Domain Knowledge Research Articles

Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning.

Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MRI involves physics-based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data-driven approaches. Our review will introduce the significant challenges faced by such knowledge-driven DL approaches in the context of fast MRI along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.

How generative AI will drive enterprise innovation

Purpose Most recent C-suite surveying suggests current applications of generative AI, although hyped, are fragmented and unlikely to yield major financial returns anticipated. Instead, business leaders expect major value from generative AI will be achieved through application of generative AI to innovation: operational innovation, product and service innovation, and most elusive of all, business model innovation. Design/methodology/approach Findings and analysis presented draws on data from several surveys of C-level executives conducted by IBM Institute for Business Value in collaboration with Oxford Economics during 2023. Each survey focused on the potential of generative AI in a particular business area. The n-count of each survey ranged from 100-3000. Findings 1. Business leaders expect generative AI to build on returns achieved from investments in traditional AI, with 10 percent RoI expected on generative AI investments by 2025. 2. Executives anticipate that generative AI will have most impact when implemented to expand innovation. 3. Specific examples provided for operational innovation, product innovation, and business model innovation Research limitations/implications We are still very early in the generative AI development cycle. We have made best efforts to project, but only time will tell for sure. Practical implications Business application of generative AI are extremely fragmented. Despite the desire to throw investments at the wall to see what sticks, it is important that leaders take a structured approach to generative AI, focusing on RoI from innovation investments. Social implications To alleviate negative impacts of generative AI, focusing on innovation potential and value maximization is crucial. Originality/value This research is based on completely new surveying and data. This papers adds to the sum total of new knowledge in the generative AI domain.

HaLo‐NeRF: Learning Geometry‐Guided Semantics for Exploring Unconstrained Photo Collections

AbstractInternet image collections containing photos captured by crowds of photographers show promise for enabling digital exploration of large‐scale tourist landmarks. However, prior works focus primarily on geometric reconstruction and visualization, neglecting the key role of language in providing a semantic interface for navigation and fine‐grained understanding. In more constrained 3D domains, recent methods have leveraged modern vision‐and‐language models as a strong prior of 2D visual semantics. While these models display an excellent understanding of broad visual semantics, they struggle with unconstrained photo collections depicting such tourist landmarks, as they lack expert knowledge of the architectural domain and fail to exploit the geometric consistency of images capturing multiple views of such scenes. In this work, we present a localization system that connects neural representations of scenes depicting large‐scale landmarks with text describing a semantic region within the scene, by harnessing the power of SOTA vision‐and‐language models with adaptations for understanding landmark scene semantics. To bolster such models with fine‐grained knowledge, we leverage large‐scale Internet data containing images of similar landmarks along with weakly‐related textual information. Our approach is built upon the premise that images physically grounded in space can provide a powerful supervision signal for localizing new concepts, whose semantics may be unlocked from Internet textual metadata with large language models. We use correspondences between views of scenes to bootstrap spatial understanding of these semantics, providing guidance for 3D‐compatible segmentation that ultimately lifts to a volumetric scene representation. To evaluate our method, we present a new benchmark dataset containing large‐scale scenes with ground‐truth segmentations for multiple semantic concepts. Our results show that HaLo‐NeRF can accurately localize a variety of semantic concepts related to architectural landmarks, surpassing the results of other 3D models as well as strong 2D segmentation baselines. Our code and data are publicly available at https://tau‐vailab.github.io/HaLo‐NeRF/.

Geriatrics and artificial intelligence in Spain (Ger-IA project): talking to ChatGPT, a nationwide survey.

The purposes of the study was to describe the degree of agreement between geriatricians with the answers given by an AI tool (ChatGPT) in response to questions related to different areas in geriatrics, to study the differences between specialists and residents in geriatrics in terms of the degree of agreement with ChatGPT, and to analyse the mean scores obtained by areas of knowledge/domains. An observational study was conducted involving 126 doctors from 41 geriatric medicine departments in Spain. Ten questions about geriatric medicine were posed to ChatGPT, and doctors evaluated the AI's answers using a Likert scale. Sociodemographic variables were included. Questions were categorized into five knowledge domains, and means and standard deviations were calculated for each. 130 doctors answered the questionnaire. 126 doctors (69.8% women, mean age 41.4 [9.8]) were included in the final analysis. The mean score obtained by ChatGPT was 3.1/5 [0.67]. Specialists rated ChatGPT lower than residents (3.0/5 vs. 3.3/5 points, respectively, P < 0.05). By domains, ChatGPT ​​scored better (M: 3.96; SD: 0.71) in general/theoretical questions rather than in complex decisions/end-of-life situations (M: 2.50; SD: 0.76) and answers related to diagnosis/performing of complementary tests obtained the lowest ones (M: 2.48; SD: 0.77). Scores presented big variability depending on the area of knowledge. Questions related to theoretical aspects of challenges/future in geriatrics obtained better scores. When it comes to complex decision-making, appropriateness of the therapeutic efforts or decisions about diagnostic tests, professionals indicated a poorer performance. AI is likely to be incorporated into some areas of medicine, but it would still present important limitations, mainly in complex medical decision-making.

Reinforcement Learning Techniques in Optimizing Energy Systems

Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing energy systems, offering the potential to enhance efficiency, reliability, and sustainability. This review paper provides a comprehensive examination of the applications of RL in the field of energy system optimization, spanning various domains such as energy management, grid control, and renewable energy integration. Beginning with an overview of RL fundamentals, the paper explores recent advancements in RL algorithms and their adaptation to address the unique challenges of energy system optimization. Case studies and real-world applications demonstrate the efficacy of RL-based approaches in improving energy efficiency, reducing costs, and mitigating environmental impacts. Furthermore, the paper discusses future directions and challenges, including scalability, interpretability, and integration with domain knowledge. By synthesizing the latest research findings and identifying key areas for further investigation, this paper aims to inform and inspire future research endeavors in the intersection of reinforcement learning and energy system optimization.

Knowledge engineering for wind energy

Abstract. With the rapid evolution of the wind energy sector, there is an ever-increasing need to create value from the vast amounts of data made available both from within the domain and from other sectors. This article addresses the challenges faced by wind energy domain experts in converting data into domain knowledge, connecting and integrating them with other sources of knowledge, and making them available for use in next-generation artificial intelligence systems. To this end, this article highlights the role that knowledge engineering can play in the digital transformation of the wind energy sector. It presents the main concepts underpinning knowledge-based systems and summarises previous work in the areas of knowledge engineering and knowledge representation in a manner that is relevant and accessible to wind energy domain experts. A systematic analysis of the current state of the art on knowledge engineering in the wind energy domain is performed with available tools put into perspective by establishing the main domain actors and their needs, as well as identifying key problematic areas. Finally, recommendations for further development and improvement are provided.

Trends, progress and future directions of nanomaterial based sensors: a bibliometric overview

Purpose Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc. Design/methodology/approach In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021. Findings Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development. Research limitations/implications The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development. Originality/value This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

Application of deep learning model incorporating domain knowledge in international migration forecasting

PurposeForecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.Design/methodology/approachThis study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.FindingsThe model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R2 values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.Originality/valueThis study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.

Speculative investment decisions in cryptocurrency: a structural equation modelling approach

ABSTRACT Cryptocurrency markets are inclined towards speculative usage due to the inherent high risk of financial loss and the potential for substantial gains during transaction completion. In response to this phenomenon, this study represents the inaugural effort to explore the influence of variables such as subjective norms, domain knowledge, impulsive investment tendencies, and self-control on decisions related to speculative investments. Utilising structural equation modelling with a dataset of 367 responses in India, the study is the first of its kind. The research reveals that subjective norms and domain knowledge play a significant role in influencing impulsive investment and self-control. Additionally, impulsive investment exhibits significant associations with decisions involving speculative investments. This insight underscores the complexity wherein individuals, despite exercising self-control, may still engage in speculative decisions that lead to adverse consequences. The findings have practical implications for investors and regulators, offering valuable insights into investment behaviours within the cryptocurrency realm.

Impact of training on knowledge, confidence and attitude amongst community health volunteers in the provision of community-based palliative care in rural Kenya

ObjectivesExisting literature suggests multiple potential roles for community health volunteers (CHVs) in the provision of palliative care (PC) in low- and middle-income countries. In Kenya the role of CHV in the provision of PC has not been reported. The objective of this study was to assess knowledge, confidence, attitude, and clinical practice of community health volunteers after attending a novel palliative care (PC) training program.MethodsA total of 105 CHVs participated in a 3-day in person training followed by a 1-month in person and telephone observation period of the palliative care activities in the community. Structured questionnaires were used pre- and post-training to assess knowledge acquisition, impact on practice, and content delivery. A mixed method study design was conducted 12-month post training to assess impact on clinical practice.ResultsImmediately after training, CHV provided positive ratings on relevance and content delivery. In the month following training, CHVs evaluated 1,443 patients, referred 154, and conducted 110 and 129 tele consults with the patients and PC providers respectively. The follow up survey at 12 months revealed improved knowledge and confidence in various domains of palliative care including symptom and spiritual assessment and provision of basic nursing and bereavement care. Focus group discussions revealed the CHVs ability to interpret symptoms, make referrals, improved communication/ interpersonal relationships, spiritual intervention, patient comfort measures and health care practices as newly learned and practiced skills.ConclusionsWe noted improved knowledge, new skills and change in practice after CHVs participation in a novel training curriculum. CHVs can make important contributions to the PC work force and be first line PC providers in the community as part of larger hub and spoke care model.

Multi-branch myocardial infarction detection and localization framework based on multi-instance learning and domain knowledge.

&#xD;Myocardial infarction (MI) is a serious cardiovascular disease that can cause irreversible damage to the heart, making early identification and treatment crucial. However, automatic MI detection and localization from an electrocardiogram (ECG) remain challenging. In this study, we propose two models, MFB-SENET and MFB-DMIL, for MI detection and localization, respectively. The MFB-SENET model is designed to detect MI, while the MFB-DMIL model is designed to localize MI. The MI localization model employs a specialized attention mechanism to integrate multi-instance learning with domain knowledge. This approach incorporates handcrafted features and introduces a new loss function called lead-loss, to improve MI localization. Grad-CAM is employed to visualize the decision-making process.&#xD;Main Results:&#xD;The proposed method was evaluated on the PTB and PTB-XL databases. Under the inter-patient scheme, the accuracy of MI detection and localization on the PTB database reached 93.88% and 67.17%, respectively. The accuracy of MI detection and localization on the PTB-XL database were 94.89% and 85.83%, respectively. Our method achieved comparable or better performance than other state-of-the-art algorithms. The proposed method combined deep learning and medical domain knowledge, demonstrates effectiveness and reliability, holding promise as an efficient MI diagnostic tool to assist physicians in formulating accurate diagnoses.&#xD.

Promoting pre-service teachers’ knowledge integration from multiple text sources across domains with instructional prompts

AbstractMultiple document comprehension and knowledge integration across domains are particularly important for pre-service teachers, as integrated professional knowledge forms the basis for teaching expertise and competence. This study examines the effects of instructional prompts and relevance prompts embedded in pre-service teachers’ learning processes on the quality their knowledge integration in multiple document comprehension across domains. 109 pre-service teachers participated in an experimental study. They read four texts on “competencies” from different knowledge domains and wrote a text on a given scenario. Experimental group 1 was aided with instructional and relevance prompts, while experimental group 2 received only relevance prompts. The control group received no prompting. Perceived relevance of knowledge integration was assessed in a pre-post-test. Pre-service teachers’ separative and integrative learning, epistemological beliefs, metacognition, study-specific self-concept, and post-experimental motivation were assessed as control variables. Participants’ texts were analyzed concerning knowledge integration by raters and with computer linguistic measures. A key finding is that combined complex prompting enhances pre-service teachers perceived relevance of knowledge integration. This study found effects of prompting types on the pre-service teachers’ semantic knowledge structures. Implications for transfer are discussed.

Knowledge sharing behavior of service sector’s employees to attain sustainable development goals

Purpose The purpose of this study is to get an understanding regarding the clusters of middle management employees on the basis of their knowledge sharing behaviour. Designing knowledge sharing behaviors with a distinct focus for a specified group of employees can be an effective and productive one. As it is often argued that the cluster of employees labeled as “middle management” is the key player for knowledge sharing behaviors – a subject of this study that intends to contribute to management strategy to enhance organizational effectiveness and subsequently to its knowledge sharing phenomona. Design/methodology/approach Cluster analysis was adopted as key tool as a part of quantitative method to accumulate the data from 597 employees who are working within the middle management of service sector located in the union territory of India named Jammu and Kashmir. Findings Three distinct segments namely – “knowledge sharing adepts (KSA),” “knowledge sharing scrupulous (KSC)” and “knowledge sharing servitudes (KSE)” as the prime domains of knowledge sharing behavior are identified. Research limitations/implications To draw a narrow focus, the study was limited to the service sector of a union territory in India, hence the findings may not be generalized. Furthermore, as knowledge sharing behavior of individuals is always evolved out of social and historical practices, findings of this cross-sectional study should ideally be needed to be updated time to time through further research. Practical implications Cluster dynamicism of knowledge sharing behavior based on the differentiated and specified group of employee functions distinctly which in turn increases the organizational productivity with a particular focus on the mid-management of the service sector – a key managerial implication of this study. Originality/value To the best of the authors’ knowledge, this research paper is the first of its kind in Jammu and Kashmir adding value to the international literature in the area of knowledge sharing behaviors of service sector.

UCDCN: a nested architecture based on central difference convolution for face anti-spoofing

AbstractThe significance of facial anti-spoofing algorithms in enhancing the security of facial recognition systems cannot be overstated. Current approaches aim to compensate for the model’s shortcomings in capturing spatial information by leveraging spatio-temporal information from multiple frames. However, the additional branches to extract inter-frame details increases the model’s parameter count and computational workload, leading to a decrease in inference efficiency. To address this, we have developed a robust and easily deployable facial anti-spoofing algorithm. In this paper, we propose Central Difference Convolution UNet++ (UCDCN), which takes advantage of central difference convolution and improves the characterization ability of invariant details in diverse environments. Particularly, we leverage domain knowledge from image segmentation and propose a multi-level feature fusion network structure to enhance the model’s ability to capture semantic information which is beneficial for face anti-spoofing tasks. In this manner, UCDCN greatly reduces the number of model parameters as well as achieves satisfactory metrics on three popular benchmarks, i.e., Replay-Attack, Oulu-NPU and SiW.

MR-Scout : Automated Synthesis of Metamorphic Relations from Existing Test Cases

Metamorphic Testing (MT) alleviates the oracle problem by defining oracles based on metamorphic relations (MRs), that govern multiple related inputs and their outputs. However, designing MRs is challenging, as it requires domain-specific knowledge. This hinders the widespread adoption of MT. We observe that developer-written test cases can embed domain knowledge that encodes MRs. Such encoded MRs could be synthesized for testing not only their original programs but also other programs that share similar functionalities. In this paper, we propose MR-Scout to automatically synthesize MRs from test cases in open-source software (OSS) projects. MR-Scout first discovers MR-encoded test cases (MTCs), and then synthesizes the encoded MRs into parameterized methods (called codified MRs ), and filters out MRs that demonstrate poor quality for new test case generation. MR-Scout discovered over 11,000 MTCs from 701 OSS projects. Experimental results show that over 97% of codified MRs are of high quality for automated test case generation, demonstrating the practical applicability of MR-Scout . Furthermore, codified-MRs-based tests effectively enhance the test adequacy of programs with developer-written tests, leading to 13.52% and 9.42% increases in line coverage and mutation score, respectively. Our qualitative study shows that 55.76% to 76.92% of codified MRs are easily comprehensible for developers.

Information searching in cultural heritage archives: a user study

PurposeThe PICCH research project contributes to opening a dialogue between cultural heritage archives and users. Hence, the users are identified and their information needs, the search strategies they apply and the search challenges they experience are uncovered.Design/methodology/approachA combination of questionnaires and interviews is used for collection of data. Questionnaire data were collected from users of three different audiovisual archives. Semi-structured interviews were conducted with two user groups: (1) scholars searching information for research projects and (2) archivists who perform their own scholarly work and search information on behalf of others.FindingsThe questionnaire results show that the archive users mainly have an academic background. Hence, scholars and archivists constitute the target group for in-depth interviews. The interviews reveal that their information needs are multi-faceted and match the information need typology by Ingwersen. The scholars mainly apply collection-specific search strategies but have in common primarily doing keyword searching, which they typically plan in advance. The archivists do less planning owing to their knowledge of the collections. All interviewees demonstrate domain knowledge, archival intelligence and artefactual literacy in their use and mastering of the archives. The search challenges they experience can be characterised as search system complexity challenges, material challenges and metadata challenges.Originality/valueThe paper provides a rare insight into the complexity of the search situation of cultural heritage archives, and the users’ multi-facetted information needs and hence contributes to the dialogue between the archives and the users.

USES-Net: An Infrared Dim and Small Target Detection Network with Embedded Knowledge Priors

Detecting and identifying small infrared targets has always been a crucial technology for many applications. To address the low accuracy, high false-alarm rate, and poor environmental adaptability that commonly exist in infrared target detection methods, this paper proposes a composite infrared dim and small target detection model called USES-Net, which combines the target prior knowledge and conventional data-driven deep learning networks to make use of both labeled data and the domain knowledge. Based on the typical encoder–decoder structure, USES-Net firstly introduces the self-attention mechanism of Swin Transformer to replace the universal convolution kernel at the encoder end. This helps to extract potential features related to dim, small targets in a larger receptive field. In addition, USES-Net includes an embedded patch-based contrast learning module (EPCLM) to integrate the spatial distribution of the target as a knowledge prior in the training network model. This guides the training process of the constrained network model with clear physical interpretability. Finally, USES-Net also designs a bottom-up cross-layer feature fusion module (AFM) as the decoder of the network, and a data-slicing-aided enhancement and inference method based on Slicing Aided Hyper Inference (SAHI) is utilized to further improve the model’s detection accuracy. An experimental comparative analysis shows that USES-Net achieves the best results on three typical infrared weak-target datasets: NUAA-SIRST, NUDT-SIRST, and IRSTD-1K. The results of the target segmentation are complete and sufficient, which demonstrates the validity and practicality of the proposed method in comparison to others.

On sparse regression, Lp‐regularization, and automated model discovery

AbstractSparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide simple relationships among scientific variables. While the statistical tools for model discovery are well established in the context of linear regression, their generalization to nonlinear regression in material modeling is highly problem‐specific and insufficiently understood. Here we explore the potential of neural networks for automatic model discovery and induce sparsity by a hybrid approach that combines two strategies: regularization and physical constraints. We integrate the concept of Lp regularization for subset selection with constitutive neural networks that leverage our domain knowledge in kinematics and thermodynamics. We train our networks with both, synthetic and real data, and perform several thousand discovery runs to infer common guidelines and trends: L2 regularization or ridge regression is unsuitable for model discovery; L1 regularization or lasso promotes sparsity, but induces strong bias that may aggressively change the results; only L0 regularization allows us to transparently fine‐tune the trade‐off between interpretability and predictability, simplicity and accuracy, and bias and variance. With these insights, we demonstrate that Lp regularized constitutive neural networks can simultaneously discover both, interpretable models and physically meaningful parameters. We anticipate that our findings will generalize to alternative discovery techniques such as sparse and symbolic regression, and to other domains such as biology, chemistry, or medicine. Our ability to automatically discover material models from data could have tremendous applications in generative material design and open new opportunities to manipulate matter, alter properties of existing materials, and discover new materials with user‐defined properties.

Transforming Physics Education: Harnessing the Potential of Concept Mapping Across Cognitive Domains

This study aimed to investigate how concept mapping affects student performance in physics education across different cognitive areas. It concerns a study conducted at Tribhuvan University-affiliated education campuses in Kathmandu and Bhaktapur districts, employing a quasi-experimental pre-test and post-test design. The investigation delves into the efficacy of concept mapping as an instructional tool to enhance student learning outcomes in physics education. The research design included a Control group with 70 participants and an Experimental group with 95 participants. The study showcased superior posttest performance in the Experimental group compared to the Control group across Knowledge, Understanding, Application, and Higher levels of cognitive domains. These findings underscore the effectiveness of Concept Mapping in enhancing student performance, positioning it as a promising educational strategy in physics instruction. The study employed the Kuder-Richardson 21 test to validate the instrument's reliability (coefficient of 0.78) and utilized SPSS version 20 for data analysis. Concept Mapping proves effective in enhancing physics education, demonstrating notable improvement in student achievement across various cognitive domains. Finding of the study revealed that Concept Mapping effectively improved posttest outcomes, emphasizing its beneficial influence on students' understanding and utilization of physics principles.

Acupuncture and tuina knowledge graph with prompt learning

IntroductionAcupuncture and tuina, acknowledged as ancient and highly efficacious therapeutic modalities within the domain of Traditional Chinese Medicine (TCM), have provided pragmatic treatment pathways for numerous patients. To address the problems of ambiguity in the concept of Traditional Chinese Medicine (TCM) acupuncture and tuina treatment protocols, the lack of accurate quantitative assessment of treatment protocols, and the diversity of TCM systems, we have established a map-filling technique for modern literature to achieve personalized medical recommendations.Methods(1) Extensive acupuncture and tuina data were collected, analyzed, and processed to establish a concise TCM domain knowledge base. (2)A template-free Chinese text NER joint training method (TemplateFC) was proposed, which enhances the EntLM model with BiLSTM and CRF layers. Appropriate rules were set for ERE. (3) A comprehensive knowledge graph comprising 10,346 entities and 40,919 relationships was constructed based on modern literature.ResultsA robust TCM KG with a wide range of entities and relationships was created. The template-free joint training approach significantly improved NER accuracy, especially in Chinese text, addressing issues related to entity identification and tokenization differences. The KG provided valuable insights into acupuncture and tuina, facilitating efficient information retrieval and personalized treatment recommendations.DiscussionThe integration of KGs in TCM research is essential for advancing diagnostics and interventions. Challenges in NER and ERE were effectively tackled using hybrid approaches and innovative techniques. The comprehensive TCM KG our built contributes to bridging the gap in TCM knowledge and serves as a valuable resource for specialists and non-specialists alike.