Development Of Knowledge-based System Research Articles

Investigating obstacles to the development of rural knowledge-based and technological systems in the agricultural sector of Sistan Region under the Decision Support System (DSS) approach

Investigating obstacles to the development of rural knowledge-based and technological systems in the agricultural sector of Sistan Region under the Decision Support System (DSS) approach

РОЗРОБКА ОНТОЛОГІЧНОЇ МОДЕЛІ БАЗИ ЗНАНЬ «БІБЛІОТЕКА» НА БАЗІ СЕРЕДОВИЩА PROTÉGÉ

Modern development of society, increasing the degree and pace of integration of information technology achievements into the sphere of human life, traditional approaches to the construction of information systems cease to be effective or become too cumbersome. The development and use of knowledge-based systems is one way to solve this problem. The material of the work is devoted to the ontological modeling of the new subject area «Library». Ontology is considered in the context of knowledge sharing. The created ontological model of the «Library» knowledge base is quite modern and relevant today. The developed ontological model of the knowledge base in this field can be implemented on thematic web resources and will significantly facilitate the semantic search of information within the subject area in comparison with the existing ones. The terminological dictionary of this subject area is defined using the term system. An analysis of the capabilities of the Protégé ontology development environment for modeling the specific subject area «Library» was carried out. The basic principle of ontology modeling in the form of a semantic network was chosen. The proposed network has the opportunity to expand and deepen knowledge about the subject area «Library». Using the Protégé environment for the implementation of the ontological model of the knowledge base made it possible to use the advantages and features of the created «Library» model, such as: structuredness, completeness, reflexivity, functionality, transitivity, reliability and consistency of information. The criterion for assessing the correctness of the ontological model of the knowledge base was selected and used. Testing of the developed ontological knowledge base was conducted and a fairly high level of its correctness in the process of information search was confirmed. The average indicator (Average) according to the SUM metric, determined for all users, is equal to 83.25%, which is an acceptable indicator of the ontological knowledge base. When using the classic relational model of database organization to implement the «Library» database. the average SUM metric for 10 users is 75.26%. An example of the developed ontology in Protégé is considered, a graphical representation of the basic graph of the ontological model of the «Library» knowledge base is given, the model includes 10 classes and subclasses, 2 properties-relations and up to 10 properties-data are defined for each class and subclass, an example of the classes of the ontological model is given «Library», an example of the "property-relations" of the ontological model «Library» is given, an example of the "property-data" of the ontological model «Library» is given. Possible directions of further development of the ontological model «Library» have been determined.

Development and preliminary testing of a prior knowledge-based visual navigation system for cardiac ultrasound scanning.

Ultrasound is widely used to diagnose disease and guide surgery because it is versatile, inexpensive and radiation-free. However, image acquisition is dependent on the operation of a professional sonographer, which is a difficult skill to learn for a wider range of non-sonographers. We propose a prior knowledge-based visual navigation method to obtain three important standard ultrasound views of the heart, based on the sonographer's skill learning and augmented reality prompts. The key information about the probe movement was captured using vision-based tracking and normalisation methods on 14 volunteers, based on a professional sonographer's practice. An augmented reality-based navigation method was then proposed to guide operators with no ultrasound experience to find standard views of the heart in a second set of three volunteers. Through quantitative analysis and qualitative scoring, the results showed that the proposed method can effectively guide non-sonographers to obtain standard views with diagnostic value. It is believed that the method proposed in this paper has clear application value in primary care, and expansion of the data will allow the accuracy of the navigation to be further improved.

Research and Design of Planning Systems in the AORBCO Model

Abstract Planning research in artificial intelligence began earlier, dating back to 1969, and the general problem solver (GPS) designed by Newell and Simon [1]. Since then, other researchers have also proposed other planners, such as QA3 system using theorem proving method to construct planning[2], Fikes and Nilsson designed STRIPS planning system [3], until today STRIPS planning method is still only a very important research method in the field of planning. After more than 50 years of research, there have been many intelligent planning methods such as graph planning method, probability planning, timing planning, heuristic planning and perceptual planning. Although the existing intelligent planning methods have been able to solve some problems, the existing planning mainly focuses on the design of the planning algorithm, and the manual analysis preprocessing workload is large. Research has pointed out that the new generation of intelligent planners should be knowledge-based development of knowledge-based planning systems is a meaningful work [4]. Therefore, by analyzing the existing intelligent planning methods and AORBCO model, this paper gives the definition of intelligent planning with self-consciousness, and proposes an intelligent planning method based on self-consciousness based on behavior control.

Can knowledge based systems be designed to counteract deskilling effects?

Recent calls in the information systems research community argue that we know intelligent systems deskill users, and future research should focus on how to design systems that do not deskill, rather than continue to examine whether the phenomenon occurs. This should be a wakeup call for public accounting firms focused on implementing restrictive audit support systems, which leads to de-skilling of novice accounting professionals. Our research focuses on redesigning knowledge-based systems to facilitate expertise development and counteract the de-skilling effects that result from use of such systems. Specifically, we manipulate the design of the system interface by providing information cues in a screen format consistent with expert knowledge representations and manipulate automatic provision versus voluntary use of explanations for users during task completion. Results show that after using the knowledge-based system to complete a series of reenacted client engagements over a three-day period, both the interface design manipulation and automatic provision of explanations had a positive effect on novice accounting professionals’ development of expert-like knowledge structures. The results of the study have important implications for the development of knowledge-based systems intended to support accounting professionals’ (and other knowledge workers’) expertise development processes.

The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease.

Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.

Indicators and Components for Developing a Model of Anti-Corruption Policy in Iran (With an Electronic Government Approach)

Objective: The model development phase is one of the most crucial stages in the policy-making process. This research aimed to identify the indicators and components for developing a model of anti-corruption policy in Iran, focusing on the implementation of electronic government. Method: This study was qualitative and applied in nature. It utilized the viewpoints of 20 experts related to the topic (from the academic community in the field of policy-making, who held Master's and Doctoral degrees and had sufficient experience and knowledge and were considered authoritative). To determine the sample size, purposive sampling and theoretical saturation rule were used. The research tool was semi-structured interviews, the validity of which was verified through face validity and reliability through the agreement coefficient among coders. Thematic analysis was used for analyzing the findings. Findings: The findings revealed the existence of five basic themes (development of electronic and knowledge-based administrative systems; leveraging information technology and advanced administrative automation in the administrative system; consolidation of results and information from supervisory organizations and centers in an independent organization; the presence of appropriate collaboration and information exchange between organizations and supervisory centers; and enhancing transparency, speed, accuracy, and completeness of organizational information bases), two organizing themes (information and communication technology and integration of information systems), and an overarching theme (electronic government) for developing an anti-corruption policy model. Conclusion: It can be concluded that combating administrative corruption is a complex issue that can be improved in terms of the quantity and quality of transparency and accountability of administrative duties through the development of electronic government.

Development of knowledge-based clinical decision support system for the management of cardiovascular diseases

Abstract Background Knowledge-based clinical decision support systems (CDSS) are technological tools that analyze patient information and present justified diagnostic and therapeutic suggestions to the user. They improve adherence to evidence-based medicine and reduce medical errors. Material and methods We have developed an advanced CDSS with the following components: 1. Knowledge Management System, which allows the introduction of interoperable clinical variables organized in taxonomies, and the generation, validation and direct maintenance of decision rules by experts doctors. 2. Smart Assistant for Medical Reports Generation, which facilitates user interaction for the introduction of clinical data (natural language and/or data entry forms) and decission making supported by real-time suggestions that are automatically generated by 3. Inference Engine. Rules and variables derived from the most relevant cardiovascular Clinical Practice Guidelines (CPGs) are introduced by cardiologists into the system. The rules undergo a process of peer review and internal and external validation with anonymized clinical cases. Results More than 2,000 clinical variables and more than 5,000 rules corresponding to more than 80 CPGs and consensuns papers have been created and validated. An internal and external pilot validation of the system has been successfully carried out for the main cardiovascular problems, such as heart failure, atrial fibrillation, valvulopathies, coronary syndromes, cardiomyopathies, hypertension, diabetes and dyslipidaemias, using more than 400 cases. Conclusion Our CDSS is capable of providing real-time diagnostic and therapeutic recommendations for complex scenarios in cardiovascular disease, combining thousands of variables and decision rules in a single system. Prospective evaluation of the system in real clinical environments, certification and integration in Electronic Medical Records for clinical use is required. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): DILEMMA solutions, SL

An Ontological Approach to Information Integration in Data Intensive Domains

The development and support of knowledge-based systems for experts in the field of social network analysis (SNA) is complicated because of the problems of viability maintenance that inevitably emerge in data intensive domains. Largely this is the case due to the properties of semi-structured objects and processes that are analyzed by data specialists using data mining techniques and others automated analytical tools. Firstly, new sources (e. g. online social networks, published databases) constantly become available for gathering, analyzing, and interpreting data. Thus, new sources should be modelled and embedded in existing data structures maintaining logical consistency. Secondly, new techniques and underlying algorithms are also constantly being developed. Therefore, analysis results should be integrated with source data, and metamodels that describe the integration should be adaptable and extensible. Thirdly, the dynamism of semi-structured objects entails constant changes in knowledge models produced by domain experts and knowledge engineers. Considering that the same data could be used by different domain experts it is crucial not only to support traceability of changes in models but also to ensure independence of expert interpretation of these models. The analysis of existing approaches to information integration shows lack of solutions implementing traceability of changes. This paper introduces a novel approach to information integration based on ontological and production knowledge models to fill this gap. A conceptual description of the approach and an underlying set-theoretical model are given. The main difference of the given approach from the existing ones is the uniformity to the integration of different kinds of ontologies as well as different versions of ontologies using rule-based model of ontological mappings, which is demonstrated by the example of solving the special case of the problem of identifying key users (so-called bridges) in social networks.

Development of knowledge-based clinical decision support system for the management of cardiovascular diseases

Abstract Background Knowledge-based clinical decision support systems (CDSS) are technological tools that analyze patient information and present justified diagnostic and therapeutic suggestions to the user. They improve adherence to evidence-based medicine and reduce medical errors. Material and methods We have developed an advanced CDSS with the following components: 1. Knowledge Management System, which allows the introduction of interoperable clinical variables organized in taxonomies, and the generation, validation and direct maintenance of decision rules by experts doctors. 2. Smart Assistant for Medical Reports Generation, which facilitates user interaction for the introduction of clinical data (natural language and/or data entry forms) and decission making supported by real-time suggestions that are automatically generated by 3. Inference Engine. Rules and variables derived from the most relevant cardiovascular Clinical Practice Guidelines (CPGs) are introduced by cardiologists into the system. The rules undergo a process of peer review and internal and external validation with anonymized clinical cases. Results More than 2,000 clinical variables and more than 5,000 rules corresponding to more than 80 CPGs and consensuns papers have been created and validated. An internal and external pilot validation of the system has been successfully carried out for the main cardiovascular problems, such as heart failure, atrial fibrillation, valvulopathies, coronary syndromes, cardiomyopathies, hypertension, diabetes and dyslipidaemias, using more than 400 cases. Conclusion Our CDSS is capable of providing real-time diagnostic and therapeutic recommendations for complex scenarios in cardiovascular disease, combining thousands of variables and decision rules in a single system. Prospective evaluation of the system in real clinical environments, certification and integration in Electronic Medical Records for clinical use is required. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): DILEMMA solutions, SL

Parameterisation of domain knowledge for rapid and iterative prototyping of knowledge-based systems

In critical infrastructure applications, timely and consistent fault detection and diagnosis is an increasingly important operational process, especially in the energy sector where safety is of the utmost importance. To realise this, engineers have to manually analyse data acquired from several assets using predefined diagnostic processes, but this is a time-consuming process requiring significant amounts of specialist expert knowledge. Data-driven approaches to support fault detection and diagnosis, and other similar problems, can produce accurate results comparable to what the engineers can achieve in a fraction of the time. However, the majority of these data-driven techniques are black box techniques and lack explainability which is often necessary for explaining decisions about critical assets in the power generation industry. Knowledge-based systems, such as rule-based expert systems have been shown to provide not only accurate decisions but also the explanation and reasoning behind these decisions in some related applications. However, there is a significant time cost associated with the development of knowledge-based systems, and in particular with the knowledge elicitation process, where the domain expert’s knowledge is formalised and is encoded into the system. This challenge is commonly referred to as the knowledge elicitation bottleneck.In this paper, we present a novel approach to performing the knowledge elicitation using a set of symbolic primitives (rise, fall, fluctuate, and stable) to parameterise typical time-series condition monitoring data. The knowledge is represented by using a common language that can easily be communicated with (and from) the domain experts. This allows for the quick and accurate elicitation of the domain experts knowledge, but also the formalisation and implementation of the knowledge into a rapidly produced diagnostic system. Further to this, due to the parametrisation of the knowledge, it is possible to iteratively improve the knowledge base by updating these parameters based on new unseen data. This approach was applied to the Tennessee Eastman dataset, which is simulated data of a real-world industrial process. It was found that by using this approach it was possible to accurately and quickly capture the knowledge required to detect several faults within the case study dataset, but also provided fully explained reasons why each fault was detected by relating the explanations to the symbolic primitives previously defined.

PROTOTYPE DEVELOPMENT OF KNOWLEDGE-BASED SYSTEM FOR LOW-VOLUME RURAL ROADS IN SARAWAK

The value of rural roads cannot be overstated in terms of a society’s economic and social development. Road construction has the potential to alter natural terrain, cause widespread environmental damage and result in significant land-use and cultural changes. Roads must be constructed in accordance with appropriate design specifications in order to balance the needs, costs and environmental impacts of road users. There are several reasons for scarcity in technical skills and technological development of Low-Volume Rural Roads (LVRRs). Universities, colleges, polytechnics and technical schools do not provide LVRR-focused education. Low-tech LVRRs skills are often acquired through years of experience and are lost due to retirement and downsizing. This article discusses the development of a conceptual knowledge-based system for low-volume rural road design. The prototype system has been developed for demonstration purposes and is not ready for implementation. This prototype system simulates a portion of a pavement expert’s or specialist’s technical reasoning skills, which it can then use to help with road design and improve the efficiency and precision of engineers tasked with evaluating all potential designs. As a result, the system increases the engineers’ efficiency in evaluating, assessing and modifying information that will aid decision makers throughout the project, thereby increasing the effectiveness of making the right decision at the right time.

Development of knowledge-based clinical decision support system for patients included in colorectal screening program

Colorectal (CRC) screening programs represent a large volume of procedures that need a follow-up endoscopy. A knowledge-based clinical decision support system (K-CDSS) is a technology which contains clinical rules and associations of compiled data that assist with clinical decision-making tasks. We develop a K-CDSS for management of patients included in CRC screening and surveillance of colorectal polyps. We collected information on 48 variables from hospital colonoscopy records. Using DILEMMA Solutions Platform © ( https://www.dilemasolution.com ) we designed a prototype K-CDSS (PoliCare CDSS), to provide tailored recommendations by combining patients data and current guidelines recommendations. The accuracy of rules was verified using four scenarios (normal colonoscopy, lesions different than polyps, non-advanced adenomas and advanced adenomas). We studied the degree of agreement between the clinical assessments made by expert doctors and nurses equipped with PoliCare CDSS. Two experts confirmed a correlation between guidelines and PoliCare recommendations. 56 consecutive endoscopy cases from colorectal screening program were included (62.8 years; range 53-71). Colonoscopy results were: absence of colon lesions ( n = 7, 12.5%), lesions in the colon that are not polyps ( n = 3, 5.4%) and resected colonic polyps ( n = 46, 82.1%; 100% R0 resection). Patients with resected polyps presented non-advanced adenoma ( n = 21, 45.6%) or advanced lesions ( n = 25, 54.4%). There were no differences in erroneous orders with PoliCare CDSS (Kappa value 1.0). PoliCare CDSS can easily be integrated into the workflow for improving the overall efficiency and better adherence to evidence-based guidelines. Los programas de cribado de cáncer colorrectal (CCR) generan un gran número de colonoscopias de seguimiento. Un sistema de soporte a la decisión clínica basado en el conocimiento (K-CDSS) es una tecnología que contiene reglas clínicas y asociaciones de datos que ayudan en la tarea de toma de decisiones clínicas. El objetivo fue desarrollar un K-CDSS para el manejo de los pacientes de cribado de CCR, y evaluar su eficacia. Recolectamos información de 48 variables de registros de colonoscopia. Mediante el software DILEMMA ( https://www.dilemasolution.com ) diseñamos un prototipo de K-CDSS (PoliCare CDSS), para proporcionar recomendaciones personalizadas, combinando los datos de los pacientes y las recomendaciones de las guías actuales. La exactitud de las reglas se verificó mediante cuatro escenarios (colonoscopia normal, lesiones diferentes a pólipos, adenomas no avanzados y adenomas avanzados). Se estudió el grado de concordancia entre las valoraciones clínicas realizadas por médicos expertos y enfermeros equipados con PoliCare CDSS. Dos expertos confirmaron una correlación entre las pautas y las recomendaciones de PoliCare. Se incluyeron 56 casos consecutivos del programa de cribado (62,8 años; rango 53-71). Los resultados de la colonoscopia fueron: ausencia de lesiones de colon (n = 7, 12,5%), lesiones en el colon que no son pólipos (n = 3, 5,4%) y pólipos de colon resecados (n = 46, 82,1%; resección R0 del 100%). Los pacientes con pólipos resecados presentaron adenoma no avanzado (n = 21, 45,6%) o lesiones avanzadas (n = 25, 54,4%). No hubo diferencias en recomendaciones erróneas con PoliCare CDSS (valor Kappa 1.0). PoliCare CDSS se puede integrar fácilmente en el flujo de trabajo de una unidad de endoscopia digestiva.

Design Patterns for a Knowledge-Driven Analytical Platform

The development and support of knowledge-based systems for experts in the field of social network analysis (SNA) is complicated because of the problems of viability maintenance that inevitably emerge in data intensive domains. Largely this is the case due to the properties of semi-structured objects and processes that are analyzed by data specialists using data mining techniques and others automated analytical tools. In order to be viable a modern knowledge-based analytical platform should be able to integrate heterogeneous information, present it to users in an understandable way and to support tools for functionality extensibility. In this paper we introduce an ontological approach to information integration and propose design patterns for developing analytical platform core functionality such as ontology repository management, domain-specific languages (DSLs) generation and source code round-trip synchronization with DSL-models.

Development of Knowledge-Based Engineering System for Structural Size Optimization of External Fixation Device

The development process of the knowledge-based engineering (KBE) system for the structural size optimization of external fixation device is presented in this paper. The system is based on algorithms for generative modeling, finite element model (FEM) analysis, and size optimization. All these algorithms are integrated into the CAD/CAM/CAE system CATIA. The initial CAD/FEM model of external fixation device is verified using experimental verification on the real design. Experimental testing is done for axial pressure. Axial stress and displacements are measured using tensometric analysis equipment. The proximal bone segment displacements were monitored by a displacement transducer, while the loading was controlled by a force transducer. Iterative hybrid optimization algorithm is developed by integration of global algorithm, based on the simulated annealing (SA) method and a local algorithm based on the conjugate gradient (CG) method. The cost function of size optimization is the minimization of the design volume. Constrains are given in a form of clinical interfragmentary displacement constrains, at the point of fracture and maximum allowed stresses for the material of the external fixation device. Optimization variables are chosen as design parameters of the external fixation device. The optimized model of external fixation device has smaller mass, better stress distribution, and smaller interfragmentary displacement, in correlation with the initial model.

Digitalization of Arctic shipping along the Northern Sea Route

The proportion of scientific papers devoted to studying and scientifically substantiating climate change and ice melting in northern latitudes has been steadily increasing in recent years. Of particular interest to our country are researches studying climate change in the Arctic and the consequences of these changes. One of the effects of climate change is the retreat of Arctic ice to the North. Thereby, it becomes possible to more effectively use the Northern Sea Route. The Northern Sea Route is very promising both economically and strategically. Therefore, its development and active use is an important task for the state. However, there are a large variety of obstacles and problems related to the effective development of the Northern Sea Route. One of the most important is the underdevelopment of transport and logistics infrastructure, as well as insufficient digitalization of the basic services required for safe navigation in the difficult conditions of the Arctic zone of the Russian Federation. In this context, the digitalization of Arctic shipping along the Northern Sea Route is an urgent task. The aim of the study is to develop approaches, methods and tools to ensure the digital transformation of Arctic shipping. Such infrastructure elements as Arctic ports, as well as services for ice and pilotage of vessels are considered separately. As the main research methods, we selected system analysis, conceptual modeling, ontological modeling and engineering. Two ontologies are presented as scientific and practical results obtained in the work: the ontology of the Arctic seaports of Russia and the ontology of shipping support in the waters of the Northern Sea Route. The paper also considers several promising directions for the development of the results obtained. One of these directions is related to the digitization of the most important components of the transport and logistics system for shipping along the northern coasts of the Russian Federation. This will provide better access to maritime transport infrastructure along the Arctic routes through the use of a single digital platform. We believe that the development of knowledge-based decision-making systems and machine learning approaches for maritime logistics management in the Arctic is a promising area for further research.

Development of knowledge-based clinical decision support system for patients included in colorectal screening program

Background & aimsColorectal (CRC) screening programs represent a large volume of procedures that need a follow-up endoscopy. A knowledge-based clinical decision support system (K-CDSS) is a technology which contains clinical rules and associations of compiled data that assist with clinical decision-making tasks. We develop a K-CDSS for management of patients included in CRC screening and surveillance of colorectal polyps. MethodsWe collected information on 48 variables from hospital colonoscopy records. Using DILEMMA Solutions Platform © (https://www.dilemasolution.com) we designed a prototype K-CDSS (PoliCare CDSS), to provide tailored recommendations by combining patients data and current guidelines recommendations. The accuracy of rules was verified using four scenarios (normal colonoscopy, lesions different than polyps, non-advanced adenomas and advanced adenomas). We studied the degree of agreement between the clinical assessments made by expert doctors and nurses equipped with PoliCare CDSS. Two experts confirmed a correlation between guidelines and PoliCare recommendations. Results56 consecutive endoscopy cases from colorectal screening program were included (62.8 years; range 53-71). Colonoscopy results were: absence of colon lesions (n=7, 12.5%), lesions in the colon that are not polyps (n=3, 5.4%) and resected colonic polyps (n=46, 82.1%; 100% R0 resection). Patients with resected polyps presented non-advanced adenoma (n=21, 45.6%) or advanced lesions (n=25, 54.4%). There were no differences in erroneous orders with PoliCare CDSS (Kappa value 1.0). ConclusionsPoliCare CDSS can easily be integrated into the workflow for improving the overall efficiency and better adherence to evidence-based guidelines.

Overcoming Concreting Problems of Rigid Pavements using Knowledge-Based System

Highway engineers encounter numerous problems during rigid pavement concreting. Thus, deciding on appropriate controlling measures is a crucial task. These problems affect the quality of pavements and increase initial cost. Experts can control them, but novices cannot. Therefore, this study describes the development of a novel knowledge-based system to control concreting problems of rigid pavements. The knowledge that includes problems encountered in the domain, their causes, preventive actions, solutions, and their effects was acquired from the experts by different methods and was presented in a classified format. The calcified knowledge was transferred into rules and coded in software by using Visual Basic. The system was tested by different users involved in highway engineering, including experts and novice engineers. The mean values of the overall system evaluation by the four types of users based on the 5-point Likert scale were 4 and 4.5 respectively. The developed system can be used to help highway engineers overcome the problems during concreting of rigid highway pavements, identify their causes, preventive actions, and effects on the construction process. It can also be used to help the experts formulate decisions quickly. The system facilitates decision making of domain experts by reducing unnecessary inputs, performing quick calculations, and providing classified recommendations. In addition, it can be used as an instructional tool for pavement engineers or highway engineering students, to interchange the expertise among the experts, and to transfer expertise to the next generation of engineers.

Four Independent Knowledge Domains to Enable an Agile, Distributed Development of User-Centred Engineering Configurators

Robot-centric automation solutions (RAS) promise greater efficiency and consistent quality in production, relieving workers of physically demanding and dangerous tasks, especially in the times of COVID-19. Nevertheless, due to their relatively high complexity and implementation costs, RAS are only used to a limited extent by small and medium-sized manufacturing companies. As a rule, the high costs of RAS arise from custom engineering efforts, which take up to 70 percent of the acquisition costs. For this reason, it is necessary to optimise the engineering of RAS. However, software tools such as configurators have been used primarily for the individualisation of products, such as automobiles or clothing, based on variants predefined by the manufacturer, and less for the engineering of automation solutions. The development of knowledge-based systems, in particular knowledge-based engineering configurators (EC), is usually performed by few proficient experts with high development effort. One of the primary challenges in the knowledge acquisition is that several experts possess partial aspects of knowledge in an inhomogeneous, implicit form. Furthermore, there is a lack of efficient development methods for EC. By reusing knowledge elements from previous development projects, a sustainable increase in efficiency is possible. In order to enable an efficient development process of EC, we introduce a structuring model consisting of four knowledge domains (KD): knowledge about specific business cases (KD1), Best Practices as case-specific solution knowledge (KD2), logical expert knowledge (KD3) as well as semantically consistent data models for interoperability of different IT systems (KD4). As the four KD are independent, their development can be agilely divided among several teams or companies. Finally, the agile development approach is validated individually for each KD as well as comprehensively within the scope of the ROBOTOP platform for planning RAS.

Semantic interoperability in the internet of things - state-of-the-art and prospects

Internet of things (IoT) is generally referred to as an internet world of little things with limited storage and processing ability, but facing communication, security, privacy, and interoperability issues. Further, device, network, platform, syntactic, and semantic are the major interoperability issues in the internet of things. Smart objects, termed as interconnected devices, generate a huge chunk of heterogeneous data and its processing, as well as usage, becomes the challenge in IoT. Therefore, this heterogeneous data is semantically annotated using linked vocabularies to make it interoperable information. The main aim of this article is to have a deep understanding of semantic interoperability that will help in sharing heterogeneous data through the development of knowledge-based systems or common ontologies.