Shunqin Zhang, Sanguo Zhang, Wenduo He, Xuan Zhang
The NER task is largely developed based on well-annotated data. However, in many scenarios, the entities may not be fully annotated, leading to serious performance degradation. To address this issue, the authors propose a robust NER approach that combines a novel PU-learning algorithm and negative sampling. Unlike many existing studies, the proposed method adopts a two-step procedure for handling unlabeled entities, thereby enhancing its capability to mitigate the impact of such entities. Moreover, this algorithm demonstrates high versatility and can be integrated into any token-level NER model with ease. The effectiveness of the proposed method is verified on several classic NER models and datasets, demonstrating its strong ability to handle unlabeled entities. Finally, the authors achieve competitive performances on synthetic and real-world datasets.
NER 任务在很大程度上是基于注释完备的数据开发的。然而，在很多情况下，实体可能没有得到充分注释，从而导致性能严重下降。为了解决这个问题，作者提出了一种结合了新型 PU 学习算法和负采样的稳健 NER 方法。与许多现有研究不同的是，所提出的方法采用了两步程序来处理未标记的实体，从而增强了其减轻此类实体影响的能力。此外，该算法还具有很强的通用性，可以轻松集成到任何标记级 NER 模型中。所提方法的有效性在多个经典 NER 模型和数据集上得到了验证，证明了其处理无标记实体的强大能力。最后，作者在合成数据集和实际数据集上取得了具有竞争力的性能。
{"title":"A Web Semantic-Based Text Analysis Approach for Enhancing Named Entity Recognition Using PU-Learning and Negative Sampling","authors":"Shunqin Zhang, Sanguo Zhang, Wenduo He, Xuan Zhang","doi":"10.4018/ijswis.335113","DOIUrl":"https://doi.org/10.4018/ijswis.335113","url":null,"abstract":"The NER task is largely developed based on well-annotated data. However, in many scenarios, the entities may not be fully annotated, leading to serious performance degradation. To address this issue, the authors propose a robust NER approach that combines a novel PU-learning algorithm and negative sampling. Unlike many existing studies, the proposed method adopts a two-step procedure for handling unlabeled entities, thereby enhancing its capability to mitigate the impact of such entities. Moreover, this algorithm demonstrates high versatility and can be integrated into any token-level NER model with ease. The effectiveness of the proposed method is verified on several classic NER models and datasets, demonstrating its strong ability to handle unlabeled entities. Finally, the authors achieve competitive performances on synthetic and real-world datasets.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":" 20","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139144107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A network intrusion detection method for information systems using federated learning and improved transformer is proposed to address the problems of long detection time and low security and accuracy when analyzing massive data in most existing intrusion detection methods. Firstly, a network intrusion detection system is constructed based on a federated learning framework, and the transformer model is used as its universal detection model. Then, the dataset is divided and an improved generative adversarial network is used for data augmentation to generate a new sample set to overcome the influence of minority class samples. At the same time, the new samples are input into the transformer local model for network attack type detection and analysis. Finally, the authors aggregate the detection results of each local model and input them into the Softmax classifier to obtain the final classification prediction results.
{"title":"A Network Intrusion Detection Method for Information Systems Using Federated Learning and Improved Transformer","authors":"Qi Zhou, Zhoupu Wang","doi":"10.4018/ijswis.334845","DOIUrl":"https://doi.org/10.4018/ijswis.334845","url":null,"abstract":"A network intrusion detection method for information systems using federated learning and improved transformer is proposed to address the problems of long detection time and low security and accuracy when analyzing massive data in most existing intrusion detection methods. Firstly, a network intrusion detection system is constructed based on a federated learning framework, and the transformer model is used as its universal detection model. Then, the dataset is divided and an improved generative adversarial network is used for data augmentation to generate a new sample set to overcome the influence of minority class samples. At the same time, the new samples are input into the transformer local model for network attack type detection and analysis. Finally, the authors aggregate the detection results of each local model and input them into the Softmax classifier to obtain the final classification prediction results.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"6 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The industrial internet of things (IIoT) necessitates robust cross-domain authentication to secure sensitive on-site equipment data. This paper presents a refined reputation-based lightweight consensus mechanism (LRBCM) tailored for IIoT's distributed network structures. Leveraging node reputation values, LRBCM streamlines ledger consensus, minimizing communication overhead and complexity. Comparative experiments show LRBCM outperforms competing mechanisms. It maintains higher throughput as the number of participating nodes increases and achieves a throughput approximately 10.78% higher than ReCon. Moreover, runtime analysis demonstrates LRBCM's scalability, surpassing ReCon by approximately 12.79% with equivalent nodes and transactions. In addition, as a combination of LRBCM, the proposed distributed lightweight authentication mechanism (ELAM) is rigorously evaluated against the security of various attacks, and its resilience is confirmed. Experiments show that ELAM has good efficiency while maintaining high security.
{"title":"Blockchain-Based Lightweight Authentication Mechanisms for Industrial Internet of Things and Information Systems","authors":"Mingrui Zhao, Chunjing Shi, Yixiao Yuan","doi":"10.4018/ijswis.334704","DOIUrl":"https://doi.org/10.4018/ijswis.334704","url":null,"abstract":"The industrial internet of things (IIoT) necessitates robust cross-domain authentication to secure sensitive on-site equipment data. This paper presents a refined reputation-based lightweight consensus mechanism (LRBCM) tailored for IIoT's distributed network structures. Leveraging node reputation values, LRBCM streamlines ledger consensus, minimizing communication overhead and complexity. Comparative experiments show LRBCM outperforms competing mechanisms. It maintains higher throughput as the number of participating nodes increases and achieves a throughput approximately 10.78% higher than ReCon. Moreover, runtime analysis demonstrates LRBCM's scalability, surpassing ReCon by approximately 12.79% with equivalent nodes and transactions. In addition, as a combination of LRBCM, the proposed distributed lightweight authentication mechanism (ELAM) is rigorously evaluated against the security of various attacks, and its resilience is confirmed. Experiments show that ELAM has good efficiency while maintaining high security.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"120 2","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138998426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The implementation of industrial robots across various sectors has ushered in unparalleled advancements in efficiency, productivity, and safety. This paper explores the domain of semantic trajectory planning in the area of industrial robotics. By adeptly merging physical constraints and semantic knowledge of environments, the proposed methodology enables robots to navigate complex surroundings with utmost precision and efficiency. In a landscape marked by dynamic challenges, the research positions semantic trajectory planning as a linchpin in fostering adaptability. It ensures robots interact safely with their surroundings, providing vital object detection and recognition capabilities. The proposed ResNet model exhibits remarkable classification performance, bolstering overall productivity. The study underscores the significance of this approach in addressing real-world industrial applications while emphasizing accuracy, precision, and enhanced productivity.
{"title":"Semantic Trajectory Planning for Industrial Robotics","authors":"Zhou Li, Gengming Xie, Varsha Arya, Kwok Tai Chui","doi":"10.4018/ijswis.334556","DOIUrl":"https://doi.org/10.4018/ijswis.334556","url":null,"abstract":"The implementation of industrial robots across various sectors has ushered in unparalleled advancements in efficiency, productivity, and safety. This paper explores the domain of semantic trajectory planning in the area of industrial robotics. By adeptly merging physical constraints and semantic knowledge of environments, the proposed methodology enables robots to navigate complex surroundings with utmost precision and efficiency. In a landscape marked by dynamic challenges, the research positions semantic trajectory planning as a linchpin in fostering adaptability. It ensures robots interact safely with their surroundings, providing vital object detection and recognition capabilities. The proposed ResNet model exhibits remarkable classification performance, bolstering overall productivity. The study underscores the significance of this approach in addressing real-world industrial applications while emphasizing accuracy, precision, and enhanced productivity.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"5 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138591951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, the internet and smart devices have developed rapidly. Many people no longer rely on newspapers, magazines, or television to receive news. They can see the latest news using computers or mobile phones. According to a study by the Taiwan Internet Information Center, nearly 90% of Taiwanese people have used the internet. Many online streaming services have emerged, and people can easily watch movies and TV programs through computers or mobile phones. Hence, some websites use digital copyright management mechanisms to protect videos from being directly downloaded. However, 30% of websites use AES-128 encryption to protect their content. If the key access mechanism is not well protected, the encryption methodology may be useless. Therefore, this paper proposes a cross-platform digital copyright management mechanism for adaptive streaming. With this mechanism, users do not need to download additional applications, as the mechanism implements Web-Assembly language through the browser.
{"title":"Digital Copyright Management Mechanism Based on Dynamic Encryption for Multiplatform Browsers","authors":"Ming-Te Chen, Yi Yang Chang, Ta Jen Wu","doi":"10.4018/ijswis.334591","DOIUrl":"https://doi.org/10.4018/ijswis.334591","url":null,"abstract":"In recent years, the internet and smart devices have developed rapidly. Many people no longer rely on newspapers, magazines, or television to receive news. They can see the latest news using computers or mobile phones. According to a study by the Taiwan Internet Information Center, nearly 90% of Taiwanese people have used the internet. Many online streaming services have emerged, and people can easily watch movies and TV programs through computers or mobile phones. Hence, some websites use digital copyright management mechanisms to protect videos from being directly downloaded. However, 30% of websites use AES-128 encryption to protect their content. If the key access mechanism is not well protected, the encryption methodology may be useless. Therefore, this paper proposes a cross-platform digital copyright management mechanism for adaptive streaming. With this mechanism, users do not need to download additional applications, as the mechanism implements Web-Assembly language through the browser.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"14 5","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138603462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Al Khaldy, Abdelraouf Ishtaiwi, Ahmad Al-qerem, A. Aldweesh, Mohammad Alauthman, Ammar Almomani, Varsha Arya
Integrating virtual reality (VR) and augmented reality (AR) technology into online stores enables more immersive and engaging shopping experiences, which is crucial for businesses to succeed in today's competitive e-commerce market. These technologies offer unique, personalized experiences that consider the preferences and requirements of each customer. This research aims to understand better the most recent developments in AR and VR technology, and how these technologies might be used in e-commerce. Multiple databases were used to conduct a thorough search, and the inclusion criteria focused on using AR and VR in e-commerce. A total of 55 papers were found and categorized based on the research methodologies and issues used. Based on the findings of the research paper, it can be concluded that integrating AR and VR technologies in e-commerce has significant potential to improve various aspects of the online shopping experience.
将虚拟现实（VR）和增强现实（AR）技术整合到在线商店中，可以实现更加身临其境和引人入胜的购物体验，这对企业在当今竞争激烈的电子商务市场中取得成功至关重要。这些技术考虑到每位顾客的偏好和要求，提供了独特的个性化体验。本研究旨在更好地了解 AR 和 VR 技术的最新发展，以及这些技术在电子商务中的应用。本研究使用了多个数据库进行全面搜索，纳入标准侧重于在电子商务中使用 AR 和 VR。共找到 55 篇论文，并根据所使用的研究方法和问题进行了分类。根据研究论文的结果，可以得出结论：在电子商务中整合 AR 和 VR 技术在改善在线购物体验的各个方面具有巨大潜力。
{"title":"Redefining E-Commerce Experience","authors":"Mohammad Al Khaldy, Abdelraouf Ishtaiwi, Ahmad Al-qerem, A. Aldweesh, Mohammad Alauthman, Ammar Almomani, Varsha Arya","doi":"10.4018/ijswis.334123","DOIUrl":"https://doi.org/10.4018/ijswis.334123","url":null,"abstract":"Integrating virtual reality (VR) and augmented reality (AR) technology into online stores enables more immersive and engaging shopping experiences, which is crucial for businesses to succeed in today's competitive e-commerce market. These technologies offer unique, personalized experiences that consider the preferences and requirements of each customer. This research aims to understand better the most recent developments in AR and VR technology, and how these technologies might be used in e-commerce. Multiple databases were used to conduct a thorough search, and the inclusion criteria focused on using AR and VR in e-commerce. A total of 55 papers were found and categorized based on the research methodologies and issues used. Based on the findings of the research paper, it can be concluded that integrating AR and VR technologies in e-commerce has significant potential to improve various aspects of the online shopping experience.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"34 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139217988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sentiment analysis and stance detection are interrelated problems of affective computing, and their outputs commonly complement each other. The focus of this article is to determine sentiments and stances of Twitter users about vaccination. A tweet dataset on COVID-19 vaccination is compiled and jointly annotated with sentiment and stance. This deep learning approach employs BERT, which is a model based on pre-trained transformers. The generative deep learning model, ChatGPT, is also used for stance and sentiment analysis on the dataset. ChatGPT achieves the best performance for stance detection, while BERT is the best performer for sentiment analysis. This study is the first one to observe stance and sentiment detection performance of ChatGPT on health-related tweets. This article also includes a full-fledged system proposal based on automatic sentiment and stance analysis. COVID-19 pandemic is an impactful global public health phenomenon, and hence, joint extraction of sentiments and stances from health-related tweets can profoundly contribute to health-related decision-making processes.
{"title":"Deep Learning-Based Sentiment and Stance Analysis of Tweets About Vaccination","authors":"D. Küçük, Nursal Arıcı","doi":"10.4018/ijswis.333865","DOIUrl":"https://doi.org/10.4018/ijswis.333865","url":null,"abstract":"Sentiment analysis and stance detection are interrelated problems of affective computing, and their outputs commonly complement each other. The focus of this article is to determine sentiments and stances of Twitter users about vaccination. A tweet dataset on COVID-19 vaccination is compiled and jointly annotated with sentiment and stance. This deep learning approach employs BERT, which is a model based on pre-trained transformers. The generative deep learning model, ChatGPT, is also used for stance and sentiment analysis on the dataset. ChatGPT achieves the best performance for stance detection, while BERT is the best performer for sentiment analysis. This study is the first one to observe stance and sentiment detection performance of ChatGPT on health-related tweets. This article also includes a full-fledged system proposal based on automatic sentiment and stance analysis. COVID-19 pandemic is an impactful global public health phenomenon, and hence, joint extraction of sentiments and stances from health-related tweets can profoundly contribute to health-related decision-making processes.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"35 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139251988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the problems of missing local context features, single word vector representation, and low entity recognition accuracy, a method for e-medical recording with named entity recognition, which is based on BERT and model fusion, is proposed. First, with the model of BERT for pre-training, the preceding and following contextual information is fused for the enhancement of word semantic representation and alleviation of the problem of polysemy; second, the network of bi-directional long-short term memory is for obtaining the sequence feature matrix, generation of optimal sequence in global sense achieved through the conditional random field model; finally, data enhancement is used to alleviate the class imbalance and improve the model ability in generalization. Results of the experiments find model proposal measured by F1 on CCKS21 data set reaches 0.8548, which is 0.51% and 0.08% higher than models with ID-CNNs-CRF and multi-task RNN. This demonstrates the excellent performance of the method proposed in this paper in improving named entity recognition.
{"title":"A Named Entity Recognition Approach for Electronic Medical Records Using BERT Semantic Enhancement and BiLSTM","authors":"Xuewei Lai, Qingqing Jie","doi":"10.4018/ijswis.333711","DOIUrl":"https://doi.org/10.4018/ijswis.333711","url":null,"abstract":"Aiming at the problems of missing local context features, single word vector representation, and low entity recognition accuracy, a method for e-medical recording with named entity recognition, which is based on BERT and model fusion, is proposed. First, with the model of BERT for pre-training, the preceding and following contextual information is fused for the enhancement of word semantic representation and alleviation of the problem of polysemy; second, the network of bi-directional long-short term memory is for obtaining the sequence feature matrix, generation of optimal sequence in global sense achieved through the conditional random field model; finally, data enhancement is used to alleviate the class imbalance and improve the model ability in generalization. Results of the experiments find model proposal measured by F1 on CCKS21 data set reaches 0.8548, which is 0.51% and 0.08% higher than models with ID-CNNs-CRF and multi-task RNN. This demonstrates the excellent performance of the method proposed in this paper in improving named entity recognition.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"27 17","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139267619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There are many problems with remote sensing images, such as large data scales, complex illumination conditions, occlusion, and dense targets. The existing semantic segmentation methods for remote sensing images are not accurate enough for small and irregular target segmentation results, and the edge extraction results are poor. The authors propose a remote sensing image segmentation method based on a DCNN and multiscale feature fusion. Firstly, an end-to-end remote sensing image segmentation model using complete residual connection and multiscale feature fusion was designed based on a deep convolutional encoder–decoder network. Secondly, weighted high-level features were obtained using an attention mechanism, which better preserved the edges, texture, and other information of remote sensing images. The experimental results on ISPRS Potsdam and Urban Drone datasets show that compared with the comparison methods, this method has better segmentation effect on small and irregular objects and achieves the best segmentation performance while ensuring the computation speed.
{"title":"Remote Sensing Image Semantic Segmentation Method Based on a Deep Convolutional Neural Network and Multiscale Feature Fusion","authors":"Guangzhen Zhang, Wangyang Jiang","doi":"10.4018/ijswis.333712","DOIUrl":"https://doi.org/10.4018/ijswis.333712","url":null,"abstract":"There are many problems with remote sensing images, such as large data scales, complex illumination conditions, occlusion, and dense targets. The existing semantic segmentation methods for remote sensing images are not accurate enough for small and irregular target segmentation results, and the edge extraction results are poor. The authors propose a remote sensing image segmentation method based on a DCNN and multiscale feature fusion. Firstly, an end-to-end remote sensing image segmentation model using complete residual connection and multiscale feature fusion was designed based on a deep convolutional encoder–decoder network. Secondly, weighted high-level features were obtained using an attention mechanism, which better preserved the edges, texture, and other information of remote sensing images. The experimental results on ISPRS Potsdam and Urban Drone datasets show that compared with the comparison methods, this method has better segmentation effect on small and irregular objects and achieves the best segmentation performance while ensuring the computation speed.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"30 18","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139267718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hugo Lebredo, Daniel Fernández-Álvarez, Jose Emilio Labra-Gayo
Several problems arise due to the differences between dentistry and general medicine. The storage of dental data in information silos, the incompatibility of data between different dental clinics or institutions from other medical areas are the most significant ones. The authors propose a decentralized architecture that combines FHIR archetypes, shape expressions, and personal online datastores (PODs) to tackle those issues as follows: FHIR archetypes are used to express the data, shape expressions are used to handle data structure and data access requests, and PODs are used to store information in a decentralized and safe manner that let the owner of the information stored to handle data access. The system allows the patient to store dental information from heterogeneous data sources transparently and respecting the patient's right to autonomy and consent. In this paper, the authors develop this architecture proposal and discuss its relevance and feasibility in the area of dental health.
{"title":"A Decentralized Architecture for Semantic Interoperability of Personal Dental Data Based on FHIR","authors":"Hugo Lebredo, Daniel Fernández-Álvarez, Jose Emilio Labra-Gayo","doi":"10.4018/ijswis.333633","DOIUrl":"https://doi.org/10.4018/ijswis.333633","url":null,"abstract":"Several problems arise due to the differences between dentistry and general medicine. The storage of dental data in information silos, the incompatibility of data between different dental clinics or institutions from other medical areas are the most significant ones. The authors propose a decentralized architecture that combines FHIR archetypes, shape expressions, and personal online datastores (PODs) to tackle those issues as follows: FHIR archetypes are used to express the data, shape expressions are used to handle data structure and data access requests, and PODs are used to store information in a decentralized and safe manner that let the owner of the information stored to handle data access. The system allows the patient to store dental information from heterogeneous data sources transparently and respecting the patient's right to autonomy and consent. In this paper, the authors develop this architecture proposal and discuss its relevance and feasibility in the area of dental health.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"25 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134957160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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