Knowledge Modeling Approach Research Articles

Knowledge and data fusion-driven dynamical modeling approach for structures with hysteresis-affected uncertain boundaries

Knowledge and data fusion-driven dynamical modeling approach for structures with hysteresis-affected uncertain boundaries

Semantic-based assembly framework for the generation of travel demand

This work applies a knowledge modelling approach in the design of a framework for the generation of travel demand for traffic simulation applications. The proposed framework is based on interchangeable modules that integrate the main stages of travel demand modelling supported by an engineered knowledge base. This approach is intended to promote greater behavioural diversity, incorporate more diverse contextual data, facilitate access to online datasets, and support users to undertake and validate investigations across a range of models and implementations. The framework provides the user with direct control to modify the schema, control the selection of data, select alternative modules to execute, and the potential to remotely retrieve data and execute modules. The framework is investigated through a prototype, which generated travel demand across a full day and performed simulation utilising two third-party traffic simulators based on the configuration by the user schema. The problem of travel demand generation was separated into discrete task modules with identification of features for alternative design or further modularisation. The prototype evaluation generated multimode travel demand that was successfully tested on third-party traffic simulators and evidenced the fitness of semantic technologies in building simulator-agnostic interchangeable framework modules that satisfy the need for configurable travel modelling. The findings of the paper also contribute to the understanding of the challenges in utilising Semantic Web technologies for implementing travel demand generation. It is proposed that the framework provides a basis to develop new and existing approaches to travel demand generation to improve modelling outcomes and adoption.

Managing Complex Knowledge in Sustainable Planning: A Semantic-Based Model for Multiagent Water-Related Concepts

The concepts of green infrastructures, nature-based solutions and ecosystem services are today considered an integral part of the broader theme of the urban bioregion, with an intrinsic character of complexity. It is certainly difficult to structure bioregional processes in a balanced and sustainable way, able to keep local energy production and consumption cycles closed. It is a complex issue of knowledge bases, and problems are increased by the participatory dimension of environmental planning. In fact, when rational planning models have failed in the face of prominent individual needs and environmental complexity, a path has emerged towards the inclusion of multiple citizens’ and stakeholders’ knowledge. The cognitive structure of the plans has thus changed from systems of exclusively expert, formal knowledge to systems of diffused, multi-agent knowledge. This has involved richness but also significant problems in understanding and managing knowledge bases. In this complexity, there are some common peculiarities when it comes to socio-environmental systems. A common feature of the reference domains of ecosystem services, nature-based solutions and green infrastructures is the water resource. A management model of hydrological data, which are structurally relevant and cross-sectoral in environmental planning actions, could represent a flagship initiative. The used approach could be conveyed to more complex and extensive areas of the environmental domain in a perspective of sustainable planning. The present paper is part of a research work oriented toward handling complex environmental subjects, such as green infrastructures, nature-based solutions or ecosystem services, with a knowledge modelling approach. This approach is based on semantic extensions, elaborated form the concept of semantic web, to allow shared interpretations of knowledge coming from different languages and scientific domains. It is also based on using applied ontologies, elaborated from the concept of ontology-based classification, to support a structured organization of knowledge contents. The main research objective is therefore to investigate about a knowledge management system with semantic extensions, populated with hydrological knowledge contents, as well as to propose a preliminary functional architecture. A simple ontology of data is extracted, aiming at clarifying and improving inter-domain communication, so as to enhance a common semantic understanding in a complex environmental system.

Banking System Incidents Analysis Using Knowledge Graph

Risk incidents in the banks' systems have caused significant social impacts and economic losses. This study proposes a risk incident knowledge modeling and analysis approach based on the knowledge graphs to realize the effective integration and continuous accumulation of incident knowledge. The authors are the first to analyze the advantages of knowledge graphs in risk incident knowledge integration for the bank's core system. Moreover, they study and compare the related field's state-of-the-art models (including CRF, BiLSTM, BiLSTM-CRF, BERT-BiLSTM-CRF). This paper proposes an improved Bert-BiLSTM-CRF model to perform entity recognition which replaces “individual word mask and training” with “full word mask and training” targeted to solve the problem of low accuracy in the extraction of incident text entities in the banking system. Experiments on 1000 banking system incident material show that the improved Bert-BiLSTM-CRF model outperforms the state-of-the-art models based on the comparison of recall (R), precision (P), and F1-measure, with a 2%-9% improvement in the F1-measure.

Causal Knowledge Modelling for Agile Development of Enterprise Application Systems

Experience shows that Agile project management tools such as Atlassian Jira capture the state of EAS projects by relying solely on expert judgement that is not supported by any knowledge model. Therefore, the assessment of project content against strategic objectives and business domain features are not supported by any tool. This is one of the reasons why Agile project management still does not provide sufficient EAS project delivery results. In order to address this problem, the Enterprise Application Software (EAS) development using Agile project management is summarized in a conceptual model. The model highlights the knowledge used and indicates its nature (empirical or causal digitized). The modified Agile management process we have developed and described in previous works is based on causal knowledge models that supports EAS development and Agile management processes. The purpose of this article is to specify knowledge repository to ensure the Agile management solutions of an EAS project are aligned with strategic goals and business domain causality. It is worth noticing that strategic goals have been identified and specified as capabilities using some enterprise architecture framework (NAF, MODAF, ArchiMate, etc.). The novelty of the proposed method is incorporating the business domain causal knowledge modelling approach into the Agile project management process. The causal knowledge unit is considered as a Management Transaction (MT), which includes closed loop dependence of its components. The modified Agile activity hierarchy (theme, initiative, epic, user story) defines the required content of their mutual interactions. An important new results obtained are the conceptual model of causal knowledge base (KB) and specification of enhanced Agile management tool components: project management database and project state assesment knowledge base. Causal KB includes specification of causal knowledge unit (MT metamodel) and specifications of traditional and causal Agile hierarchy meta-models. These conceptual models define the causal knowledge components necessary to evaluate the state of Agile activities in the EAS development project using intelligent Agile project management tool.

Architecture and knowledge modelling for self-organized reconfiguration management of cyber-physical production systems

ABSTRACT The demand for reconfigurations of production systems is increasing, driven by shorter innovation and product life cycles and economic volatility. Another trend in the domain of industrial automation is the emergence of cyber-physical production systems, which offer promising potentials, for example, self-organization capabilities. A suitable cyber-physical production system architecture that incorporates knowledge modelling and management concerns, plus a reconfiguration management methodology, is crucial for realizing self-organized reconfiguration management. In this paper, first reference architectures, architectural patterns, and basic principles, as well as knowledge modelling and management approaches, are discussed in general. Afterwards, these are examined concerning the reconfiguration management use case focusing on UML/XML-based and ontology-based approaches. A novel approach comprising a multi-agent system and the MAPE-K concept for reconfiguration management is presented. In addition, the approach contains a service-oriented architecture for a deterministic plant control within a layered architecture. The knowledge modelling is realized through a UML information model, which can be integrated into the system utilizing XML files. Furthermore, the provided tool support is described. It enables a user to describe system components in an effort-reduced manner and conform to the schema defined by the information model and its restrictions via a GUI.

Knowledge-based integrated product design framework towards sustainable low-carbon manufacturing

With a global challenge on the serious ecological problems, low-carbon manufacturing aiming to reduce carbon emission and resource consumption is gaining the ever-increasing attention. Due to the significant impact on the product lifecycle, low-carbon product design is considered as an effective and attractive approach to improve the eco-market trade-off of electromechanical products. Existing low-carbon product design approaches focus on solving specific low-carbon problems, and how to explore and navigate the integrative design space considering low-carbon and knowledge in a holistic perspective is rarely discussed. In response, this paper proposes a knowledge-based integrated product design framework to support low-carbon product development. An ontology-based knowledge modelling approach is put forward to represent the multidisciplinary design knowledge to facilitate knowledge sharing and integration. Subsequently, a function–structure synthesis approach based on case-based reasoning is presented to narrow down the design space to generate suitable design solutions for achieving desired functions. A multi-objective mathematical model is established, and the multi-objective particle swarm optimization is adopted to solve the low-carbon product optimization. Furthermore, a decision-making ranking approach based on the closeness degree is employed to prioritize the potential solutions from Pareto set. Finally, a case study of low-carbon product design of hydraulic machine is demonstrated to show the effectiveness.

Unifying Online and Offline Preference for Social Link Prediction

Recent advances in network representation learning have enabled significant improvement in the link prediction task, which is at the core of many downstream applications. As an increasing amount of mobility data become available because of the development of location-based technologies, we argue that this resourceful mobility data can be used to improve link prediction tasks. In this paper, we propose a novel link prediction framework that utilizes user offline check-in behavior combined with user online social relations. We model user offline location preference via a probabilistic factor model and represent user social relations using neural network representation learning. To capture the interrelationship of these two sources, we develop an anchor link method to align these two different user latent representations. Furthermore, we employ locality-sensitive hashing to project the aggregated user representation into a binary matrix, which not only preserves the data structure but also improves the efficiency of convolutional network learning. By comparing with several baseline methods that solely rely on social networks or mobility data, we show that our unified approach significantly improves the link prediction performance.Summary of Contribution: This paper proposes a novel framework that utilizes both user offline and online behavior for social link prediction by developing several machine learning algorithms, such as probabilistic factor model, neural network embedding, anchor link model, and locality-sensitive hashing. The scope and mission has the following aspects: (1) We develop a data and knowledge modeling approach that demonstrates significant performance improvement. (2) Our method can efficiently manage large-scale data. (3) We conduct rigorous experiments on real-world data sets and empirically show the effectiveness and the efficiency of our proposed method. Overall, our paper can contribute to the advancement of social link prediction, which can spur many downstream applications in information systems and computer science.

An ontology model to support the automated design of aquaponic grow beds

Aquaponics is a promising sustainable farming method that combines aquaculture and hydroponics. It allows the growth of crops without soil, pesticides, or fertilizers, and with a minimum amount of water. In aquaponic systems, the design of the growing area is directly linked to the type of crop about to be planted. The type of crop directly determines, for example, the spacing between plants and between channels, which is critical to determine the footprint required and estimate the system productivity. This paper proposes a knowledge modeling approach to support the design of aquaponic systems by automatically determining the required characteristics of the aquaponic system based on crop selection. The knowledge modeling is outlined as an ontology model that formally describes the existent links between the aquaponic grow bed characteristics and its design parameters. This study gives practitioners the capacity to visualize the impact of the desired crop selection on the aquaponic system design, as well as supporting clearer decision-making regarding production facility layout and system design in aquaponic farms.

A security specific knowledge modelling approach for secure software engineering

The paradigm shift of ‘Build Security In’ has emerged in recent decades with the underpinning idea that software security has to be an integral part of all the phases of the software development lifecycle. As a result, each phase of the lifecycle is associated with security specific best practices such as threat modelling and static code analysis. It was observed that various artefacts (i.e., security requirements, architectural flaws, bug reports, security test cases) generated as a result of security best practices tend to be disconnected from each other. This creates a significant barrier to ensure that the security issues identified in the architectural level are incorporated in the implementation level. In order to address this issue, this paper presents a knowledge-modelling based approach to semantically infer the associations between architectural level security flaws and code level security bugs, which is manually tedious. Threat modelling and static analysis are used to identify security flaws, and security bugs, respectively. The case study based experimental results reveal that the architectural security flaws have a significant impact on originating security bugs in the code level.

Fuzzy Ontology Based Activity Recognition for Assistive Health Care Using Smart Home

Assistive health care system is a viable solution for elderly care to offer independent living. Such health care systems are feasible through smart homes, which are intended to enhance the living quality of the occupant. Activities of daily living (ADL) are considered in the design of a smart home and are extended to abnormality detection in the case of health care. Abnormality in occupant behavior is the deviation of ongoing activity with that of the built activity model. Generally, supervised machine learning strategies or knowledge engineering strategies are employed in the process of activity modeling. Supervised machine learning approaches incur overheads in annotating the dataset, while the knowledge modeling approaches incur overhead by being dependent on the domain expert for occupant specific knowledge. The proposed approach on the other hand, employs an unsupervised machine learning strategy to readily extract knowledge from unlabelled data using contextual pattern clustering and subsequently represents it as ontology activity model. Ontology offers enhanced activity recognition through its semantically clear representation and reasoning, it has restriction in handling temporal data. Hence, this article in addition to unsupervised modeling focuses at enabling temporal reasoning within ontology using fuzzy logic. The proposed fuzzy ontology activity recognition (FOAR) framework represents an activity model as a fuzzy temporal ontology. Fuzzy SWRL rules modeled within ontology aid activity recognition and abnormality detection for health care. The experimental results show that the proposed FOAR has better performance in abnormality detection than that of the existing systems.

Validating data acquired with experimental multimodal biometric system installed in bank branches

An experimental system was engineered and implemented in 100 copies inside a real banking environment comprising: dynamic handwritten signature verification, face recognition, bank client voice recognition and hand vein distribution verification. The main purpose of the presented research was to analyze questionnaire responses reflecting user opinions on: comfort, ergonomics, intuitiveness and other aspects of the biometric enrollment process. The analytical studies and experimental work conducted in the course of this work will lead towards methodologies and solutions of the multimodal biometric technology, which is planned for further development. Before this stage is achieved a study on the data usefulness acquired from a variety of biometric sensors and from survey questionnaires filled in by banking tellers and clients was done. The decision-related sets were approximated by the Rough Set method offering efficient algorithms and tools for finding hidden patterns in data. Prediction of evaluated biometric data quality, based on enrollment samples and on user subjective opinions was made employing the developed method. After an introduction to the principles of applied biometric identity verification methods, the knowledge modelling approach is presented together with achieved results and conclusions.

Application of a modeling approach on a cyber-physical system "RobAIR"

This paper proposes a new method of fault diagnosis of cyber-physical systems (CPS). The complexity of these systems in heterogeneous technologies require elaborate diagnostic techniques and maintenance. We illustrate our methodology on a telepresence robot "RobAIR" as an example of CPS.First, we applied a knowledge modeling approach on this case study through the representation of four types of knowledge: functional, structural, topological and behavioral. These representations are achieved using a modeling tool "Papyrus". Secondly, from knowledge representation, three algorithms for detection and fault identification are proposed to remove the ambiguity in functions and components. These algorithms will be explained through the RobAIR example.

Modelling the Structure and Dynamics of Biological Pathways.

There is a need for formalised diagrams that both summarise current biological pathway knowledge and support modelling approaches that explain and predict their behaviour. Here, we present a new, freely available modelling framework that includes a biologist-friendly pathway modelling language (mEPN), a simple but sophisticated method to support model parameterisation using available biological information; a stochastic flow algorithm that simulates the dynamics of pathway activity; and a 3-D visualisation engine that aids understanding of the complexities of a system’s dynamics. We present example pathway models that illustrate of the power of approach to depict a diverse range of systems.

Past and future perspectives on mathematical models of tick-borne pathogens.

Ticks are vectors of pathogens which are important both with respect to human health and economically. They have a complex life cycle requiring several blood meals throughout their life. These blood meals take place on different individual hosts and potentially on different host species. Their life cycle is also dependent on environmental conditions such as the temperature and habitat type. Mathematical models have been used for the more than 30 years to help us understand how tick dynamics are dependent on these environmental factors and host availability. In this paper, we review models of tick dynamics and summarize the main results. This summary is split into two parts, one which looks at tick dynamics and one which looks at tick-borne pathogens. In general, the models of tick dynamics are used to determine when the peak in tick densities is likely to occur in the year and how that changes with environmental conditions. The models of tick-borne pathogens focus more on the conditions under which the pathogen can persist and how host population densities might be manipulated to control these pathogens. In the final section of the paper, we identify gaps in the current knowledge and future modelling approaches. These include spatial models linked to environmental information and Geographic Information System maps, and development of new modelling techniques which model tick densities per host more explicitly.

Detailed clinical models: representing knowledge, data and semantics in healthcare information technology.

ObjectivesThis paper will present an overview of the developmental effort in harmonizing clinical knowledge modeling using the Detailed Clinical Models (DCMs), and will explain how it can contribute to the preservation of Electronic Health Records (EHR) data.MethodsClinical knowledge modeling is vital for the management and preservation of EHR and data. Such modeling provides common data elements and terminology binding with the intention of capturing and managing clinical information over time and location independent from technology. Any EHR data exchange without an agreed clinical knowledge modeling will potentially result in loss of information.ResultsMany attempts exist from the past to model clinical knowledge for the benefits of semantic interoperability using standardized data representation and common terminologies. The objective of each project is similar with respect to consistent representation of clinical data, using standardized terminologies, and an overall logical approach. However, the conceptual, logical, and the technical expressions are quite different in one clinical knowledge modeling approach versus another. There currently are synergies under the Clinical Information Modeling Initiative (CIMI) in order to create a harmonized reference model for clinical knowledge models.ConclusionsThe goal for the CIMI is to create a reference model and formalisms based on for instance the DCM (ISO/TS 13972), among other work. A global repository of DCMs may potentially be established in the future.

Providing semantic metadata to online learning resources on sustainable agriculture and farming: combining values and technical knowledge

Sustainable or organic agriculture aims at harmonizing the efficient production of food with the preservation of the environmental conditions for continuing production in a sustained way. As such, it embodies a set of environmental values that are currently taught and learnt worldwide in specific courses or as part of broader programs or curricula. The increased availability of online, open learning resources enables new possibilities for education in the domain of sustainable agriculture and farming. Concretely, portal projects related to sustainable agriculture have been deployed in the last years, using metadata as a mechanism to make resource search more effective in large, multilingual, and heterogeneous content databases. As environmental values are part of the learning needs targeted by those systems, it is required that both axiological descriptions (relating to values) and technical information (related to domain-specific issues) are expressed through metadata records, which requires a clear differentiation of both kinds of descriptions. This article reports on a knowledge modeling approach that fulfils that need and that can be used for the annotation of learning resources using standardized metadata schemas as IEEE LOM. The model has been prepared for the annotation of learning resources in a cross-national federation of organic agriculture repositories called Organic.Edunet, enabling the combination of both kinds of aspects in the formulation of searches. An analytic assessment of the expressive capabilities of the approach is provided using example resources.

Knowledge-aware identity services

The identification problem is concerned with the question whether two objects in an application refer to the same real-world entity. In this paper, the identification problem is investigated from a knowledge modelling point of view. We develop a framework of establishing knowledge-aware identity services by abstracting identity knowledge into an additional identity layer. The knowledge model in the identity service layer provides a capability for combining declarative formulae with concrete data and thus allows us to capture domain-specific identity knowledge at flexible levels of abstraction. By adding validation constraints to the identity service, we are also able to reason about inconsistency of identity knowledge. In doing so, the accuracy of identity knowledge can be improved over time, especially when utilising identity services provided by different communities in a service-oriented architecture. Our experimental study shows the effectiveness of the proposed knowledge modelling approach and the effects of domain-specific identity knowledge on data quality control.

Learning from others: Exchange of classification rules in intelligent distributed systems

Learning by an exchange of knowledge and experiences enables humans to act efficiently in a very dynamic environment. Thus, it would be highly desirable to enable intelligent distributed systems to behave in a way which follows that biological archetype. We believe that knowledge exchange will become increasingly important in many application areas such as intrusion detection, driver assistance, or robotics. Constituents of a distributed system such as software agents, cars equipped with smart sensors, or intelligent robots may learn from each other by exchanging knowledge in form of classification rules, for instance. This article proposes techniques for the exchange of classification rules that represent uncertain knowledge. For that purpose, we introduce methods for knowledge acquisition in dynamic environments, for gathering and using meta-knowledge about rules (i.e., experience), and for rule exchange in distributed systems. The methods are based on a probabilistic knowledge modeling approach. We describe the results of two case studies where we show that knowledge exchange (exchange of learned rules) may be superior to information exchange (exchange of raw observations, i.e. samples) and demonstrate that the use of experiences (meta-knowledge concerning the rules) may improve that rule exchange process further. Some possible real application scenarios are sketched briefly and an application in the field of intrusion detection in computer networks is elaborated in more detail.

Research on Cooperative Maintenance Decision System for Power Plant

The cooperative maintenance is carried out of China's electric power system, an important research topic, how to achieve wide-area collaboration maintenance decision is one of the forefront research tasks in the maintenance field. In order to solve the knowledge resource sharing and utilization in the plants maintenance field, on the basis of analyzing the current status and needs of the maintenance network, to achieve the object of swarm cooperative maintenance decision-making, we have established the collaborative maintenance decision system, is supported by multi-knowledge, multi-method, multi-resource, multi-information based on knowledge grid, and also have investigated the knowledge modeling approach. Resource nodes open mutually, share resource information and by the unified management and scheduling of the knowledge grid platform, coordination between each other, provide the necessary services for different power plants, manufacturers and research institutes. That is beneficial to enhancing the diagnosis standards and treatment ability of the maintenance decision system, reducing the diagnosis and maintenance costs, and improving the diagnosis efficiency.