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Knowledge representation models that aim to present data in a structured and comprehensible manner have gained popularity as a research focus in the pursuit of achieving human-level intelligence. Humans possess the ability to understand, reason and interpret knowledge. They acquire knowledge through their experiences and utilize it to carry out various actions in the real world. Similarly, machines can also perform these tasks, a process known as knowledge representation and reasoning. In this survey, we present a thorough analysis of knowledge representation models and their crucial role in information management within the healthcare domain. We provide an overview of various models, including ontologies, first-order logic and rule-based systems. We classify four knowledge representation models based on their type, such as graphical, mathematical and other types. We compare these models based on four criteria: heterogeneity, interpretability, scalability and reasoning in order to determine the most suitable model that addresses healthcare challenges and achieves a high level of satisfaction. [ABSTRACT FROM AUTHOR]

Knowledge representation models that aim to present data in a structured and comprehensible manner have gained popularity as a research focus in the pursuit of achieving human-level intelligence. Humans possess the ability to understand, reason and interpret knowledge. They acquire knowledge through their experiences and utilize it to carry out various actions in the real world. Similarly, machines can also perform these tasks, a process known as knowledge representation and reasoning. In this survey, we present a thorough analysis of knowledge representation models and their crucial role in information management within the healthcare domain. We provide an overview of various models, including ontologies, first-order logic and rule-based systems. We classify four knowledge representation models based on their type, such as graphical, mathematical and other types. We compare these models based on four criteria: heterogeneity, interpretability, scalability and reasoning in order to determine the most suitable model that addresses healthcare challenges and achieves a high level of satisfaction.

Indoor fire is a sudden and frequent disaster that severely threatens the safety of indoor people worldwide. Indoor fire emergency evacuation is crucial to reducing losses involving various objects and complex relations. However, traditional studies only rely on numerical simulation, which cannot provide adequate support for decision-making in indoor fire scenarios. The knowledge graph is a knowledge base that can fully utilize massive heterogeneous data to form a sound knowledge system; however, it has not been effectively applied in the fire emergency domain. This study is a preliminary attempt to construct a knowledge graph for indoor fire emergency evacuation. We constructed the indoor fire domain ontology and proposed a four-tuple knowledge representation model. A knowledge graph was constructed with 1852 nodes and 2364 relations from 25 indoor fire events. The proposed method was tested for the case study of Henan Pingdingshan '5.25' Fire Accident in China. Results show that the proposed knowledge representation model and the corresponding knowledge graph can represent complicated indoor fire events and support indoor fire emergency evacuation. [ABSTRACT FROM AUTHOR]

This article shows that usually the automatic control system of autonomous flying robots based on unmanned aerial vehicles has limited computing resources, which makes it impossible to use known labor-intensive logical models of knowledge representation and processing to plan goal-directed behavior. Thus, there is a need to develop a model of knowledge representation and processing that makes it possible to plan goal-oriented behavior under conditions of a priori uncertainty of the problem environment with polynomial complexity. To solve this problem, a model of knowledge representation is constructed in the form of a set of typical basic, intermediate, and terminal growth elements used to automatically plan goal-seeking behavior in the space of subtasks in the form of a growing reduction network model for solving complex problems under uncertainty. Automatic goal-setting procedures are developed that allow an autonomous flying robot to secure its activities under a priori uncertainty in an unstable problem environment. [ABSTRACT FROM AUTHOR]

Indoor fire is a sudden and frequent disaster that severely threatens the safety of indoor people worldwide. Indoor fire emergency evacuation is crucial to reducing losses involving various objects and complex relations. However, traditional studies only rely on numerical simulation, which cannot provide adequate support for decision-making in indoor fire scenarios. The knowledge graph is a knowledge base that can fully utilize massive heterogeneous data to form a sound knowledge system; however, it has not been effectively applied in the fire emergency domain. This study is a preliminary attempt to construct a knowledge graph for indoor fire emergency evacuation. We constructed the indoor fire domain ontology and proposed a four-tuple knowledge representation model. A knowledge graph was constructed with 1852 nodes and 2364 relations from 25 indoor fire events. The proposed method was tested for the case study of Henan Pingdingshan ‘5.25’ Fire Accident in China. Results show that the proposed knowledge representation model and the corresponding knowledge graph can represent complicated indoor fire events and support indoor fire emergency evacuation.

We propose a principle of constructing procedures for planning the goal-directed behavior of autonomous intelligent mobile robots of various purposes under underdetermined unstable operating conditions. To build a knowledge representation model, its typical constructions have been developed in the form of implicative decision rules formed on the basis of polyvariable conditionally dependent predicates of various content. The structure of different-in-purpose predicates of this type, which can contain both polyvariables in the form of active fuzzy semantic networks and individual variables of the "object" and "event" types associated with certain conditions of the problem environment, is determined. It is shown that the use of active fuzzy semantic networks permits one to describe various situations and subsituations of the problem environment regardless of a specific subject area, as well as to determine in general terms the relationships that can be observed by an intelligent robot in the process of planning behavior between its objects in the problem environment and the events occurring in it. Knowledge processing tools have been developed at various stages of decision inference that allow constructing effective behavior planning procedures providing autonomous intelligent mobile robots with the ability to perform complex tasks formulated as a generalized description of the target situation of the problem environment. Upper bound estimates are found for the complexity of planning procedures for purposeful behavior by an autonomous intelligent mobile robot built according to the proposed principle of organizing tools for knowledge processing and decision inference. [ABSTRACT FROM AUTHOR]

Effective and efficient provision and reuse of knowledge across a knowledge network in the global value chain still faces two challenges, namely the interpretability of model and the efficacy of construction. This research aims to address these challenges by proposing a novel representation model for design knowledge. First, a knowledge representation model based on the integration of the cognitive process theory and the Requirement-Function-Behavior-Structure model (C-RFBS model) is proposed to incorporate key elements from the cognitive process of designers to capture the rationale of deliberation and the context of decision-making, which the knowledge records created become more interpretable. Second, knowledge graph is employed to improve the productivity of knowledge records creation, storage and exploration. On this basis, we describe the creation of knowledge records using the C-RFBS model as well as the computational framework and methods for storing knowledge using knowledge graph. The proposed model and methods are implemented in a knowledge retrieval system on which we have conducted a fork design case study to evaluate and demonstrate the models and methods. As shown in the evaluation, the proposed model can effectively support knowledge elicitation and achieved improved performance in terms of knowledge retrieval through incorporating knowledge graph.

The importance of the process of technical maintenance of boiler plants of thermal power plants of chemical production is substantiated. Difficulties in the implementation of technical maintenance of the specified type of equipment at the present stage are presented. The aim of the study is set, aimed at eliminating the existing difficulties. It is shown that in order to achieve this goal, it is necessary to solve the following tasks: analyze the process of maintenance of boiler plants and, based on the results of this analysis, develop a SADT-model of the process of their maintenance; develop models for representing knowledge about boiler plants and their structural elements; implement the developed models in a workflow system to automate the implementation of boiler maintenance procedures. According to the tasks set, as well as using a systematic approach, the analysis of the process of maintenance of boiler plants as an object of digitalization was carried out. The results of the analysis are given, which make it possible to conclude about the possibility of automating this process using a specially developed workflow system. Models for representing knowledge about boiler plants in the form of a SADT-model formalizing the relationship between maintenance procedures, as well as frames formalizing the relationship between the characteristics of equipment are presented. A brief description of the module of the workflow system is given, examples of its dialog boxes are given. [ABSTRACT FROM AUTHOR]

Effective and efficient provision and reuse of knowledge across a knowledge network in the global value chain still faces two challenges, namely the interpretability of model and the efficacy of construction. This research aims to address these challenges by proposing a novel representation model for design knowledge. First, a knowledge representation model based on the integration of the cognitive process theory and the Requirement-Function-Behavior-Structure model (C-RFBS model) is proposed to incorporate key elements from the cognitive process of designers to capture the rationale of deliberation and the context of decision-making, which the knowledge records created become more interpretable. Second, knowledge graph is employed to improve the productivity of knowledge records creation, storage and exploration. On this basis, we describe the creation of knowledge records using the C-RFBS model as well as the computational framework and methods for storing knowledge using knowledge graph. The proposed model and methods are implemented in a knowledge retrieval system on which we have conducted a fork design case study to evaluate and demonstrate the models and methods. As shown in the evaluation, the proposed model can effectively support knowledge elicitation and achieved improved performance in terms of knowledge retrieval through incorporating knowledge graph. [ABSTRACT FROM AUTHOR]

Parallel and pipeline methods for performing complex flight missions by groups of intelligent unmanned aerial vehicles (UAVs) are comparatively analyzed to find the most effective way for performing such missions under the criteria of minimum time and limited functionality of onboard computers. A model of knowledge representation for planning goal-oriented behavior in the a priori undescribed problem environment regardless of the specific domain is proposed. Procedures for knowledge processing and inference on semantic networks are developed for planning goal-oriented behavior of intelligent UAVs in the process of performing various subtasks of a complex flight mission with polynomial complexity. [ABSTRACT FROM AUTHOR]

Background. The need to automate the decision-making process on legal issues in various fields of human activity determines the importance of this work. The purpose of the article is to demonstrate a new approach to organizing and structuring legal knowledge using semantic networks. Materials and methods. The basic methods of knowledge representation are considered. Graph theory as a mathematical apparatus, as well as Text Mining methods for extracting information from text documents were used. Results. The advantages of semantic networks as a knowledge representation model are shown. An approach to the automated formation of a knowledge base based on legal text documents is presented. An example of its practical application is given. Conclusions. The described model opens up broad prospects for the automation of legal information processing. The considered approach can be used both for solving frequently encountered practical decision-making legal issues, and for the timeconsuming task of automating the regulatory legal acts examination.

A Materials Engineering Application (MEA) has been presented as a solution for the problems of materials design, solutions simulation, production and processing, and service evaluation. Large amounts of data are generated in the MEA distributed and heterogeneous environment. As the demand for intelligent engineering information applications increases, the challenge is to effectively organize these complex data and provide timely and accurate on-demand services. In this paper, based on the supporting environment of Open Cloud Services Architecture (OCSA) and Virtual DataSpace (VDS), a new semantic-driven knowledge representation model for MEA information is proposed. Faced with the MEA constantly changing user requirements, this model elaborates the semantic representation of data, services and their relationships to support the construction of domain knowledge ontology. Then, based on the ontology modeling in VDS, the semantic representations of association mapping, rule-based reasoning, and evolution tracking are analyzed to support MEA knowledge acquisition. Finally, an application example of knowledge representation in the field of materials engineering is given to illustrate the proposed model, and some experimental comparisons are discussed for evaluating and verifying the effectiveness of this method.

Interactions among people have substantially changed since the emergence of social networks, the expansion of the Internet and the proliferation of connected mobile devices, and so have the possibilities of collaborative learning, with the inclusion of new e-learning platforms. From this point, assessing human knowledge in these virtual environments is not a trivial task. This work presents a novel cloud-computing-based service which relies on advanced artificial intelligence mechanisms to infer knowledge and interest from users considering the aggregated data presented from/to these users in different social networks. This way it is possible to assess with a certain degree of confidence the user knowledge level in different topics as well as recommend additional specific education related to his/her former studies in order to get a better/desired job. [ABSTRACT FROM AUTHOR]

Design rationale (DR) explains why an artifact is designed the way it is. An explicit representation of DR is helpful to designers, allowing them to understand, improve, and reuse previous designs. The argumentation-based representation is the mainstream approach to DR representation. It has a semiformal graphical format to depict the structure of arguments for solving a design problem. This paper argues that because the design is not just a problem-solving process but also a cognitive activity that is continuously iterative and evolving, the conventional argumentation-based representation of DR has some inherent limitations. An improved, intent-driven representation model is proposed to capture and formalize the DR and its evolving history to support DR reuse. The model's knowledge structure, consisting of DR elements and their relationships, is detailed. A preliminary knowledge representation of the model based on Web Ontology Language is introduced. Furthermore, the context of DR is defined to document the complete DR and support effective traceability of design thinking. A graphical DR modeling system is developed, and an example is demonstrated to verify the system's application and the effectiveness of the proposed representation model. The paper provides an effective method to retain and manage a designer's implicit design knowledge, which has the potential to significantly improve the integrated management of product development knowledge. [ABSTRACT FROM AUTHOR]

The authors discuss the method of designing the distance learning didactic materials for the asynchronous mode. The approach is based on the modular structure of the fundamental knowledge. The new role of the teacher is being discussed as well as its impact on the didactic materials’ structure and content. The paper introduces the knowledge representation model designed to meet the requirements of theoretical knowledge presentation in a selected domain using the Learning Object concept. First Published Online: 14 Oct 2010

The paper deals with the problems that arise while developing software of such distributed systems as real-time, computer network protocols and distributed information-processing systems. In the introduction, some essential peculiarities typical of distributed systems (DS) and a general scheme for developing such systems are presented. The main idea of this paper is the use of a piece-linear aggregate mathematical method for formal specification, simulation and validation of DS. The main advantage of this approach is that it integrates two tasks of differing nature, i.e. simulation and correctness analysis of specification, on the basis of a single specification. The method of a transformation of aggregate specification to the first-order predicates and its further use for the correctness analysis of the aggregate specification using logic-based programming will also be presented. It will also be shown how to transform an informal description of system functioning to aggregate specification using production rules. The presented methods are explained using illustrative examples. [ABSTRACT FROM AUTHOR]

Typhoon events can cause serious environmental damage and economic losses. Understanding the development of typhoon events will provide valuable knowledge for disaster prevention and mitigation. In the age of big data, the sharp contrast between the sudden increase of mass information and the lack of a knowledge appreciation mechanism appears. There is an urgent need to promote the transformation of information services to knowledge services in the field of hazard management. Knowledge representation, as a strategy for symbolizing and formalizing knowledge, affects knowledge acquisition, storage, management, and application, and is the basis and prerequisite for the implementation of knowledge services. Based on the evolution law of typhoon events and human cognitive habits, a formal knowledge representation method for typhoon events (TKRM) is proposed in this paper. First, by analyzing the evolution characteristics of typhoon events, the TKRM framework with three layers consisting of "event–process–state" was constructed, which was used to describe the knowledge composition and relationship of the different granularity of typhoon events. Second, the formal representation of the TKRM framework was formed by using a finite state machine (FSM) as a reference, taking time and location as the basic conditions, and extending the hierarchical and parallel representation mechanism. Finally, the rationality and practical value of the TKRM were verified using a case study. [ABSTRACT FROM AUTHOR]