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نتیجه جستجو - semantic web

تعداد مقالات یافته شده: 43
ردیف عنوان نوع
1 Multi-Ontology Mapping Generative Adversarial Network in Internet of Things for Ontology Alignment
نگاشت چند هستی شناسی شبکه متخاصم مولد در اینترنت اشیا برای تراز هستی شناسی-2022
On the Semantic web, ontologies are thought to be the remedy to data heterogeneity, and correlating ontologies is a highly effective technique. Although the use of representation learning approaches to a variety of applications has showed significant promise, they have had little effect on the issue of ontology matching and classification. In order to establish alignments between two ontologies, this research presents the Multi-Ontology Mapping Generative Adversarial Network in Internet of Things (MOMGANI). For the instance of ontology mapping, we suggest using a two-system representation learning network consisting of a Generator and Discriminator. The Generator applies a probabilistic softmax classifier to the different Name, Label, Comments, Properties, Instance descriptions, concept characteristics, and the neighbourhood concepts for each of the ontologys properties. In order to support the assertions that the Generator has generated, the Discriminator network employs a novel Bidirectional Long Short-Term Memory (Bi-LSTM network) with an Ontology Attention mechanism enhanced by the concept’s descriptions. As a result, both systems are in a feedback mechanism where they can learn from one another. The system will produce a set of triples that list all the associated concepts from various ontologies as its final product. Domain experts will review these triples outside of the band to ensure that only true concepts and triples are chosen for the alignment. In comparison to using the ontologies separately, the aligned ontology enables extended querying and inference across related ontologies and domains. Considering metrics like recall, precision, and F-measure, the experimental evaluation was performed utilizing the datasets for classes alignment, property alignment, and instances alignment. The proposed architecture provides a recall, precision, and F-measure of 0.92, 0.99, and 0.83 respectively which reveals that this model outperforms the traditional methods.
Keywords: Generative adversarial network | Ontology alignment | IoT and OntoGenerator and OntoLSTM
مقاله انگلیسی
2 Knowledge-Based Management of Virtual Training Scenarios
مدیریت دانش محور سناریوهای آموزش مجازی-2021
Virtual reality (VR) gains increasing attention as a method of implementing training systems in different domains, in particular, when real training is potentially dangerous for the trainees or the environment, or requires expensive equipment. The essential element of professional training is domain-specific knowledge, which can be represented using the semantic web approach. It enables reasoning as well as complex queries against the representation of training scenarios, which can be valuable for teaching purposes. However, the available methods and tools for creating VR training systems do not use semantic knowledge representation. Currently, the creation, modification, and management of training scenarios require skills in programming and computer graphics. Hence, they are unavailable to domain experts without expertise in IT. In this paper, we propose an ontology-based representation and a method of modeling VR training scenarios. In our approach, trainees’ activities, potential mistakes as well as equipment and its possible errors are represented using domain knowledge understandable to domain experts. We illustrate the approach by modeling VR training scenarios for electrical operators of high-voltage installations.
Keywords: semantic web | knowledge representation | ontologies | training | virtual reality | 3D content
مقاله انگلیسی
3 Combining computer vision with semantic reasoning for on-site safety management in construction
ترکیب بینایی ماشین با استدلال معنایی برای مدیریت ایمنی در هر دو سو در ساخت -2021
Computer vision has been utilized to extract safety-related information from images with the advancement of video monitoring systems and deep learning algorithms. However, construction safety management is a knowledge-intensive task; for instance, safety managers rely on safety regulations and their prior knowledge during a jobsite safety inspection. This paper presents a conceptual framework that combines computer vision and ontology techniques to facilitate the management of safety by semantically reasoning hazards and corre- sponding mitigations. Specifically, computer vision is used to detect visual information from on-site photos while the safety regulatory knowledge is formally represented by ontology and semantic web rule language (SWRL) rules. Hazards and corresponding mitigations can be inferred by comparing extracted visual information from construction images with pre-defined SWRL rules. Finally, the example of falls from height is selected to validate the theoretical and technical feasibility of the developed conceptual framework. Results show that the proposed framework operates similar to the thinking model of safety managers and can facilitate on-site hazard identi- fication and prevention by semantically reasoning hazards from images and listing corresponding mitigations. 1. Introduction
keywords: بینایی ماشین | هستی شناسی | استدلال معنایی | شناسایی ریسک | مدیریت ایمنی ساخت | Computer vision | Ontology | Semantic reasoning | Hazard identification | Construction safety management
مقاله انگلیسی
4 The effect of WeChat-based training on improving the knowledge of tuberculosis management of rural doctors
تأثیر آموزش مبتنی بر وی چت بر ارتقای دانش مدیریت سل در پزشکان روستایی-2021
Computer vision has been utilized to extract safety-related information from images with the advancement of video monitoring systems and deep learning algorithms. However, construction safety management is a knowledge-intensive task; for instance, safety managers rely on safety regulations and their prior knowledge during a jobsite safety inspection. This paper presents a conceptual framework that combines computer vision and ontology techniques to facilitate the management of safety by semantically reasoning hazards and corresponding mitigations. Specifically, computer vision is used to detect visual information from on-site photos while the safety regulatory knowledge is formally represented by ontology and semantic web rule language (SWRL) rules. Hazards and corresponding mitigations can be inferred by comparing extracted visual information from construction images with pre-defined SWRL rules. Finally, the example of falls from height is selected to validate the theoretical and technical feasibility of the developed conceptual framework. Results show that the proposed framework operates similar to the thinking model of safety managers and can facilitate on-site hazard identification and prevention by semantically reasoning hazards from images and listing corresponding mitigations.
keywords: سل | مدیریت | آموزش مبتنی بر وی چت | پزشکان روستایی | چین | Tuberculosis | Management | WeChat-based training | Rural doctors | China
مقاله انگلیسی
5 Adding value to Linked Open Data using a multidimensional model approach based on the RDF Data Cube vocabulary
افزودن ارزش به داده های باز شده پیوند شده با استفاده از روش مدل چند بعدی مبتنی بر واژگان مکعبی داده های RDF -2020
Most organisations using Open Data currently focus on data processing and analysis. However, although Open Data may be available online, these data are generally of poor quality, thus discouraging others from contributing to and reusing them. This paper describes an approach to publish statistical data from public repositories by using Semantic Web standards published by the W3C, such as RDF and SPARQL, in order to facilitate the analysis of multidimensional models. We have defined a framework based on the entire lifecycle of data publication including a novel step of Linked Open Data assessment and the use of external repositories as knowledge base for data enrichment. As a result, users are able to interact with the data generated according to the RDF Data Cube vocabulary, which makes it possible for general users to avoid the complexity of SPARQL when analysing data. The use case was applied to the Barcelona Open Data platform and revealed the benefits of the application of our approach, such as helping in the decision-making process.
Keywords: Linked Open Data | Multidimensional modelling | Conceptual modelling | RDF Data Cube vocabulary | Semantic web | Big data
مقاله انگلیسی
6 Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods
شناسایی موقعیت داخلی بیماران برای هدایت مراقبت های مجازی: رویکرد هوش مصنوعی با استفاده از یادگیری ماشین و روش های دانش بنیان-2020
In a digitally enabled healthcare setting, we posit that an individual’s current location is pivotal for supporting many virtual care services—such as tailoring educational content towards an individual’s current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive decline through daily activities in their home. However, unobtrusively estimating an individual’s indoor location in real-world care settings is still a challenging problem. Moreover, the needs of location-specific care interventions go beyond absolute coordinates and require the individual’s discrete semantic location; i.e., it is the concrete type of an individual’s location (e.g., exam vs. waiting room; bathroom vs. kitchen) that will drive the tailoring of educational content or recognition of activities. We utilized Machine Learning methods to accurately identify an individual’s discrete location, together with knowledge-based models and tools to supply the associated semantics of identified locations. We considered clustering solutions to improve localization accuracy at the expense of granularity; and investigate sensor fusion-based heuristics to rule out false location estimates. We present an AI-driven indoor localization approach that integrates both data-driven and knowledge-based processes and artifacts. We illustrate the application of our approach in two compelling healthcare use cases, and empirically validated our localization approach at the emergency unit of a large Canadian pediatric hospital.
Keywords: Virtual care | Ambient sensors | Indoor localization | Machine learning | Semantic web | eHealth platform | Data fusion | Self-management | Ambient assisted living | Activities of daily living
مقاله انگلیسی
7 A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome
یک سیستم هوش مصنوعی پیشنهادی برای حمایت از تشخیص صرع بر اساس طبقه بندی صرع 2017: نشان داده شده توسط سندرم دراوت-2020
Purpose: The 2017 epilepsy and seizure diagnosis framework emphasizes epilepsy syndromes and the etiologybased approach.We developed a propositional artificial intelligence (AI) system based on the above concepts to support physicians in the diagnosis of epilepsy. Methods:We analyzed and built ontology knowledge for the classification of seizure patterns, epilepsy, epilepsy syndrome, and etiologies. Protégé ontology tool was applied in this study. In order to enable the system to be close to the inferential thinking of clinical experts, we classified and constructed knowledge of other epilepsyrelated knowledge, including comorbidities, epilepsy imitators, epilepsy descriptors, characteristic electroencephalography (EEG) findings, treatments, etc. We used the OntologyWeb Language with Description Logic (OWL-DL) and Semantic Web Rule Language (SWRL) to design rules for expressing the relationship between these ontologies. Results: Dravet syndrome was taken as an illustration for epilepsy syndromes implementation.We designed an interface for the physician to enter the various characteristics of the patients. Clinical data of an 18-year-old boy with epilepsy was applied to the AI system. Through SWRL and reasoning engine Drools execution, we successfully demonstrate the process of differential diagnosis. Conclusion: We developed a propositional AI system by using the OWL-DL/SWRL approach to deal with the complexity of current epilepsy diagnosis. The experience of this system, centered on the clinical epilepsy syndromes, paves a path to construct an AI system for further complicated epilepsy diagnosis.
Keywords: Epilepsy syndrome | Etiology | OWL-DL | Protégé | Seizure classification | SemanticWeb Rule Language
مقاله انگلیسی
8 A social-semantic recommender system for advertisements
یک سیستم پیشنهادی اجتماعی معنایی برای تبلیغات-2020
Social applications foster the involvement of end users in Web content creation, as a result of which a new source of vast amounts of data about users and their likes and dislikes has become available. Having access to users’ contributions to social sites and gaining insights into the consumers’ needs is of the utmost importance for marketing decision making in general, and to advertisement recommendation in particular. By analyzing this information, advertisement recommendation systems can attain a better understanding of the users’ interests and preferences, thus allowing these solutions to provide more precise ad suggestions. However, in addition to the already complex challenges that hamper the performance of recommender systems (i.e., data sparsity, cold-start, diversity, accuracy and scalability), new issues that should be considered have also emerged from the need to deal with heterogeneous data gathered from disparate sources. The technologies surrounding Linked Data and the Semantic Web have proved effective for knowledge management and data integration. In this work, an ontology-based advertisement recommendation system that leverages the data produced by users in social networking sites is proposed, and this approach is substantiated by a shared ontology model with which to represent both users’ profiles and the content of advertisements. Both users and advertisement are represented by means of vectors generated using natural language processing techniques, which collect ontological entities from textual content. The ad recommender framework has been extensively validated in a simulated environment, obtaining an aggregated f-measure of 79.2% and a Mean Average Precision at 3 (MAP@3) of 85.6%.
Keywords:Knowledge-based systems | Recommender systems | Natural language processing | Advertising | Social network services
مقاله انگلیسی
9 An ontology-based methodology for hazard identification and causation analysis
یک روش مبتنی بر هستی شناسی برای شناسایی ریسک و تجزیه و تحلیل علیت-2019
This article presents a dynamic hazard identification methodology founded on an ontology-based knowl-edge modeling framework coupled with probabilistic assessment. The objective is to develop an efficientand effective knowledge-based tool for process industries to screen hazards and conduct rapid risk esti-mation. The proposed generic model can translate an undesired process event (state of the process)into a graphical model, demonstrating potential pathways to the process event, linking causation tothe transition of states. The Semantic web-based Web Ontology Language (OWL) is used to captureknowledge about unwanted process events. The resulting knowledge model is then transformed intoProbabilistic-OWL (PR-OWL) based Multi-Entity Bayesian Network (MEBN). Upon queries, the MEBNsproduce Situation Specific Bayesian Networks (SSBN) to identify hazards and their pathways along withprobabilities. Two open-source software programs, Protégé and UnBBayes, are used. The developed modelis validated against 45 industrial accidental events extracted from the U.S. Chemical Safety Board’s (CSB)database. The model is further extended to conduct causality analysis.
Keywords:Hazard identification |Probabilistic ontology |Web ontology language | Multi-entity Bayesian network | Expert system
مقاله انگلیسی
10 مروری بر تجمیع دستگاه های مدل سازی اطلاعات ساختمانی (BIM) و اینترنت اشیاء (IoT): وضعیت کنونی و روند آینده
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 56
تجمیع مدل سازی اطلاعات ساختمانی (BIM) با داده های زمان واقعی(بلادرنگ) دستگاه های اینترنت اشیاء (IoT)، نمونه قوی را برای بهبود ساخت وساز و بهره وری عملیاتی ارائه می دهد. اتصال جریان-های داده های زمان واقعی که بر گرفته از مجموعه هایی از شبکه های حسگرِ اینترنت اشیاء (که این جریان های داده ای، به سرعت در حال گسترش هستند) می باشند، با مدل های باکیفیت BIM، در کاربردهای متعددی قابل استفاده می باشد. با این حال، پژوهش در زمینه ی تجمیع BIM و IOT هنوز در مراحل اولیه ی خود قرار دارد و نیاز است تا وضعیت فعلی تجمیع دستگاه های BIM و IoT درک شود. این مقاله با هدف شناسایی زمینه های کاربردی نوظهور و شناسایی الگوهای طراحی رایج در رویکردی که مخالف با تجمیع دستگاه BIM-IoT می باشد، مرور جامعی در این زمینه انجام می دهد و به بررسی محدودیت های حاضر و پیش بینی مسیرهای تحقیقاتی آینده می پردازد. در این مقاله، در مجموع، 97 مقاله از 14 مجله مربوط به AEC و پایگاه داده های موجود در صنایع دیگر (در دهه گذشته)، مورد بررسی قرار گرفتند. چندین حوزه ی رایج در این زمینه تحت عناوین عملیات ساخت-وساز و نظارت، مدیریت ایمنی و بهداشت، لجستیک و مدیریت ساختمان، و مدیریت تسهیلات شناسایی شدند. نویسندگان، 5 روش تجمیع را همراه با ذکر توضیحات، نمونه ها و بحث های مربوط به آنها به طور خلاصه بیان کرده اند. این روش های تجمیع از ابزارهایی همچون واسط های برنامه نویسی BIM، پایگاه داده های رابطه ای، تبدیل داده های BIM به پایگاه داده های رابطه ای با استفاده از طرح داده های جدید، ایجاد زبان پرس وجوی جدید، فناوری های وب معنایی و رویکردهای ترکیبی، استفاده می کنند. براساس محدودیت های مشاهده شده، با تمرکز بر الگوهای معماری سرویس گرا (SOA) و راهبردهای مبتنی بر وب برای ادغام BIM و IoT، ایجاد استانداردهایی برای تجمیع و مدیریت اطلاعات، حل مسئله همکاری و محاسبات ابری، مسیرهای برجسته ای برای تحقیقات آینده پیشنهاد شده است.
کلمه های کلیدی: مدل سازی اطلاعات ساختمانی (BIM) | دستگاه اینترنت اشیاء (IoT) | حسگرها | ساختمان هوشمند | شهر هوشمند | محیط ساخته شده هوشمند | تجمیع.
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