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KNowNEt:Guided Health Information Seeking from LLMs via knowledge graph Integration

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER graphICS 2025年第1期31卷 547-557页

作者： Yan, Youfu Hou, Yu Xiao, Yongkang Zhang, Rui Wang, Qianwen Univ Minnesota Twin Cities Dept Comp Sci & Engn Minneapolis MN 55455 USA Univ Minnesota Twin Cities Med Sch Minneapolis MN USA

The increasing reliance on Large Language Models (LLMs) for health information seeking can pose severe risks due to the potential for misinformation and the complexity of these topics. This paper introduces KnowNet a visualization system that integrates LLMs with knowledge graphs (KG) to provide enhanced accuracy and structured exploration. Specifically, for enhanced accuracy, KnowNet extracts triples (e.g., entities and their relations) from LLM outputs and maps them into the validated information and supported evidence in external KGs. For structured exploration, KnowNet provides next-step recommendations based on the neighborhood of the currently explored entities in KGs, aiming to guide a comprehensive understanding without overlooking critical aspects. To enable reasoning with both the structured data in KGs and the unstructured outputs from LLMs, KnowNet conceptualizes the understanding of a subject as the gradual construction of graph visualization. A progressive graph visualization is introduced to monitor past inquiries, and bridge the current query with the exploration history and next-step recommendations. We demonstrate the effectiveness of our system via use cases and expert interviews.

关键词： knowledge graphs Visualization Alzheimer's disease Accuracy Data visualization Large language models Electronic mail Human-AI interactions knowledge graph conversational agent large language model progressive visualization

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A knowledge graph-based intelligent planning method for remanufacturing processes of used parts

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JOURNAL OF ENGINEERING DESIGN 2025年

作者： Zhu, Shuo Gao, Liuyang Jiang, Zhigang Yan, Wei Zhang, Hua Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmission & Mfg Engn Minist Educ Wuhan Peoples R China Wuhan Univ Sci & Technol Sch Automobile & Traff Engn Wuhan Peoples R China Wuhan Univ Sci & Technol Acad Green Mfg Engn Wuhan Peoples R China

Intelligent remanufacturing process planning is crucial for the efficient and high-quality remanufacturing of used parts with complex failure characteristics. However, due to the varied failure characteristics of used parts, the diversity of remanufacturing processes, and complex non-linear relationships among remanufacturing process elements, relying solely on mathematical programming or manual empirical is difficult to effectively model and optimise the remanufacturing process planning. To this end, a knowledge graph-based intelligent planning method for remanufacturing processes is proposed to enhance efficiency and quality by combining mathematical programming and knowledge reuse. Firstly, with failure characteristics as decision nodes, a full-element remanufacturing process ontology model is constructed, linking used parts, failure characteristics, and corresponding process plans. The BERT-BiLSTM-CRF model extracts remanufacturing process entities, and a remanufacturing process knowledge graph (RPKG) is constructed. Secondly, an intelligent decision-making model based on graph multi-node path retrieval is proposed. Aim to minimise carbon emissions, time, and cost, combining feature similarity calculations and nearest neighbour search (NNS) to efficiently retrieve the optimal process plan for each failure characteristic. Then, the optimal process plans are merged based on process constraints to create the complete plan. Finally, a concrete case is given to verify the effectiveness and advantages of this method.

关键词： Remanufacturing process full process element knowledge graph intelligent planning

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Sublinear smart semantic search based on knowledge graph over encrypted database

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COMPUTERS & SECURITY 2025年 151卷

作者： Li, Jiaxin Peng, Haipeng Li, Lixiang Beijing Univ Posts & Telecommun Informat Secur Ctr State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Natl Engn Lab Disaster Backup & Recovery Beijing 100876 Peoples R China

In light of the growing significance of data privacy in cloud computing, searchable encryption has emerged as a prominent area of research in recent years. Given that the majority of achievements are only concentrated on keyword-based search, there is a clear need to explore secure semantic extension to enhance the utilization of information. Nevertheless, majority of secure semantic schemes mainly focus on the direct combination of traditional searchable encryption and semantic extension techniques, which results in the high computation and communication overheads for multi-keyword queries. Besides, to enhance the flexibility and extensibility of secure semantic search, some researchers have already proposed encryption methods for structured data. However, the effectiveness and practicality of these methods above are often limited by their rigid structure description and redundant index building. To handle these problems, we present a secure sublinear semantic search scheme based on the knowledge graph, which can achieve sublinear knowledge graph (KG) search and smart sentence queries concurrently. The knowledge graph encryption index is constructed with the same structure as the inverted index, thereby enabling sublinear efficiency. Furthermore, the system also offers functions for both node and edge semantic extension in the knowledge graph. We demonstrate that our proposed scheme is adaptively secure and present a series of experiments to analyze its performance. The experimental results show that our scheme exhibits significant advantages in all computation, communication and expression situations.

关键词： Semantic search Searchable encryption Sublinear knowledge graph Inverted index

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GN-GCN: Grid neighborhood-based graph convolutional network for spatio-temporal knowledge graph reasoning

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2025年 220卷 728-739页

作者： Han, Bing Qu, Tengteng Jiang, Jie Peking Univ Acad Adv Interdisciplinary Studies Beijing 100871 Peoples R China Peking Univ Coll Engn Beijing 100871 Peoples R China Beijing Univ Civil Engn & Architecture Sch Geomatics & Urban Spatial Informat Beijing 100044 Peoples R China

Owing to the difficulty of utilizing hidden spatio-temporal information, spatio-temporal knowledge graph (KG) reasoning tasks in real geographic environments have issues of low accuracy and poor interpretability. This paper proposes a grid neighborhood-based graph convolutional network (GN-GCN) for spatio-temporal KG reasoning. Based on the discretized process of encoding spatio-temporal data through the GeoSOT global grid model, the GN-GCN consists of three parts: a static graph neural network, a neighborhood grid calculation, and a time evolution unit, which can learn semantic knowledge, spatial knowledge, and temporal knowledge, respectively. The GN-GCN can also improve the training accuracy and efficiency of the model through the multiscale aggregation characteristic of GeoSOT and can visualize different probabilities in a spatio-temporal intentional probabilistic grid map. Compared with other existing models (RE-GCN, CyGNet, RE-NET, etc.), the mean reciprocal rank (MRR) of GN-GCN reaches 48.33 and 54.06 in spatio-temporal entity and relation prediction tasks, increased by 6.32/18.16% and 6.64/15.67% respectively, which achieves state-of-the-art (SOTA) results in spatio-temporal reasoning. The source code of the project is available at https://***/10.18170/DVN/ UIS4VC.

关键词： knowledge graph Spatio-temporal reasoning graph convolutional network Neighborhood grid calculation

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MetaFactory: A cloud-based framework to configure and generate dynamic data structures from the STEP-NC knowledge graph

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JOURNAL OF MANUFACTURING SYSTEMS 2025年 80卷 89-107页

作者： Xiao, Wenlei Qiu, Tianze Guo, Jiurong Zhao, Gang Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Beihang Univ Jiangxi Res Inst Nanchang 330200 Peoples R China Beihang Univ Jingdezhen Res Inst Jingdezhen 333000 Peoples R China AV Chengdu Aircraft Ind Grp Co Ltd Chengdu 610031 Peoples R China

In our previous studies, twin-oriented manufacturing has been identified as a crucial solution to address the manufacturing crisis. Within this context, the notion of "digital twin as a service" necessitates that various twin services share and communicate with each other in a standardized manner. STEP-NC offers a potentially unified model to facilitate data exchange, providing object-oriented and standardized data models fora comprehensive representation of manufacturing resources in the digital realm. However, the complexity of STEP-NC renders it too cumbersome for implementation in diverse cloud-based services or PC-based software. This complexity is a fundamental reason why STEP-NC has struggled to find application in commercial CNC systems despite years of research. To overcome this technical challenge, this paper introduces a novel concept termed "dynamic STEP-NC data structure", inspired by the dynamic language philosophy of dynamic programming language (such as Python). This approach allows different services and software packages to maintain their own data definitions while still aligning with the original STEP-NC definition. We have developed a framework called MetaFactory that supports the configuration of streamlined data structures and generates the corresponding program code required by various service developers. On this basis, we implemented automatic modeling for a STEP-NC object-oriented database. Using the data trimming and dimensionality reduction methods provided by MetaFactory, several prototype systems for different application scenarios have been developed.

关键词： Twin-oriented manufacturing STEP-NC Dynamic data structure Object-oriented database knowledge graph

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Research on a BIM Model Quality Compliance Checking Method Based on a knowledge graph

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2025年第1期39卷

作者： Zhu, Simiao Zhou, Jianliang Cheng, Lei Fu, Xuehai Wang, Yan Dai, Kexin China Univ Min & Technol Sch Mech & Civil Engn Xuzhou 221116 Jiangsu Peoples R China

With the development of information technology, building information modeling (BIM) is becoming widely used in the construction industry. It is very important to ensure the quality and compliance of BIM models. To solve the problem of time-consuming and laborious traditional checking methods and the limitation that existing software hard coding cannot adapt to rule changes, this paper proposes a BIM model quality compliance checking method based on knowledge graph that can quickly check BIM models through automated checking and ensure the modeling quality of BIM models. Firstly, knowledge extraction for BIM modeling standards and delivery standards is carried out based on knowledge graph technology to structurally represent text. Then, Revit secondary development technology and the C# programming language are adopted to develop a BIM model quality compliance checking system based on the knowledge graph analysis results and the structured query language (SQL) built-in rule knowledge base. Based on the knowledge base, the BIM model is automatically checked for quality compliance, and the naming, material, parameters, and rationality of each professional component are checked. Next, it is checked whether it conforms to the standard. Finally, the model modification plan is provided by checking the problem components and noncompliance details. The case application results show that the BIM model quality compliance checking system developed based on knowledge graph technology can identify defects or errors that do not meet the standards in the model, verify the feasibility and practicability of the system, and provide a new method for BIM designers to modify and improve the model.

关键词： knowledge graph Building information model Automated checking Rule base C# Revit secondary development

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A knowledge graph approach to drug repurposing for Alzheimer's, Parkinson's and Glioma using drug-disease-gene associations

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2025年 115卷 108302页

作者： Selote, Ruchira Makhijani, Richa Indian Inst Informat Technol Dept Comp Sci & Engn Nagpur India

Drug Repurposing gives us facility to find the new uses of previously developed drugs rather than developing new drugs from start. Particularly during pandemic, drug repurposing caught much attention to provide new applications of the previously approved drugs. In our research, we provide a novel method for drug repurposing based on feature learning process from drug-disease-gene network. In our research, we aimed at finding drug candidates which can be repurposed under neurodegenerative diseases and glioma. We collected association data between drugs, diseases and genes from public resources and primarily examined the data related to Alzheimer's, Parkinson's and Glioma diseases. We created a knowledge graph using neo4j by integrating all these datasets and applied scalable feature learning algorithm known as node2vec to create node embeddings. These embeddings were later used to predict the unknown associations between disease and their candidate drugs by finding cosine similarity between disease and drug nodes embedding. We obtained a definitive set of candidate drugs for repurposing. These results were validated from the literature and CodReS online tool to rank the candidate drugs. Additionally, we verified the status of candidate drugs from pharmaceutical knowledge databases to confirm their significance.

关键词： knowledge graph Drug repurposing Node embeddings Biased random walk Feature learning Cosine similarity

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Historical facts learning from Long-Short Terms with Language Model for Temporal knowledge graph Reasoning

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INFORMATION PROCESSING & MANAGEMENT 2025年第3期62卷

作者： Xu, Wenjie Liu, Ben Peng, Miao Jiang, Zihao Jia, Xu Liu, Kai Liu, Lei Peng, Min Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China Hong Kong Univ Sci & Technol Guangzhou Data Sci & Analyt Thrust Guangzhou 511458 Guangdong Peoples R China Hebei Normal Univ Coll Comp & Cyber Secur Shijiazhuang 050024 Hebei Peoples R China

Temporal knowledge graph Reasoning (TKGR) aims to reason the missing parts in TKGs based on historical facts from different time periods. Traditional GCN-based TKGR models depend on structured relations between entities. To utilize the rich linguistic information in TKGs, some models have focused on applying pre-trained language models (PLMs) to TKGR. However, previous PLM-based models still face some issues: (1) they did not mine the associations in relations;(2) they did not differentiate the impact of historical facts from different time periods. (3) they introduced external knowledge to enhance the performance without fully utilizing the inherent reasoning capabilities of PLMs. To deal with these issues, we propose HFL: H istorical F acts L earning from Long-Short Terms with Language Model for TKGR. Firstly, we construct time tokens for different types of time intervals to use timestamps and input the historical facts relevant to the query into the PLMs to learn the associations in relations. Secondly, we take a multi-perspective sampling strategy to learn from different time periods and use the original text information in TKGs or even no text information to learn reasoning abilities without any external knowledge. Finally, we perform HFL on four TKGR benchmarks, and the experiment results demonstrate that HFL has great competitiveness compared to both graph-based and PLMbased models. Additionally, we design a variant that applies HFL to LLMs and evaluate the performance of different LLMs.

关键词： knowledge graph Temporal knowledge graph reasoning Pre-trained language model Large-scale language model

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Granular concept-enhanced relational graph convolution networks for link prediction in knowledge graph

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INFORMATION SCIENCES 2025年 694卷

作者： Dai, Yuhao Yan, Mengyu Li, Jinhai Kunming Univ Sci & Technol Fac Sci Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Data Sci Res Ctr Kunming 650500 Yunnan Peoples R China

Link prediction is a task of completing absent triplets by leveraging existing triplets in KG and the ultimate goal is to mitigate the incompleteness and sparsity of KG in terms of content. As well known, Relational graph Convolutional Networks (R-GCN) model is a promising method for link prediction due to its capability of the graph structure. However, R-GCN mainly relies on information from adjacent nodes, leading to shortcomings in the model for capturing a wider range of relational information. Meanwhile, Formal Concept Analysis (FCA) is increasingly being applied in various fields as an effective data analysis tool. Inspired by this, we integrate FCA into R-GCN to address the shortcomings of R-GCN mentioned above. Specifically, a formal context is first created according to the entities and relations, and granular concepts can be obtained by the formal context. Then the weights of the relational parameters in R-GCN are redistributed based on similarity of granular concepts. Further we develop granular concept-enhanced relational graph convolution networks (GCR-GCN) model, where granular concept can simulate the process of data conceptualization in human brain very well, so it has stronger interpretability compared to the black-box characteristic of R-GCN. Finally, experimental results demonstrate that the GCRGCN model improves the effectiveness of link prediction by effectively assigning different weights to entities with the same relation. In addition, the granular concept effectively improves the computational efficiency of the model and reduces the storage pressure of the model during computation.

关键词： knowledge graph Link prediction Formal concept analysis graph convolution networks Granular computing

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ROS package search for robot software development: a knowledge graph-based approach

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FRONTIERS OF COMPUTER SCIENCE 2025年第6期19卷 1-16页

作者： Wang, Shuo Mao, Xinjun Yang, Shuo Wu, Menghan Zhang, Zhang Natl Univ Def Technol Coll Comp Changsha 410000 Peoples R China Key Lab Software Engn Complex Syst Changsha 410000 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410000 Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310058 Peoples R China

In recent years, ROS (Robot Operating System) packages have become increasingly popular as a type of software artifact that can be effectively reused in robotic software development. Indeed, finding suitable ROS packages that closely match the software's functional requirements from the vast number of available packages is a nontrivial task using current search methods. The traditional search methods for ROS packages often involve inputting keywords related to robotic tasks into general-purpose search engines (e.g., Google) or code hosting platforms (e.g., GitHub) to obtain approximate results of all potentially suitable ROS packages. However, the accuracy of these search methods remains relatively low because the task-related keywords may not precisely match the functionalities offered by the ROS packages. To improve the search accuracy of ROS packages, this paper presents a novel semantic-based search approach that relies on the semantic-level ROS Package knowledge graph (RPKG) to automatically retrieve the most suitable ROS packages. Firstly, to construct the RPKG, we employ multi-dimensional feature extraction techniques to extract semantic concepts, including code file name, category, hardware device, characteristics, and function, from the dataset of ROS package text descriptions. The semantic features extracted from this process result in a substantial number of entities (32,294) and relationships (54,698). Subsequently, we create a robot domain-specific small corpus and further fine-tune a pre-trained language model, BERT-ROS, to generate embeddings that effectively represent the semantics of the extracted features. These embeddings play a crucial role in facilitating semantic-level understanding and comparisons during the ROS package search process within the RPKG. Secondly, we introduce a novel semantic matching-based search algorithm that incorporates the weighted similarities of multiple features from user search queries, which searches out more accurate

关键词： robot software ROS package search knowledge graph

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