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knowledge graph for Identifying Geological Disasters by Integrating Computer Vision with Ontology

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Journal of Earth Science 2023年第5期34卷 1418-1432页

作者： Qinjun Qiu Zhong Xie Die Zhang Kai Ma Liufeng Tao Yongjian Tan Zhipeng Zhang Baode Jiang School of Computer Science China University of GeosciencesWuhan 430078China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China National Engineering Research Center of Geographic Information System Wuhan 430074China School of Resource and Environmental Sciences Wuhan UniversityWuhan 430074China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China

The occurrence of geological disasters can have a large impact on urban safety. Protecting people’s safety is the most important concern when disasters occur. Safety improvement requires a large amount of comprehensive and representative risk analysis and a large collection of information related to geological hazards, including unstructured knowledge and experience. To address the relevant information and support safety risk analysis, a geological hazard knowledge graph is developed automatically based on computer vision and domain-geoscience ontology to identify geological hazards from input images while obeying safety rules and regulations, even when affected by changes. In the implementation of the knowledge graph, we design an ontology schema of geological disasters based on a top-down approach, and by organizing knowledge as a logical semantic expression, it can be shared using ontology technologies and therefore enable semantic interoperability. Computer vision approaches are then used to automatically detect a set of entities and attributes, using the data from input images, and object types and their attributes are identified so that they can be stored in Neo4j for reasoning and searching. Finally, a reasoning model for geological hazard identification was developed using the Neo4j database to create nodes, relationships, and their properties for modeling, and geological hazards in the images can be automatically identified by searching the Neo4j database. An application on geological hazard is presented. The results show the effectiveness of the proposed approach in terms of identifying possible potential hazards in geological hazards and assisting in formulating targeted preventive measures.

关键词： geological hazard computer vision knowledge graph city safety ontology

knowledge graph Construction and Applications for Web Search and Beyond

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Data Intelligence 2019年第4期1卷 333-349页

作者： Peilu Wang Hao Jiang Jingfang Xu Qi Zhang Sogou Inc. Beijing 100084China

knowledge graph(KG)has played an important role in enhancing the performance of many intelligent *** this paper,we introduce the solution of building a large-scale multi-source knowledge graph from scratch in Sogou Inc.,including its architecture,technical implementation and *** previous works that build knowledge graph with graph databases,we build the knowledge graph on top of SogouQdb,a distributed search engine developed by Sogou Web Search Department,which can be easily scaled to support petabytes of *** a supplement to the search engine,we also introduce a series of models to support inference and graph based ***,the data of Sogou knowledge graph that are collected from 136 different websites and constantly updated consist of 54 million entities and over 600 million entity *** also introduce three applications of knowledge graph in Sogou Inc.:entity detection and linking,knowledge based question answering and knowledge based dialog *** applications have been used in Web search products to help user acquire information more efficiently.

关键词： knowledge graph Search engine Question answering

Unsupervised Cross-Media Hashing Learning via knowledge graph

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Chinese Journal of Electronics 2022年第6期31卷 1081-1091页

作者： YE Zhaoda HE Xiangteng PENG Yuxin Wangxuan Institute of Computer Technology Peking University

With the rapid growth of multimedia data, cross-media hashing has become an important technology for fast cross-media retrieval. Because the manual annotations are difficult to obtain in real-world application, unsupervised cross-media hashing is studied to address the hashing learning without manual *** unsupervised cross-media hashing methods generally focus on calculating the similarities through the features of multimedia data, while the learned hashing code cannot reflect the semantic relationship among the multimedia data, which hinders the accuracy in the cross-media retrieval. When humans try to understand multimedia data, the knowledge of concept relations in our brain plays an important role in obtaining high-level *** by this, we propose a knowledge guided unsupervised cross-media hashing(KGUCH) approach, which applies the knowledge graph to construct high-level semantic correlations for unsupervised cross-media hash *** contributions in this paper can be summarized as follows: 1) The knowledge graph is introduced as auxiliary knowledge to construct the semantic graph for the concepts in each image and text instance, which can bridge the multimedia data with high-level semantic correlations to improve the accuracy of learned hash codes for crossmedia retrieval. 2) The proposed KGUCH approach constructs correlation of the multimedia data from both the semantic and the feature aspects, which can exploit complementary information to promote the unsupervised cross-media hash learning. The experiments are conducted on three widely-used datasets, which verify the effectiveness of our proposed KGUCH approach.

关键词： knowledge graph fast cross-media retrieval multimedia data semantic graph unsupervised cross-media hashing methods learned hash codes feature extraction file organisation graph theory semantic networks unsupervised cross-media hashing learning unsupervised cross-media hashing approach multimedia computing information retrieval manual annotations learning (artificial intelligence) learned hashing code high-level semantic correlations unsupervised learning

Construction of fault diagnosis system for control rod drive mechanism based on knowledge graph and Bayesian inference

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Nuclear Science and Techniques 2023年第2期34卷 58-75页

作者： Xue‑Jun Jiang Wen Zhou Jie Hou Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China University of Chinese Academy of Sciences Beijing 100049China

knowledge graph technology has distinct advantages in terms of fault *** this study,the control rod drive mechanism(CRDM)of the liquid fuel thorium molten salt reactor(TMSR-LF1)was taken as the research object,and a fault diagnosis system was proposed based on knowledge *** subject–relation–object triples are defined based on CRDM unstructured data,including design specification,operation and maintenance manual,alarm list,and other forms of expert *** this study,we constructed a fault event ontology model to label the entity and relationship involved in the corpus of CRDM fault events.A three-layer robustly optimized bidirectional encoder representation from transformers(RBT3)pre-training approach combined with a text convolutional neural network(TextCNN)was introduced to facilitate the application of the constructed CRDM fault diagnosis graph database for fault *** RBT3-TextCNN model along with the Jieba tool is proposed for extracting entities and recognizing the fault query intent *** on the dataset collected from TMSR-LF1 CRDM fault diagnosis unstructured data demonstrate that this model has the potential to improve the effect of intent recognition and entity ***,a fault alarm monitoring module was developed based on WebSocket protocol to deliver detailed information about the appeared fault to the operator ***,the Bayesian inference method combined with the variable elimination algorithm was proposed to enable the development of a relatively intelligent and reliable fault diagnosis ***,a CRDM fault diagnosis Web interface integrated with graph data visualization was constructed,making the CRDM fault diagnosis process intuitive and effective.

关键词： CRDM knowledge graph Fault diagnosis Bayesian inference RBT3-TextCNN Web interface

Meta-path reasoning of knowledge graph for commonsense question answering

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Frontiers of Computer Science 2024年第1期18卷 49-59页

作者： Miao ZHANG Tingting HE Ming DONG National Engineering Research Center for E-Learning Central China Normal UniversityWuhan 430079China Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal UniversityWuhan 430079China National Language Resources Monitoring&Research Center for Network Media Central China Normal UniversityWuhan 430079China School of Computer Central China Normal UniversityWuhan 430079China

Commonsense question answering(CQA)requires understanding and reasoning over QA context and related commonsense knowledge,such as a structured knowledge graph(KG).Existing studies combine language models and graph neural networks to model ***,traditional knowledge graph are mostly concept-based,ignoring direct path evidence necessary for accurate *** this paper,we propose MRGNN(Meta-path Reasoning graph Neural Network),a novel model that comprehensively captures sequential semantic information from concepts and *** MRGNN,meta-paths are introduced as direct inference evidence and an original graph neural network is adopted to aggregate features from both concepts and paths *** conduct sufficient experiments on the CommonsenceQA and OpenBookQA datasets,showing the effectiveness of ***,we conduct further ablation experiments and explain the reasoning behavior through the case study.

关键词： question answering knowledge graph graph neural network meta-path reasoning

An Intelligent Quality Control Method for Manufacturing Processes Based on a Human–Cyber–Physical knowledge graph

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Engineering 2024年第10期41卷 242-260页

作者： Shilong Wang Jinhan Yang Bo Yang Dong Li Ling Kang State Key Laboratory of Mechanical Transmissions Chongqing UniversityChongqing 400044China College of Mathematics and Statistics Chongqing UniversityChongqing 400044China

Quality management is a constant and significant concern in *** determination of correct solutions for comprehensive problems helps avoid increased backtesting *** study proposes an intelligent quality control method for manufacturing processes based on a human–cyber–physical(HCP)knowledge graph,which is a systematic method that encompasses the following elements:data management and classification based on HCP ternary data,HCP ontology construction,knowledge extraction for constructing an HCP knowledge graph,and comprehensive application of quality control based on HCP *** proposed method implements case retrieval,automatic analysis,and assisted decision making based on an HCP knowledge graph,enabling quality monitoring,inspection,diagnosis,and maintenance strategies for quality *** practical applications,the proposed modular and hierarchical HCP ontology exhibits significant superiority in terms of shareability and reusability of the acquired ***,the HCP knowledge graph deeply integrates the provided HCP data and effectively supports comprehensive decision *** proposed method was implemented in cases involving an automotive production line and a gear manufacturing process,and the effectiveness of the method was verified by the application system ***,the proposed method can be extended to other manufacturing process quality control tasks.

关键词： Quality control Human-cyber-physical ternary data knowledge graph

Constructing an Educational knowledge graph with Concepts Linked to Wikipedia

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Journal of Computer Science & Technology 2021年第5期36卷 1200-1211页

作者： Fu-Rong Dang Jin-Tao Tang Kun-Yuan Pang Ting Wang Sha-Sha Li Xiao Li College of Computer Science and Technology National University of Defense TechnologyHunan 410073China Information Center National University of Defense TechnologyHunan 410073China

To use educational resources efficiently and dig out the nature of relations among MOOCs(massive open online courses),a knowledge graph was built for MOOCs on four major platforms:Coursera,EDX,XuetangX,and *** paper demonstrates the whole process of educational knowledge graph construction for *** this knowledge graph,the largest knowledge graph of MOOC resources at present,stores and represents five classes,11 kinds of relations and 52779 entities with their corresponding properties,amounting to more than 300000 ***,24188 concepts are extracted from text attributes of MOOCs and linked them directly with corresponding Wikipedia entries or the closest entries calculated semantically,which provides the normalized representation of knowledge and a more precise description for MOOCs far more than enriching words with explanatory ***,prerequisites discovered by direct extractions are viewed as an essential supplement to augment the connectivity in the knowledge *** knowledge graph could be considered as a collection of unified MOOC resources for learners and the abundant data for researchers on MOOC-related applications,such as prerequisites mining.

关键词： concept extraction educational resource knowledge graph massive open online course(MOOC) prerequisite

Entity set expansion in knowledge graph:a heterogeneous information network perspective

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Frontiers of Computer Science 2021年第1期15卷 127-138页

作者： Chuan SHI Jiayu DING Xiaohuan CAO Linmei HU Bin WU Xiaoli LI School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China Institute for Infocomm Research Agency for ScienceTechnology and ResearchSingapore 138632Singapore

Entity set expansion(ESE)aims to expand an entity seed set to obtain more entities which have common *** is important for many applications such as dictionary con-struction and query *** ESE methods relied heavily on the text and Web information of ***,some ESE methods employed knowledge graphs(KGs)to extend ***,they failed to effectively and fficiently utilize the rich semantics contained in a KG and ignored the text information of entities in *** this paper,we model a KG as a heterogeneous information network(HIN)containing multiple types of objects and ***-grained multi-type meta paths are proposed to capture the hidden relation among seed entities in a KG and thus to retrieve candidate *** we rank the entities according to the meta path based structural ***,to utilize the text description of entities in Wikipedia,we propose an extended model CoMeSE++which combines both structural information revealed by a KG and text information in Wikipedia for *** experiments on real-world datasets demonstrate that our model achieves better performance by combining structural and textual information of entities.

关键词： entity set expansion knowledge graph heterogeneous information network multi-type meta path

A subgraph matching algorithm based on subgraph index for knowledge graph

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Frontiers of Computer Science 2022年第3期16卷 123-140页

作者： Yunhao SUN Guanyu LI Jingjing DU Bo NING Heng CHEN Faculty of Information Science and Technology Dalian Maritime UniversityLiaoning 116026China Faculty of Software Dalian University of Foreign LanguagesLiaoning 116044China

The problem of subgraph matching is one fundamental issue in graph search,which is NP-Complete ***,subgraph matching has become a popular research topic in the field of knowledge graph analysis,which has a wide range of applications including question answering and semantic *** this paper,we study the problem of subgraph matching on knowledge ***,given a query graph q and a data graph G,the problem of subgraph matching is to conduct all possible subgraph isomorphic mappings of q on *** graph is formed as a directed labeled multi-graph having multiple edges between a pair of vertices and it has more dense semantic and structural features than general *** accelerate subgraph matching on knowledge graph,we propose a novel subgraph matching algorithm based on subgraph index for knowledge graph,called as *** subgraph matching algorithm consists of two key *** design is a subgraph index of matching-driven flow graph(FGqT),which reduces redundant calculations in *** design is a multi-label weight matrix,which evaluates a near-optimal matching tree for minimizing the intermediate *** the aid of these two key designs,all subgraph isomorphic mappings are quickly conducted only by traversing *** empirical studies on real and synthetic graphs demonstrate that our techniques outperform the state-of-the-art algorithms.

关键词： knowledge graph subgraph matching subgraph index matching tree