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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 |
A Knowledge Map for ICT Integration in the Silver Economy
نقشه دانش برای ادغام ICT در اقتصاد نقره ای-2021 In the European Union, the silver economy is focused on developing strategies to facilitate the elderly population, mainly in terms
of creating services based on innovations and technology. The paper encompasses a detailed overview of the silver economy in
Estonia and the introduction of ICT solutions for the elderly population through knowledge management techniques. A knowledge
map has been created using the Ontology model in Protégé to demonstrate the key knowledge resources for ICT integration in the
silver economy. The results and information displayed in the knowledge map have been gathered from two detailed workshops
which were further validated through questionnaires and interviews from the professionals in the field. The purpose of this paper is
to discuss the different knowledge resources and competencies relevant to the integration of ICT in the silver economy which will
serve as a basis for the development of a knowledge management model in the future.
keywords: Smart Specialization Strategy | ICT | Silver Economy | Knowledge Map | Ontology | Digital Competency |
مقاله انگلیسی |
3 |
Knowledge Management Process for Air Quality Systems based on Data Warehouse Specification
فرآیند مدیریت دانش برای سیستم های کیفیت هوا بر اساس مشخصات انبار داده-2021 Even though several systems for Air Quality (AQ) monitoring have been in existence for over a decade, a research model for
Knowledge Management (KM) of AQ data has to be created in order to enhance the decision-making and organize the air quality
data collected from the Internet of Things (IoT) consumer devices. This model should be made more performant by ensuring greater
flexibility and interoperability between devices and emerging technologies. In this context, we propose an approach for representing
Data WareHouse (DWH) schema based on an ontology that captures the multidimensional knowledge of tools, techniques, and
technologies used for novel AQ systems. This enhances decision-making by coping with potential problems such as data sources
heterogeneity and covering the various phases of the decision-making life cycle.
Keywords: Knowledge Management | Air Quality | Data Warehouse | Conceptual Data Model | Multidimensional Design | Ontology. |
مقاله انگلیسی |
4 |
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 |
5 |
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 |
6 |
Stages of Knowledge Representation on the Example of the Typology of Interdisciplinarity: Philosophical Aspects
هیچ یک-2021 It is suggested, when creating knowledge management mechanisms to avoid any extremes in their presentation: various "centrisms", hypertrophy in the use of both mathematical and verbal-meaningful knowledge. It is shown that it is important to observe the principle of "ethics of engagement ", which can be implemented on the basis of a productive interdisciplinary synthesis. The stages of knowledge presentation are considered on the basis of the typology of interdisciplinarity. It is argued that the stage of semantisation (conceptualization) of knowledge representation, when creating control systems, should be preceded by the stage of ontologization. It enhances the distinctiveness of knowledge representation. The stage of ontologization is necessary for the construction of more detailed explanatory constructions, due to the greater formalization of the ontological representation, in comparison with the stage of semantisation. It is assumed that the taxonomy stage can become the basis for the ontologization of knowledge representation, for example, in knowledge engineering.
Keywords: ethics of engagement | knowledge representation | typology of interdisciplinarity semantisation (conceptualization) | ontologization and taxonomization stages |
مقاله انگلیسی |
7 |
Ontology-augmented Prognostics and Health Management for shopfloor-synchronised joint maintenance and production management decisions
پیش آگهی و مدیریت سلامت با هستی شناسی تقویت شده برای تصمیمات مدیریت تولید و نگهداری مشترک هماهنگ شده با کف مغازه-2021 In smart factories, guaranteeing shopfloor-synchronised and real-time decision-making is essential to be
responsive to the ever-changing internal environment, namely the shopfloor of the production system and assets.
At operational level, decisions should balance counter acting objectives of maintenance and production; there-
fore, their decision-making processes should be joint and coordinated, to fulfil production requirements
considering the health state of the assets. The knowledge of the current state is promoted by the application of
Prognostics and Health Management (PHM) as an aid to support informed decision-making. Nevertheless, PHM-
purposed information is usually not complete in terms of production requirements. To support joint maintenance
and production management decisions, an ontological approach is proposed. The ontology, called ORMA
(Ontology for Reliability-centred MAintenance), has a modular structure, including formalisation of asset, pro-
cess, and product knowledge. Via suitable relationships, rules, and axioms, ORMA can infer product feasibility
based on the current health state of the assets and their functional units. ORMA is implemented in a Flexible
Manufacturing Line at a laboratory scale. Therein, an integrated solution, involving a health state detection
algorithm that interacts with the ontology, supports human decision-making via a web-based dashboard; joint
maintenance and production management decisions can be then taken, relying on diversified information pro-
vided by the PHM algorithm as well as the augmentation via ontology reasoning. The proposed ontology-based
solution represents a step towards reconfigurability of smart factories where human and automated decision-
making processes work in synergy. keywords: هستی شناسی | استدلال | پیشگویی و مدیریت بهداشت | phm | نگهداری | تولید | Ontology | Reasoning | Prognostics and health management | PHM | maintenance | production |
مقاله انگلیسی |
8 |
Knowledge reuse for ontology modelling in Maintenance and Industrial Asset Management
استفاده از دانش برای مدل سازی هستی شناسی در مدیریت نگهداری و مدیریت دارایی صنعتی-2021 Maintenance and Industrial Asset Management (AM) are fundamental business processes in guaranteeing the
availability of physical assets at minimum risk and cost, while balancing the interests of several stakeholders. To
reach operational excellence, intra- and inter-enterprise interoperability of systems is needed to support infor-
mation management and integration between several involved parties. To this end, ontology engineering is
relevant since it supports interoperability at technical and semantic levels. However, ontology modelling
methodologies are varied, and several best practices exist, amongst which knowledge reuse. Nevertheless,
reusing extant knowledge is not completely exploited so far, causing a heterogeneous ensemble of ontologies that
are not orchestrated. The present work aims at promoting the adoption of knowledge reuse for ontology
modelling in maintenance and AM. Therefore, an extensive review of existing ontologies for the two targeted
business processes is performed with a twofold objective: firstly, to realise a cross-industrial ontological com-
pendium, and secondly to understand the state of art of ontology modelling in maintenance and AM. To support
the adoption of knowledge reuse, this practice is framed in AMODO (Asset Management Ontology Development
methOdology). Finally, a laboratory-sized showcase is provided to prove the usefulness of relying on knowledge
reuse during the ontology development. The results show that the developed ontology is realised faster and is
inherently aligned with established ontologies, towards enterprise systems interoperability. Consequently,
maintenance and AM business processes may rely on information management and integration to pursue
operational excellence. keywords: هستی شناسی | استفاده مجدد از دانش | قابلیت همکاری | نگهداری | مدیریت دارایی | Ontology | Knowledge reuse | Interoperability | Maintenance | Asset management |
مقاله انگلیسی |
9 |
Ontology knowledge base combined with Bayesian networks for integrated corridor risk warning
پایگاه دانش هستی شناسی همراه با شبکه های بیزی برای هشدار خطر یکپارچه راهرو-2021 With the accelerated urbanization process, the emergence of urban underground integrated pipeline corridors
is the trend for cities, especially large and medium-sized cities. However, due to the complexity of the internal
system of the integrated corridor, there are various risks in the process of its construction and operation and
maintenance, and the risk factors are complex and diverse. In this paper, we introduce ontology technology and
knowledge base construction into the risk management of integrated pipeline corridor, build an ontology-based
knowledge base of integrated pipeline corridor risk, and construct a Bayesian network based on the established
risk knowledge base for risk evaluation of identified risk factors. The combination of ontology knowledge base
construction and Bayesian network method of integrated pipeline corridor risk makes the risk identification
system completer and more effective, and the method can effectively evaluate the disaster risk level of
integrated pipeline corridor operation and maintenance, which can meet the practical needs of integrated
pipeline corridor operation and maintenance risk management and disaster prevention and mitigation work.
Keywords: Integrated corridor | Risk warning | Ontology knowledge | Bayesian networks |
مقاله انگلیسی |
10 |
An ontology-based knowledge management approach supporting simulation-aided design for car crash simulation in the development phase
یک رویکرد مدیریت دانش مبتنی بر هستی شناسی حمایت از طراحی شبیه سازی کمک برای شبیه سازی تصادف اتومبیل در مرحله توسعه-2021 In the automotive industry, the design process is both costly and time-consuming. This research focuses
on improving the design process by mainly reducing time while producing more robust vehicles. Vehicle
development is based on simulation; thus, the design process is referred to as simulation-aided design.
Engineering design is highly collaborative and knowledge intensive. Therefore, knowledge management
plays a crucial role in today’s global economy and is essential for the competitiveness of companies.
However, current research on engineering knowledge management focuses on either the codification or
the personalisation approaches of knowledge management. Thus, this paper addresses an integrated and
collaborative approach. This paper aims to develop an ontology-based knowledge management approach
to support simulation-aided design, specifically car crash simulation. The knowledge management support system is designed to ensure the capture and retrieval of engineering knowledge and to enable
collaboration between different stakeholders. An evaluation of the models and technologies used is also
undertaken, based on use case scenarios.
keywords: مدیریت دانش | هستی شناسی | مدل دانش | بازیابی دانش | همکاری | طراحی مهندسی | شبیه سازی سقوط | Knowledge management | Ontology | Knowledge model | Knowledge retrieval | Collaboration | Engineering design | Crash simulation |
مقاله انگلیسی |