph embedding

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Computer Systems Science & Engineering 2023年第3期44卷 2411-2423页

作者： Zhipeng Li Shanshan Feng Jun Shi Yang Zhou Yong Liao Yangzhao Yang Yangyang Li Nenghai Yu Xun Shao University of Science and Technology of China Hefei230027China Shenzhen CyberAray Co. Ltd.Shenzhen518038China Harbin Institute of Technology Shenzhen518055China National Engineering Research Center for Risk Perception and Prevention(RPP) Beijing100041China School of Regional Innovation and Social Design Engineering Kitami Institute of TechnologyKitami090-8507Japan

Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and *** graph is a general language for describing and modeling complex *** types of events continually occur,which are often related to historical and concurrent *** this paper,we formalize the future event prediction as a temporal knowledge graph reasoning *** existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training *** a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the *** address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge ***,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events *** paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence *** specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and *** evaluate our proposed method on four benchmark *** experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.

关键词： Event prediction temporal knowledge graph graph representation learning knowledge embedding

Improving knowledge Graph Completion Using Soft Rules and Adversarial Learning

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Chinese Journal of Electronics 2021年第4期30卷 623-633页

作者： TANG Caifang RAO Yuan YU Hualei SUN Ling CHENG Jiamin WANG Yutian Xi'an Key Laboratory of Social Intelligence and Complexity Data Processing School of Software EngineeringXi'an Jiaotong University Shaanxi Joint Key Laboratory for Artifact Intelligence School of Software EngineeringXi'an Jiaotong University School of Software Engineering Xi'an Jiaotong University

knowledge graph is a useful resources and tools for describing entities and relationships in natural language processing tasks. However, the existing knowledge graph are incomplete. Therefore, knowledge graph completion technology has become a research hotspot in the field of artificial intelligence, but the traditional knowledge graph embedding method does not fully take into account the role of logic rules and the effect of false negative samples on knowledge *** on the logic rules of knowledge and the role of adversarial learning in knowledge embedding, we proposes a model to improve the completion of knowledge graph:soft Rules and graph adversarial learning(RUGA). Firstly,the traditional knowledge graph embedding model is trained as generator and discriminator by using adversarial learning method, and high-quality negative samples are obtained. Then these negative samples and the existing positive samples together constitute the label triple in the injection rule model. The whole model will benefit from both high-quality samples and logical rules. In addition,we evaluated the performance of link prediction task and triple classification task on Freebase and Yago datasets respectively. Finally, the experimental results show that the model can effectively improve the effect of knowledge graph completion.

关键词： knowledge graph knowledge embedding knowledge graph completion Adversarial learning

COKG-QA: Multi-hop Question Answering over COVID-19 knowledge Graphs

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Data Intelligence 2022年第3期4卷 471-492页

作者： Huifang Du Zhongwen Le Haofen Wang Yunwen Chen Jing Yu College of Design and Innovation Tongji UniversityShanghai 200092China School of Computer Science Fudan UniversityShanghai 200433China DataGrand Inc. Shanghai 201203China

COVID-19 evolves rapidly and an enormous number of people worldwide desire instant access to COVID-19 information such as the overview, clinic knowledge, vaccine, prevention measures, and COVID-19 mutation. Question answering(QA) has become the mainstream interaction way for users to consume the ever-growing information by posing natural language questions. Therefore, it is urgent and necessary to develop a QA system to offer consulting services all the time to relieve the stress of health services. In particular, people increasingly pay more attention to complex multi-hop questions rather than simple ones during the lasting pandemic, but the existing COVID-19 QA systems fail to meet their complex information needs. In this paper, we introduce a novel multi-hop QA system called COKG-QA, which reasons over multiple relations over large-scale COVID-19 knowledge Graphs to return answers given a question. In the field of question answering over knowledge graph, current methods usually represent entities and schemas based on some knowledge embedding models and represent questions using pre-trained models. While it is convenient to represent different knowledge(i.e., entities and questions) based on specified embeddings, an issue raises that these separate representations come from heterogeneous vector spaces. We align question embeddings with knowledge embeddings in a common semantic space by a simple but effective embedding projection mechanism. Furthermore, we propose combining entity embeddings with their corresponding schema embeddings which served as important prior knowledge, to help search for the correct answer entity of specified types. In addition, we derive a large multi-hop Chinese COVID-19 dataset(called COKG-DATA for remembering) for COKG-QA based on the linked knowledge graph Open KG-COVID-19 launched by Open KG1, including comprehensive and representative information about COVID-19. COKG-QA achieves quite competitive performance in the 1-hop and 2-hop dat

关键词： COVID-19 Question answering knowledge graph knowledge embedding Pre-trained model Multi-hop KGQA

knowledge Graph based Mutual Attention for Machine Reading Comprehension over Anti-Terrorism Corpus

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Data Intelligence 2023年第3期5卷 685-706页

作者： Feng Gao Jin Hou Jinguang Gu Lihua Zhang School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065Hubei The Key Laboratory of Rich-Media knowledge Organization and Service of Digital Publishing Content Insitute of Scientic and Technical Information of ChinaBeijing 100038China Wuhan University of Science and Technology Big Data Science and Engineering Research Institute Wuhan 430065Hubei Eastchina Jiaotong University Nanchang 330013Jiangxi

Machine reading comprehension has been a research focus in natural language processing and intelligence ***,there is a lack of models and datasets for the MRC tasks in the anti-terrorism ***,current research lacks the ability to embed accurate background knowledge and provide precise *** address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic ***,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the *** eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the *** results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.

关键词： Machine reading comprehension Anti-terrorism domain knowledge embedding knowledge attention Mutual attention

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