Knowledge Graph Model Research Articles

Электронный корпус татарского языка на базе модели лингвистических графов знаний

The article presents a new version of the electronic corpus of the Tatar language, updated based on a linguistic knowledge graph model for Turkic languages. This new version of the corpus allows for information description across multiple linguistic levels: morphonological, syntactic, and semantic, through the use of knowledge graphs to represent linguistic data. This approach enhances corpus functionality, enabling searches that incorporate syntactic and semantic information. A distinctive feature of the electronic corpus implementation is that the model employed aligns closely with the structural and functional characteristics of Turkic languages and serves as a foundation for developing various software products for semantic text processing in Turkic languages. In particular, these products include the linguistic portal "Turkic Morphme" and the new version of the Tatar language electronic corpus, "Tugan Tel.".

Intelligent question answering for water conservancy project inspection driven by knowledge graph and large language model collaboration

Intelligent question answering for water conservancy project inspection driven by knowledge graph and large language model collaboration

Generative AI-based knowledge graphs for the illustration and development of mHealth self-management content.

Digital therapeutics (DTx) in the form of mobile health (mHealth) self-management programs have demonstrated effectiveness in reducing disease activity across various diseases, including fibromyalgia and arthritis. However, the content of online self-management programs varies widely, making them difficult to compare. This study aims to employ generative artificial intelligence (AI)-based knowledge graphs and network analysis to categorize and structure mHealth content at the example of a fibromyalgia self-management program. A multimodal mHealth online self-management program targeting fibromyalgia and post-viral fibromyalgia-like syndromes was developed. In addition to general content, the program was customized to address specific features and digital personas identified through hierarchical agglomerative clustering applied to a cohort of 202 patients with chronic musculoskeletal pain syndromes undergoing multimodal assessment. Text files consisting of 22,150 words divided into 24 modules were used as the input data. Two generative AI web applications, ChatGPT-4 (OpenAI) and Infranodus (Nodus Labs), were used to create knowledge graphs and perform text network analysis, including 3D visualization. A sentiment analysis of 129 patient feedback entries was performed. The ChatGPT-generated knowledge graph model provided a simple visual overview with five primary edges: "Mental health challenges", "Stress and its impact", "Immune system function", "Long COVID and fibromyalgia" and "Pain management and therapeutic approaches". The 3D visualization provided a more complex knowledge graph, with the term "pain" appearing as the central edge, closely connecting with "sleep", "body", and "stress". Topical cluster analysis identified categories such as "chronic pain management", "sleep hygiene", "immune system function", "cognitive therapy", "healthy eating", "emotional development", "fibromyalgia causes", and "deep relaxation". Gap analysis highlighted missing links, such as between "negative behavior" and "systemic inflammation". Retro-engineering of the self-management program showed significant conceptual similarities between the knowledge graph and the original text analysis. Sentiment analysis of free text patient comments revealed that most relevant topics were addressed by the online program, with the exception of social contacts. Generative AI tools for text network analysis can effectively structure and illustrate DTx content. Knowledge graphs are valuable for increasing the transparency of self-management programs, developing new conceptual frameworks, and incorporating feedback loops.

Billion-scale pre-trained knowledge graph model for conversational chatbot

Conversational Chatbot is a critical technology for managing customer service on E-commerce platforms. However, most Chatbots struggle with data scarcity and sparsity. To tackle these issues, a novel two-step (pretrain and fine-tune) click-through-rate prediction model called knowledge-enhanced deep cross network (K-DCN) is proposed. First, a billion-scale conversation knowledge graph is constructed, inspired by real-world application scenarios. Then, the conversation knowledge graph is pretrained to capture the semantic and structure information respectively. Our K-DCN leverages novel user-state and dialogue-interaction representations from the pre-trained conversation knowledge graph, integrating them with the conventional dense and sparse feature representations within the Deep Cross Network framework. This integration is designed to perform item ranking in recommendation. From the experiments, our proposed K-DCN significantly outperforms the baseline models, and the model has been deployed in real world application.

A precise and refined identification method for carbonate bioreefs and prograding bodies guided by a knowledge graph

Carbonate bioreef formations serve as crucial hydrocarbon reservoirs, and their accurate identification bears significant implications for oil and gas exploration. Moreover, the precise and refined delineation of prograding body structures aids in the comprehensive analysis of stratigraphic geologic configurations. We develop the knowledge graph and geologic strata interpolation constraints (KGGSICs) model for the intricate identification of carbonate bioreefs and prograding body structures. Furthermore, we assess our KGGSIC-Unet architecture on the Dengying Formation Sections 3-4 carbonate bioreefs and prograding bodies in the Moxi area of the Sichuan Basin. Experimental results indicate that the KGGSIC enhances the predictive performance of the U-Net and realizes the precise and refined segmentation of carbonate bioreefs and prograding body structures. In addition, through a meticulous geologic study of the area, we synthesize the 2D profile identification results to achieve the precise and refined identification of carbonate bioreefs and prograding bodies.

Joint Knowledge Graph and Large Language Model for Fault Diagnosis and Its Application in Aviation Assembly

Joint Knowledge Graph and Large Language Model for Fault Diagnosis and Its Application in Aviation Assembly

The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data.

A knowledge graph can effectively showcase the essential characteristics of data and is increasingly emerging as a significant means of integrating information in the field of artificial intelligence. Coronary artery plaque represents a significant etiology of cardiovascular events, posing a diagnostic challenge for clinicians who are confronted with a multitude of nonspecific symptoms. To visualize the hierarchical relationship network graph of the molecular mechanisms underlying plaque properties and symptom phenotypes, patient symptomatology was extracted from electronic health record data from real-world clinical settings. Phenotypic networks were constructed utilizing clinical data and protein‒protein interaction networks. Machine learning techniques, including convolutional neural networks, Dijkstra's algorithm, and gene ontology semantic similarity, were employed to quantify clinical and biological features within the network. The resulting features were then utilized to train a K-nearest neighbor model, yielding 23 symptoms, 41 association rules, and 61 hub genes across the three types of plaques studied, achieving an area under the curve of 92.5%. Weighted correlation network analysis and pathway enrichment were subsequently utilized to identify lipid status-related genes and inflammation-associated pathways that could help explain the differences in plaque properties. To confirm the validity of the network graph model, we conducted coexpression analysis of the hub genes to evaluate their potential diagnostic value. Additionally, we investigated immune cell infiltration, examined the correlations between hub genes and immune cells, and validated the reliability of the identified biological pathways. By integrating clinical data and molecular network information, this biomedical knowledge graph model effectively elucidated the potential molecular mechanisms that collude symptoms, diseases, and molecules.

Analytic prediction for acceptable pricing in industry interaction with complex network evolution based on knowledge graph fusion

This paper proposes a trend prediction analysis method based on the evolution of knowledge graph fusion for the analysis of price fluctuation trends with limited information and lacking historical data. Based on an actual business case, the article first establishes a knowledge graph and corresponding data model based on the price analysis and application scenarios in business decision-making and based on the collected data. Afterward, the evolution mechanism model and the corresponding theoretical basis are described. After that, the feasibility of the proposed method is proved theoretically and via simulation verification. This method does not require tags and historical data as necessary support, thereby reducing the data requirements of machine learning predictions, and can be used as a supplementary solution to complete trend analysis with less requirement on data curation. The method's stability and feasibility have been substantiated through experiments, showcasing distinct effects on the trend emanating from different interrelated knowledge graphs. Recognizing the potentially conflicting constraints between diverse correlations, we undertake a comprehensive evaluation of each map's influence. This evaluation is achieved through knowledge graph fusion processing, enabling a predictive assessment under the collective impact of various factors.

Analysis of Influencing Factors of Construction Cost Based on Building Information Model

Construction cost is a complex and multi-level system, influenced by many factors. By combining the risk influence factor analysis method with actual cost management, a knowledge graph model of the influencing parameters of construction project cost based on the Building Information Model (BIM) is established. The model includes two types of internal influence factors and external influence factors, focusing on analyzing and establishing the logical relationship between different influencing factors. The aim is to fully explore the influencing factors of construction costs and their cross-influencing relations under different stages and execution subjects, thus helping to realize effective control over the influencing factors of construction costs.

Fostering idealogical and polical education via knowledge graph and KNN model: an emphasis on positive psychology

As the primary domain of ideological and political education in higher education institutions, ideological and political courses must align with principles rooted in human psychology and education. Integrating educational psychology into ideological and political teaching in universities enhances the scientific, targeted, and forward-thinking nature of such education. The burgeoning exploration of knowledge graph applications has extended to machine translation, semantic search, and intelligent question answering. Diverging from traditional text matching, the knowledge spectrum graph transforms information acquisition in search engines. This paper pioneers a predictive system for delineating the relationship between educational psychology and ideological and political education in universities. Initially, it extracts diverse psychological mapping relationships of students, constructing a knowledge graph. By employing the KNN algorithm, the system analyzes psychological characteristics to effectively forecast the relationship between educational psychology and ideological and political education in universities. The system's functionality is meticulously detailed in this paper, and its performance is rigorously tested. The results demonstrate high accuracy, recall rates, and F1 values. The F1 score can reach 0.95enabling precise sample classification. The apex of the average curve for system response time peaks at approximately 2.5 s, maintaining an average response time of less than 3 s. This aligns seamlessly with the demands of practical online teaching requirements. The system adeptly forecasts the relationship between educational psychology and ideological and political education in universities, meeting response time requirements and thereby fostering the scientific and predictive nature of ideological and political teaching in higher education institutions.

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.

A Distributed Dynamic Framework to Allocate Collaborative Tasks Based on Capability Matching in Heterogeneous Multirobot Systems

Collaboration among a group of robots with heterogeneous capabilities is an important research problem that enables to combine different robot functionalities, and thus, conducts complex tasks that may be difficult to achieve by a single robot with limited resources. In this paper, we propose a new distributed task allocation framework based on the capability matching of heterogeneous robots. The framework is composed of an ontological dynamic knowledge graph model and a hardware control scheme to model the capability and optimize resource utilization for collaborative tasks. We introduce an intuitive hardware control scheme based on a dynamic knowledge graph that resolves possible conflicts between the hardware control of different types of robots. Action sequences are produced by a task and motion planning algorithm to collaboratively perform the assigned task. The performance of the proposed methodology is evaluated by both simulations and hardware experiments.

An Event-Centric Knowledge Graph Approach for Public Administration as an Enabler for Data Analytics

In a continuously evolving environment, organizations, including public administrations, need to quickly adapt to change and make decisions in real-time. This requires having a real-time understanding of their context that can be achieved by adopting an event-native mindset in data management which focuses on the dynamics of change compared to the state-based traditional approaches. In this context, this paper proposes the adoption of an event-centric knowledge graph approach for the holistic data management of all data repositories in public administration. Towards this direction, the paper proposes an event-centric knowledge graph model for the domain of public administration that captures these dynamics considering events as first-class entities for knowledge representation. The development of the model is based on a state-of-the-art analysis of existing event-centric knowledge graph models that led to the identification of core concepts related to event representation, on a state-of-the-art analysis of existing public administration models that identified the core entities of the domain, and on a theoretical analysis of concepts related to events, public services, and effective public administration in order to outline the context and identify the domain-specific needs for event modeling. Further, the paper applies the model in the context of Greek public administration in order to validate it and showcase the possibilities that arise. The results show that the adoption of event-centric knowledge graph approaches for data management in public administration can facilitate data analytics, continuous integration, and the provision of a 360-degree-view of end-users. We anticipate that the proposed approach will also facilitate real-time decision-making, continuous intelligence, and ubiquitous AI.

Exploring the Guerrilla Warfare of the National Government Navy Behind Enemy Lines in the Yangtze River Basin during the War of Resistance Against Japanese Aggression

Abstract The guerrilla warfare behind enemy lines of the national government is still a weak link in the study of the history of the war of resistance, and the naval guerrilla warfare on the Yangtze River is even less specifically discussed. This paper first analyzes the change in the national government’s naval operational thinking after the July 7 Incident and up to the Battle of Wuhan and divides the naval operations in the Yangtze River Basin during the period of total war of resistance into four stages. Then, combined with the ontology theory and taking various archives, museums, libraries, and memorial halls as sources, after fusing heterogeneous data, it adopts the relevant writings, notes, diaries, speeches, photographs, objects of the Navy’s Yangtze River Resistance as well as the results of the archive compilation formed later to construct the Navy’s Yangtze River Resistance Archives Knowledge Graph Model. After the reasoning and searching of the knowledge map, it is concluded that in 1938 and 1940, the Yangtze River Bray Guerrilla Group killed 950 and 3020 enemies, respectively, destroyed 20,400 tons and 118,400 tons of warships, strongly supported the Battle of Wuhan and two Battles of Xiangbei, and prevented the Japanese army from invading to the west along the river, and in 1940, it destroyed the Japanese large-scale transports and merchant ships of a total of 58,700 tons, which dealt a heavy blow to the Japanese army on the Yangtze River and the Yangtze River. Heavy blows to the Japanese water transport capacity of the Yangtze River delayed its front and rear troops, weapons, supplies, and transportation plans so that its ability to expand received a major limitation, unable to continue the attack on the Yangtze River west of Wuhan, to defend the safety of Chongqing and Sichuan. This paper makes up for the research gaps in the history of the National Government’s naval resistance and the history of guerrilla battles in the Bray of the Yangtze River Basin and provides a useful exploration of the combination of resistance history research and information technology.

Research on distribution network fault processing technology based on knowledge of graph

Safety and reliability are the basis of the development of a distribution network. To analyze the risk transmission process in the distribution network and ensure the safe and reliable operation of the power system, this paper intends to use the knowledge graph method to realize the risk analysis of the distribution network information system. Firstly, the knowledge graph method is used to extract and integrate the risk knowledge of the multi-dimensional information collected by the distribution network. Secondly, the knowledge graph model of distribution network risk analysis is constructed, and the multi-dimensional distribution network fault handling and knowledge graph construction oriented to the feeder and platform area are designed. The distribution line parameters of the low-voltage distribution network model, neutral point grounding mode, and different fault types are analyzed, and the non-planned island is searched based on the knowledge graph adjacency matrix. Finally, combined with the simulation experiment, it is verified that the proposed method can effectively depict the information risk process of the distribution network. The structure of this paper starts from the multi-node complex distribution network, combined with a knowledge graph and deep learning method, which can solve the distribution network fault more quickly.

Function2vec: A Geographic Knowledge Graph Model of Urban Function Evolution and Its Application

Urban function evolution (UFE) has become more and more complex in emerging cities. However, insufficient theoretical support exists for the visual expression of the spatial correlation between UFE patterns. In order to fill this gap, we use the 2013 and 2022 Point-of-Interest (POI) data of Shenzhen city to implement the funtion2vec model based on the node2vec model and urban tree theory. In this model, we first divide UFE patterns into three categories: Function Replace (FR), Function Newly Added (FNA), and Function Vanishing (FV). Then, we calculate the correlation between those UFE patterns using their functional vectors, resulting in a graph structure representing the urban function evolution network (UFEN). Based on our case study, we obtained the following conclusions: (1) From 2013 to 2022, the UFE in Shenzhen was primarily dominated by FR (89.44%). (2) FV and FNA exhibit a long-tailed distribution, adhering to the 20–80 law. (3) Through the UFEN based on FR, healthcare services are well suited to form mutual complementarities with other functions; science, education, and cultural services demand a higher complementarity with other functions; administrative offices exhibit a strong diversity in their evolutionary patterns; and the integration of transportation hubs with other functions results in a significantly deviating urban function evolution from its original pattern. The above conclusions suggest that function2vec can well express UFE in emerging cities by adding spatial correlation in UFE.

A question answering system for assembly process of wind turbines based on multi-modal knowledge graph and large language model

In the field of wind power generation, wind turbines serve as the foundation for harnessing electrical energy. However, the assembly process information for wind turbines is typically dispersed among various modalities such as 3D models, natural text, and images in the form of process documents. The difficulty in effectively utilising historical process knowledge hampers the efficiency of assembly process design and subsequently affects production efficiency. To address this issue, this paper constructs a Multi-modal Process Knowledge Graph for Wind Turbines, named MPKG-WT. Additionally, a wind turbine assembly process question-answering system combining multi-modal knowledge graphs with large language models (LLMs) is proposed to enable efficient utilisation of historical assembly process knowledge. The proposed approach achieves outstanding results when compared with other state-of-the-art KBQA methods and recent LLMs using a wind turbine assembly process dataset. The effectiveness of the approach is further validated through a visualised assembly process question-answering system. The research findings demonstrate a significant improvement in assembly process design efficiency.

KSFinder-a knowledge graph model for link prediction of novel phosphorylated substrates of kinases.

Aberrant protein kinase regulation leading to abnormal substrate phosphorylation is associated with several human diseases. Despite the promise of therapies targeting kinases, many human kinases remain understudied. Most existing computational tools predicting phosphorylation cover less than 50% of known human kinases. They utilize local feature selection based on protein sequences, motifs, domains, structures, and/or functions, and do not consider the heterogeneous relationships of the proteins. In this work, we present KSFinder, a tool that predicts kinase-substrate links by capturing the inherent association of proteins in a network comprising 85% of the known human kinases. We also postulate the potential role of two understudied kinases based on their substrate predictions from KSFinder. KSFinder learns the semantic relationships in a phosphoproteome knowledge graph using a knowledge graph embedding algorithm and represents the nodes in low-dimensional vectors. A multilayer perceptron (MLP) classifier is trained to discern kinase-substrate links using the embedded vectors. KSFinder uses a strategic negative generation approach that eliminates biases in entity representation and combines data from experimentally validated non-interacting protein pairs, proteins from different subcellular locations, and random sampling. We assess KSFinder's generalization capability on four different datasets and compare its performance with other state-of-the-art prediction models. We employ KSFinder to predict substrates of 68 "dark" kinases considered understudied by the Illuminating the Druggable Genome program and use our text-mining tool, RLIMS-P along with manual curation, to search for literature evidence for the predictions. In a case study, we performed functional enrichment analysis for two dark kinases - HIPK3 and CAMKK1 using their predicted substrates. KSFinder shows improved performance over other kinase-substrate prediction models and generalized prediction ability on different datasets. We identified literature evidence for 17 novel predictions involving an understudied kinase. All of these 17 predictions had a probability score ≥0.7 (nine at >0.9, six at 0.8-0.9, and two at 0.7-0.8). The evaluation of 93,593 negative predictions (probability ≤0.3) identified four false negatives. The top enriched biological processes of HIPK3 substrates relate to the regulation of extracellular matrix and epigenetic gene expression, while CAMKK1 substrates include lipid storage regulation and glucose homeostasis. KSFinder outperforms the current kinase-substrate prediction tools with higher kinase coverage. The strategically developed negatives provide a superior generalization ability for KSFinder. We predicted substrates of 432 kinases, 68 of which are understudied, and hypothesized the potential functions of two dark kinases using their predicted substrates.

A performance evaluation method based on combination of knowledge graph and surrogate model

A performance evaluation method based on combination of knowledge graph and surrogate model

Knowledge graph modeling method for product manufacturing process based on human–cyber–physical fusion

The data generated in the product manufacturing process are usually distributed in different formats, triggering fragmented knowledge and disconnected information. To address this problem, we present a knowledge graph modeling method for the product manufacturing process. First, the concepts of human–cyber–physical (HCP) elements are analyzed in detail. The HCP-related classes, attributes, and relations are defined in a formalized manner in the ontology modeling process. Second, a knowledge graph model for the product manufacturing process (KGM/PMP) is constructed by three steps, including knowledge extraction, knowledge fusion, and knowledge reasoning. When constructing the KGM/PMP model, a deep learning method called BERT-D’BiGRU-CRF is presented to automatically extract knowledge from the manufacturing data. Moreover, a set of reasoning rules are designed to infer new knowledge. Finally, a case study is carried out to validate the effectiveness of the proposed method. The validity of the BERT-D’BiGRU-CRF method on knowledge extraction is verified by comparing performance with four other methods. The applicability of the knowledge graph model is demonstrated through developing a prototype system. With this system, manufacturing knowledge can be provided for the demanders rapidly and accurately.