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A dynamic knowledge graph-based assembly sequence planning method

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2025年

作者： Huang, Shaohua Jiang, Mingjie Sun, Jingkai Rong, Haoming Pu, Jun Guo, Yu Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn 29 Yudao St Nanjing 210016 Peoples R China AVIC Changhe Aircraft Ind Grp Corp Ltd Jingdezhen Peoples R China

In assembly sequence planning (ASP) of complex products, hierarchical structure, geometric feasibility, assembly tool changes, and assembly direction changes should be fully considered. Since the traditional information models of ASP are mostly static and abstract matrices, the outdated information reduces sequence rationality and increases time costs. To address these limitations, a novel decision-making framework based on dynamic knowledge graph (DKG) is proposed to build an intuitive semantic information model, planning the assembly sequences of complex products. An automated DKG generation method with updating mechanisms is designed to ensure that the DKG remains effective throughout the construction and maintenance process. A double-layer degree ordering algorithm (DDOA) is proposed to analyze sequence constraints within the DKG for obtaining a higher-quality assembly sequence. Comparative experiments demonstrated that the DDOA exhibits optimal performance. The solved assembly sequence possesses the best value of the objective function, with the fewest changes in assembly directions and assembly tools, as well as the shortest runtime.

关键词： Assembly sequence planning dynamic knowledge graph knowledge extraction double-layer degree ordering complex product

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FedMDKGE: Multi-granularity dynamic knowledge graph Embedding in Federated Learning

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-18页

作者： Huang, Wei Chen, Junling Wang, Dexian Zhang, Pengfei Liu, Jia Li, Tianrui Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China Fuzhou Univ Fujian Key Lab Network Comp & Intelligent Informat Fuzhou 350108 Peoples R China Fuzhou Univ Engn Res Ctr Big Data Intelligence Minist Educ Fuzhou 350108 Peoples R China Chengdu Univ Tradit Chinese Med Sch Intelligent Med Chengdu 611137 Peoples R China Xihua Univ Sch Comp & Software Engn Chengdu 611756 Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China

As knowledge is time-sensitive, some researchers have started to focus on dynamic knowledge graphs to provide time-dimensioned knowledge content thus reflecting richer information. But they have not yet combined temporal information at different granularities. Also, in the case of multiple knowledge graphs distributed across different clients, it is of interest to ensure that the knowledge graph embedding representations are learned without exposing data and collaboratively. Therefore, in this paper, we propose a framework for multi-granularity dynamic knowledge graph embedding in federated learning (FedMDKGE), which allows multiple parties to interact securely with temporal information at different granularities. In the client, we present a multi-granularity dynamic knowledge graph embedding model that improves the capability of dynamic knowledge graph embedding representation by focusing on multi-granularity temporal facts from the perspective of knowledge utilization. On the server, we design a multi-granularity aggregation rule to accommodate multi-party information aggregation at different granularities. Finally, we conduct extensive experiments to demonstrate the superior performance of our model. The results on these real datasets show that FedMDKGE considering multi-granularity temporal information performs better than all comparative baselines and interconnect information for multi-party dynamic knowledge graph embedding without exposing data.

关键词： Federated learning dynamic knowledge graph Multi-granularity Temporal information

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A derived information framework for a dynamic knowledge graph and its application to smart cities

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 152卷 112-126页

作者： Bai, Jiaru Lee, Kok Foong Hofmeister, Markus Mosbach, Sebastian Akroyd, Jethro Kraft, Markus Univ Cambridge Dept Chem Engn & Biotechnol Philippa Fawcett Dr Cambridge CB3 0AS England CMCL Innovat Sheraton House Cambridge CB3 0AX England Cambridge Ctr Adv Res & Educ Singapore CARES 1 Create WayCREATE Tower05-05 Singapore 138602 Singapore Nanyang Technol Univ Sch Chem & Biomed Engn 62 Nanyang Dr Singapore 637459 Singapore Alan Turing Inst London NW1 2DB England

In this work, we develop a derived information framework to semantically annotate how a piece of information can be obtained from others in a dynamic knowledge graph. We encode this using the notion of a "derivation"and capture its metadata with a lightweight ontology. We provide an agent template designed to monitor derivations and to standardise agents performing this and related operations. We implement both synchronous and asynchronous communication modes for agents interacting with the knowledge graph. When occurring in conjunction, directed acyclic graphs of derivations can arise, with changing data propagating through the knowledge graph by means of agents' actions. While the framework itself is domain-agnostic, we apply it in the context of smart cities as part of the World Avatar project and demonstrate that it is capable of handling sequential events across different timescales. Starting from source information, the framework automatically populates derived data and ensures they remain up to date upon access for a potential flood impact assessment use case.

关键词： dynamic knowledge graph Derived information Data provenance Directed acyclic graph Smart cities

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Universal Digital Twin - A dynamic knowledge graph

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DATA-CENTRIC ENGINEERING 2021年第4期2卷 e14-e14页

作者： Akroyd, Jethro Mosbach, Sebastian Bhave, Amit Kraft, Markus Univ Cambridge Dept Chem Engn & Biotechnol Cambridge CB3 0AS England Cambridge Ctr Adv Res & Educ Singapore CARES Singapore 138602 Singapore CMCL Innovat Cambridge CB3 0AX England Nanyang Technol Univ Sch Chem & Biomed Engn Singapore 637459 Singapore

This paper introduces a dynamic knowledge-graph approach for digital twins and illustrates how this approach is by design naturally suited to realizing the vision of a Universal Digital Twin. The dynamic knowledge graph is implemented using technologies from the Semantic Web. It is composed of concepts and instances that are defined using ontologies, and of computational agents that operate on both the concepts and instances to update the dynamic knowledge graph. By construction, it is distributed, supports cross-domain interoperability, and ensures that data are connected, portable, discoverable, and queryable via a uniform interface. The knowledge graph includes the notions of a "base world" that describes the real world and that is maintained by agents that incorporate real-time data, and of "parallel worlds" that support the intelligent exploration of alternative designs without affecting the base world. Use cases are presented that demonstrate the ability of the dynamic knowledge graph to host geospatial and chemical data, control chemistry experiments, perform cross-domain simulations, and perform scenario analysis. The questions of how to make intelligent suggestions for alternative scenarios and how to ensure alignment between the scenarios considered by the knowledge graph and the goals of society are considered. Work to extend the dynamic knowledge graph to develop a digital twin of the UK to support the decarbonization of the energy system is discussed. Important directions for future research are highlighted.

关键词： Agents data digital twin dynamic knowledge graph interoperability

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A kind of intelligent dynamic industrial event knowledge graph and its application in process stability evaluation

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JOURNAL OF INTELLIGENT MANUFACTURING 2025年第3期36卷 1801-1818页

作者： Li, Qingzong Jiang, Pingyu Wang, Jianwei Yang, Maolin Yang, Yuqian Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China

Event knowledge graph (EKG) is a method of representing real-world entities, events, their attributes, and the relations between them in a graph structure. The EKG has been applied in manufacturing industry to empower intelligence manufacturing. But there are limitations of generic EKG in addressing manufacturing issues. Because in the manufacturing industry, there is not only text-type knowledge but also signals, images, videos, etc. In particular, some of the relations between entities/events of the knowledge are in the form of formulas, functions, and even trained artificial intelligence models. This kind of knowledge is called Functional knowledge in this paper. The generic EKG is suitable for representing text-type knowledge but not Functional knowledge. Thus, the research aims to present a new kind of EKG that has the ability to represent various types of knowledge, especially Functional knowledge. In this regard, an intelligent dynamic Industrial Event knowledge graph (IEKG) is proposed. Firstly, Functional Relation, Functional Triple, and a knowledge representation model based on property graphs for the schema layer of IEKG are proposed for representing Functional knowledge. Secondly, a dynamic construction method of the instance layer of IEKG based on the event triggering mechanism is proposed, which enables the IEKG to be constructed dynamically with the production operation. Third, the constructed IEKG is applied in production monitoring using a novel graph similarity-based process stability evaluation method. Finally, a web application encapsulating our theory was developed and applied on a kneading machine in a prebaked carbon anode factory. The result shows that our proposed method has the ability to represent Functional knowledge. Compared to the existing EKG, it has a better and broader ability of knowledge representation. The application of process stability evaluation demonstrates the potential of IEKG in addressing manufacturing issues.

关键词： Event knowledge graph knowledge graph representation dynamic knowledge graph Process stability evaluation

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Investigating the Challenges and Prospects of Construction Models for dynamic knowledge graphs

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IEEE ACCESS 2024年 12卷 40973-40988页

作者： Farghaly, Maha Mounir, Mahmoud Aref, Mostafa Moussa, Sherin M. Future Univ Egypt Fac Comp & Informat Technol Informat Syst Dept Cairo 11835 Egypt Ain Shams Univ Fac Comp & Informat Sci Cairo 11566 Egypt Univ Francaise Egypte Lab Interdisciplinaire Univ Francaise Egypte UFEID Cairo 11837 Egypt

Recently, dynamic knowledge graphs (DKGs) have been considered the foundation stone for several powerful knowledge-aware applications. DKG has a great advancement over static knowledge graph with the ability to capture the dynamicity of knowledge. The correctness and completeness of DKGs strongly affect the accuracy of the dependent application, in which many factors may have an impact, including data sources, graph construction model, and evaluation methods. Despite the increasing attention to DKGs, the literature of DKG construction is not comprehensively investigated, and the limitations are not fully revealed. In this paper, a comparative study is conducted for the emerging construction models of DKG. An extensive analysis is provided for each of the three main phases of DKG construction: entity extraction, relationship extraction and graph completion. For the different phases, we investigated the employed techniques, the used data sources, as well as the associated challenges, limitations, and evaluation metrics of each model. The learning approach is introduced as a novel categorization perspective for the employed techniques in the DKG construction. Finally, the encountered challenges and limitations are inspected to deduce the possible future directions that can be adopted for effective and advanced DKGs construction. It was found that 100% of the investigated models lack the key aspects of dynamicity in DKGs, 75% suffer from insufficient training features, 58% have a clear exposure to bias, 33% are vulnerable to changes, 25% have a performance and efficiency concerns, while lack of evaluation and comparison represented 25% and 17% of the models respectively.

关键词： dynamic knowledge graph learning approaches knowledge graph completion knowledge graph construction

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DPN:dynamics Priori Networks for Radiology Report Generation

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Tsinghua Science and Technology 2025年第2期30卷 600-609页

作者： Bokai Yang Hongyang Lei Huazhen Huang Xinxin Han Yunpeng Cai Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China School of Artificial Intelligence Shenzhen Polytechnic UniversityShenzhen 518055China

Radiology report generation is of significant *** standard image captioning tasks,radiology report generation faces more pronounced visual and textual biases due to constrained data availability,making it increasingly reliant on prior knowledge in this *** this paper,we introduce a radiology report generation network termed dynamics Priori Networks(DPN),which leverages a dynamic knowledge graph and prior ***,we establish an adaptable graph network and harness both medical domain knowledge and expert insights to enhance the model’s ***,we introduce an image-text contrastive module and an image-text matching module to enhance the quality of the generated *** method is evaluated on two widely available datasets:X-ray collection from Indiana University(IU X-ray)and Medical Information Mart for Intensive Care,Chest X-Ray(MIMIC-CXR),where it demonstrates superior performance,particularly excelling in critical metrics.

关键词： radiology report generation dynamic knowledge graph prior knowledge contrastive learning

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Fully Automated Kinetic Models Extend our Understanding of Complex Reaction Mechanisms

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CHEMIE INGENIEUR TECHNIK 2023年第5期95卷 740-748页

作者： Rihm, Simon D. Bai, Jiaru Pascazio, Laura Kraft, Markus Univ Cambridge Dept Chem Engn & Biotechnol Philippa Fawcett Dr Cambridge CB3 0AS England Cambridge Ctr Adv Res & Educ Singapore CARES 1 Create Way05-05 CREATE Tower Singapore 138602 Singapore Natl Univ Singapore Dept Chem & Biomol Engn 4 Engn Dr 4 Singapore 117585 Singapore Nanyang Technol Univ Sch Chem & Biomed Engn 62 Nanyang Dr Singapore 637459 Singapore Alan Turing Inst London England

Automating the creation and calibration of microkinetic models enables researchers to perform detailed analysis of reaction mechanisms as well as multi-scale modelling of reactors based on ab initio calculations. The example of electrochemical CO2 reduction shows that this requires a structured and standardised representation of concepts across multiple scales. Massive progress has been made in this area through dynamic knowledge graphs, whereby the first partial successes can be reported in the area of mechanism generation, quantum mechanical calculations as well as calibration with experimental data.

关键词： dynamic knowledge graph Electrochemical CO2 reduction Microkinetic modelling Model automation Multi-scale modelling

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A security detection approach based on autonomy-oriented user sensor in social recommendation network

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2022年第3期18卷

作者： Wan, Shanshan Liu, Ying Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing 100044 Peoples R China

User social network-based recommender system has achieved significant performance in current recommendation fields. However, the characteristic of openness brings great hidden dangers to the security of recommender systems. Shilling attackers can change the recommendations by foraging user relationships. Most shilling attack detection approaches depend on the explicit user historical data to locate shilling attackers. Some important features such as information propagation and social feedback of users in social networks have not been noticed. We propose a security detection method based on autonomy-oriented user sensor (AOUSD) to identify shilling attackers. Specifically, (1) the user is simulated as a social sensor with autonomous capabilities, (2) the user interaction model is built based on information propagation, information feedback and information disappearance mechanisms of social sensors, and a user dynamic knowledge graph is formed by considering the variable time function, (3) hierarchical clustering method is used to generate preliminary suspicious candidate groups and graph community detection clustering method is applied on the dynamic knowledge graph to detect the attackers. Then, AOUSD is first simulated on NetLogo and it is compared with other algorithms based on the Amazon data. The results prove the advantages of AOUSD in the efficiency and accuracy on shilling attack detection.

关键词： Autonomous sensor social recommendation network dynamic knowledge graph shilling attack graph community detection

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Intelligent Career Advisory System: Enhancing Career Matching with BERT and graph Neural Networks 5

Intelligent Career Advisory System: Enhancing Career Matchin...

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5th International Conference on Consumer Electronics and Computer Engineering, ICCECE 2025

作者： Hua, Min Abisado, Mideth College of Computing and Information Technologies National University Manila 1008 Philippines School of Information Engineering Xinyang Agriculture and Forestry University Xinyang China

ISBN: (纸本)9798331533113

Traditional career recommendation systems often fail to capture dynamic skill demands, subtle query semantics, and potential ethical and bias issues. In this paper, we propose a hybrid model that integrates BERT for semantic understanding and graph Neural Networks (GNN) for relational reasoning to construct a dynamically updated career knowledge graph. The graph is built by fusing multi-source data from LinkedIn, O*NET, and synthetic data generated via GPT-4, and a cross-modal attention mechanism is used to fuse text and graph features. Experiments on the CareerQA dataset, consisting of 50,000 annotated queries, demonstrates that our model improves the Fl-score by 18.7% and achieves an NDCG@5 of 0.921. A user study involving 1,200 participants further validates the system's practical effectiveness with an 89.2 % positive feedback rate. © 2025 IEEE.

关键词： BERT Career Recommendation Cross-Modal Attention dynamic knowledge graph graph Neural Networks

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