Ontology-based Model Research Articles

Ontology-Based Modeling of Effect-Based Knowledge in Disaster Response

Emergency response and management requires the coordination of agencies and different services in a complex evolving situation. This in turn, requires diverse models representing detailed knowledge about the types of adverse events, their potential impact and the means and resources that are best suited for an effective response. The basic formal infrastructure incident assessment ontology (BFiaO) is oriented towards fulfilling these needs. BFiaO is a meta-model for handling infrastructure-related situations, but it did not provide models for a catalogue of adverse events and the means necessary for an adequate response. In this article, the authors present the key ontological commitments required for developing BFiaO-based extensible typologies of adverse events that are driven by the effects rather than by other aspects such as causes, or facilities affected. The model of a concrete case study is then presented that connects adverse event types to the kind of actions and resources required for mitigation.

Ontology-based modelling of extended web service secure conversation pattern

Ontology-based modelling of extended web service secure conversation pattern

Ontology-Based Model to Support Ubiquitous Healthcare Systems for COPD Patients

Over the past 30 years, information technology has gradually transformed the way health care is provisioned for patients. Chronic Obstructive Pulmonary Disease (COPD) is an incurable malady that threatens the lives of millions around the world. The huge amount of medical information in terms of complex interdependence between progression of health problems and various other factors makes the representation of data more challenging. This study investigated how formal semantic standards could be used for building an ontology knowledge repository to provide ubiquitous healthcare and medical recommendations for COPD patient to reduce preventable harm. The novel contribution of the suggested framework resides in the patient-centered monitoring approach, as we work to create dynamic adaptive protection services according to the current context of patient. This work executes a sequential modular approach consisting of patient, disease, location, devices, activities, environment and services to deliver personalized real-time medical care for COPD patients. The main benefits of this project are: (1) adhering to dynamic safe boundaries for the vital signs, which may vary depending on multiple factors; (2) assessing environmental risk factors; and (3) evaluating the patient’s daily activities through scheduled events to avoid potentially dangerous situations. This solution implements an interrelated set of ontologies with a logical base of Semantic Web Rule Language (SWRL) rules derived from the medical guidelines and expert pneumologists to handle all contextual situations.

A Rule-Based Reasoner for Underwater Robots Using OWL and SWRL.

Web Ontology Language (OWL) is designed to represent varied knowledge about things and the relationships of things. It is widely used to express complex models and address information heterogeneity of specific domains, such as underwater environments and robots. With the help of OWL, heterogeneous underwater robots are able to cooperate with each other by exchanging information with the same meaning and robot operators can organize the coordination easier. However, OWL has expressivity limitations on representing general rules, especially the statement “If … Then … Else …”. Fortunately, the Semantic Web Rule Language (SWRL) has strong rule representation capabilities. In this paper, we propose a rule-based reasoner for inferring and providing query services based on OWL and SWRL. SWRL rules are directly inserted into the ontologies by several steps of model transformations instead of using a specific editor. In the verification experiments, the SWRL rules were successfully and efficiently inserted into the OWL-based ontologies, obtaining completely correct query results. This rule-based reasoner is a promising approach to increase the inference capability of ontology-based models and it achieves significant contributions when semantic queries are done.

Modelling residual value risk through ontology to address vulnerability of PPP project system

For facilitating the management of Residual Value Risk (RVR) in Public Private Partnership (PPP) projects, an ontology-based model is established to describe the generation process and complex relationships of RVR. An ontology-based approach is proposed to analyze the RVR in PPPs, which is a framework addressing the vulnerability and a knowledge-based modeling for RVR management. The RVR ontology model is composed of class of Project, Risk, and Vulnerability, as well as taxonomy of risk factors for risk sources (RS), risk events (RE), risk consequences (RC), exposure (V1), resilience (V2) and sensitivity (V3). Meanwhile, different relationships among taxonomies, classes and individuals are expressed in model. Moreover, the object properties for class project and the object properties of inherited/non-inherited relationships are defined. Meanwhile, project-based, risk-based, and vulnerability-based datatype property are further described. Then a real individual is established by using the ontology editing software Protégé. The proposed RVR ontology model can be used to visualize and manipulate various representations in RVR management as well as to implement the work of risk reasoning and analyzing. The proposed RVR ontology framework provides a useful framework to systematize different knowledge of RVRs in PPP projects, in which the related knowledge can be described clearly and effectively. Moreover, the proposed framework can enhance data process function and improve the analysis of RVR probability and vulnerability in PPP projects through sharedness and transferability of RVR knowledge provided by ontology-based RVR model for different stakeholders in PPP projects.

OntoProg: An ontology-based model for implementing Prognostics Health Management in mechanical machines

Trends in Prognostics Health Management (PHM) have been introduced into mechanical items of manufacturing systems to predict Remaining Useful Life (RUL). PHM as an estimate of the RUL allows Condition-based Maintenance (CBM) before a functional failure occurs, avoiding corrective maintenance that generates unnecessary costs on production lines. An important factor for the implementation of PHM is the correct data collection for monitoring a machine’s health, in order to evaluate its reliability. Data collection, besides providing information about the state of degradation of the machine, also assists in the analysis of failures for intelligent interventions. Thus, the present work proposes the construction of an ontological model for future applications such as expert system in the support in the correct decision-making, besides assisting in the implementation of the PHM in several manufacturing scenarios, to be used in the future by web semantics tools focused on intelligent manufacturing, standardizing its concepts, terms, and the form of collection and processing of data. The methodological approach Design Science Research (DSR) is used to guide the development of this study. The model construction is achieved using the ontology development 101 procedure. The main result is the creation of the ontological model called OntoProg, which presents: a generic ontology addressing by international standards, capable of being used in several types of mechanical machines, of different types of manufacturing, the possibility of storing the knowledge contained in events of real activities that allow through consultations in SPARQL for decision-making which enable timely interventions of maintenance in the equipment of a real industry. The limitation of the work is that said model can be implemented only by specialists who have knowledge in ontology.

EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis.

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments.

An ontology-based approach for supply-chain quality control: From a principal–agent perspective

The efficacy of the principal–agent contract in supply-chain quality control depends not only on contract parameters but also such noncontract parameters as cost of a high-quality effort and the diagnostic error of the inspection policy. The noncontract parameters usually fluctuate and are unobservable during contract execution, which may hinder suppliers’ high-quality effort, or, in other words, result in a lower efficacy for the contract. This article proposes an ontology-based approach to facilitating a principal–agent contract by monitoring the contract’s loss of efficacy. The approach consists of ontology-based models and data-centric algorithms. The ontology-based models not only formally represent concepts and relations between concepts involved in predicting whether a contract is efficient, but also organise multichannel data such as news, marketplace reports and industry databases containing information of factors impacting the unobservable noncontract parameters’ fluctuations. Based on the ontology-based models and multichannel data, the data-centric algorithms are developed to predict whether a contract will lose efficacy. We evaluate our approach through case study, simulation and comparison against related approaches to supply-chain quality control. The case study proves that our approach is appropriate. In the simulation evaluation, a combination of our approach and principal–agent contract is more efficient than just a principal–agent contract. The comparison results against related approaches show that our approach is a novel, inexpensive and directly applicable tool for reducing both asymmetric information and moral hazard in supply-chain quality control.

Current Development of Ontology-Based Context Modeling

Any information used to characterize the situation of an entity: a person, a place, or an object, can be considered as context. Indeed, context is crucial to avoid semantic ambiguity in data interpretation. However, linking data to its context is a recognized research issue. Adopting an ontology-based approach to model formally the context enables automatic interpretation and reasoning capabilities. This article discusses the main context modeling approaches based ontology by highlighting their principles, scenarios, use cases, benefits, and challenges to explore the use of ontologies to represent contexts.

Ontological MobiHealth System

<p>MobiHealth systems are tele-monitoring systems based on BAN (Body Area Network) and mobile health care (m-health) service platform utilizing next generation public wireless networks. Smart phone applications have self-monitoring and sensing capabilities and are used in health interventions and disease prevention. In this study, a prototype mobile sensing platform was introduced for m-Health and telemedicine applications, which medical sensors on patient body reports biological signals and environmental sensors send environment raw data to base station (smart phone). Then raw data were preprocessed and necessary features (situations) were extracted. In order to context computation and respond with adaptability, we have used ontology-based model. </p>

Ontology-Based Modelling and Information Extracting of Physical Entities in Semantic Sensor Networks

ABSTRACTThe semantic sensor web adds semantic web technologies such as ontology to sensor network. Semantic technologies can help the better management of query and data aggregation of the sensor network. So far, several ontologies have been presented for the semantic presentation of sensor networks concepts. However, applications and end-users require physical entities information rather than technical details and information regarding sensors and sensor network. This paper semantically models physical entities whose information is collected by sensor networks at a level higher than sensors and their observations. Hence, first, an ontology is presented for semantically modelling physical entities in the real world. Then, essential extensions are added to the model for modelling time, place, and relations of the entities with each other. In the end, a method was proposed for extracting the semantic information of entities from the data collected based on the presented model. Result of the modelling and simulation of the presented strategies on climate data indicates the desirable performance of modelling and entity-based information reasoning as compared to works carrying out sensor-based semantic modelling.

Investigation of design and coordination issues in outpatient chemotherapy clinics using an ontology-based conceptual model

In this paper, we develop an ontological model to check the consistency in the design process for identifying potential conflicts and improving communication and coordination in outpatient chemotherapy clinics (OCCs). The paper describes how potential inconsistency might be detected through a conceptual model to account for the context of OCCs using the enterprise engineering approach design and engineering methodology for organisations (DEMO) and reengineering or modification of the inconsistencies can be accomplished. The implications of the developed model revealed guidelines for modelling an ontological representation and automatically identifying problems and conflicts in the design process of the corresponding system. The observed hindrance reflects two decision makers with opposing objectives for the same patient. Our improving suggestion is to provide an information link between the treatment planner and the patient appointment scheduling transaction by which the two actors supply each other with information converging to one statement.

Model-based development and application generation for the batch process industry

In the context of required flexibility for production systems due to dynamic product life cycles and market demands, the application of semantic technologies is regarded as a beneficial approach. This paper describes the development workflow of an ontology-based model for representing concepts of the batch process domain towards the automated generation of an application in an industrial supervisory control and data acquisition (SCADA) system. The presented approach shows the feasibility of model-based development for industrial practice by integrating the ontology in an SQL database, which is accessible for the utilized industrial SCADA software.

Linked Semantic Model for Information Resource Service Toward Cloud Manufacturing

Information resource services are the key element for resource sharing in cloud manufacturing. Traditional resource service models focus on modeling the attributes, interfaces, and descriptions of the resources into resource information services. Such resource services are suitable for local environment but suffer semantic heterogeneities in open cloud environment. Recently, well-designed ontologies are applied in resource service models to unify the schema and eliminate the semantic heterogeneities among the services. However, the effectiveness of ontology-based models mainly depends on the expertise of the ontology experts in ontology designing. Moreover, it is difficult to catch the dynamic changes in the cloud once the ontology has been embedded. In this paper, a semantic model is presented for information resource service modeling that uses semantic links instead of ontologies. The model takes advantage of semantic links to enable automated integrating and distributed updating in resource service cloud. In the experiment, the model is applied on practical manufacturing resources from a wheel manufacturing company. The case study and experimental results show that the proposed model is suitable for modeling manufacturing resources into cloud services and enables the flexible and distributed manipulation on resource services in the cloud environment.

Ontology-Based Modeling of Control Logic in Building Automation Systems

The control logic implemented in building automation systems (BAS) has a significant impact on the overall energy demand of the building. However, information on the control logic, if documented, is often concealed from further data integration and reuse in heterogeneous information silos using disparate data formats. In particular, existing data formats and information models offer limited support to describe control logic explicitly. Ontology-based modeling of the control logic of BAS can potentially result in a versatile source of information for information-driven processes to further increase the performance of technical equipment in a building. Therefore, we present a novel information model, CTRLont, which allows to formally specify the domain of control logic in BAS. We demonstrate the usefulness of the novel information model by using it as a knowledge base for automating rule-based verification of designed control logic in BAS. We successfully apply the methodology to a simple control of an air handling unit and indicate a number of future steps.

An ontology-based modelling and reasoning framework for assembly sequence planning

In the current era of increased customization, changing manufacturing systems and business globalization, effective use of product design information and knowledge generated from the product model can facilitate the decision-making of an assembly sequence by providing feasible product relationships and a viable semantic foundation. To enrich such semantics, a geometry-enhanced ontology modelling and reasoning framework is proposed in this paper to explicitly express relevant concepts for assembly sequence planning (ASP). A rule-based reasoning mechanism based on Ontology Web Language Description Logics (OWL-DL) and Semantic Web Rule Language (SWRL) is also suggested to clarify implicit relations by incorporating reasoning units (RUs) to process complex geometric information. This framework is then validated with a complex case study related to assembly sequence planning.

Models and methods of information management support in case of finance planning

Subject of Research. This study focuses on enterprise management optimization and development of integrated management information system supporting distributed decision-making processes. The paper describes local automation problems and heterogeneous information system integration for planning and forecasting support. Method. We propose an original enterprise document flow model with the use of temporal stochastic modeling methods. The paper considers the ontology-based model of heterogeneous information system integration. This study proposes ontology concept classification and methods for concept mapping and information objects integration. Main Results.A mathematical model of finance planning is developed based on the proposed models and methods. This model considers stochastic factors and enables funds to be reserved for future expenses. Proposed methods were applied in Finance Planning information system at ITMO University in 2016-2017. The system contains data coming from several information subsystems: Accounting, Salary and Scholarship, Logistics, Contract management. It provides users with expense forecast for 2-3 months that is about 20 % of the annual budget. The software was developed using Oracle DBMS technologies and PL/SQL language within the framework of the Management Information System at ITMO University. Practical Relevance. These approaches may be applied for integration of heterogeneous information systems and data sources at different areas. Proposed methods may be used not only in finance planning but are applicable also to other problems of management optimization with planning and forecasting support.

The OntoREA Accounting Model: Ontology-based Modeling of the Accounting Domain

McCarthy developed a framework for modeling the economic rationale of different business transactions along the enterprise value chain described in his seminal article “The REA Accounting Model – A Generalized Framework for Accounting Systems in a Shared Data Environment” Originally, the REA accounting model was specified in the entity-relationship (ER) language. Later on other languages – especially in form of generic data models and UML class models (UML language) – were used. Recently, the OntoUML language was developed by Guizzardi and used by Gailly et al. for a metaphysical reengineering of the REA enterprise ontology. Although the REA accounting model originally addressed the accounting domain, it most successfuly is applied as a reference framework for the conceptual modeling of enterprise systems. The primary research objective of this article is to anchor the REA-based models more deeply in the accounting domain. In order to achieve this objective, essential primitives of the REA model are identified and conceptualized in the OntoUML language within the Asset Liability Equity (ALE) context of the traditional ALE accounting domain.

Recommendation-Based Conceptual Modeling and Ontology Evolution Framework (CMOE+)

Within an enterprise, various stakeholders create different conceptual models, such as process, data, and requirements models. These models are fundamentally based on similar underlying enterprise (domain) concepts, but they differ in focus, use different modeling languages, take different viewpoints, utilize different terminology, and are used to develop different enterprise artifacts; as such, they typically lack consistency and interoperability. This issue can be solved by enterprise-specific ontologies, which serve as a reference during the conceptual model creation. Using such a shared semantic repository makes conceptual models interoperable and facilitates model integration. The challenge to accomplish this is twofold: on the one hand, an up-to-date enterprise-specific ontology needs to be created and maintained, and on the other hand, different modelers also need to be supported in their use of the enterprise-specific ontology. The authors propose to tackle these challenges by means of a recommendation-based conceptual modeling and an ontology evolution framework, and we focus in particular on ontology-based modeling support. To this end, the authors present a framework for Business Process Modeling Notation (BPMN) as a conceptual modeling language, and focus on how modelers can be assisted during the modeling process and how this impacts the semantic quality of the resulting models. Subsequently, a first, large-scale explorative experiment is presented involving 140 business students to evaluate the BPMN instantiation of our framework. The experiments show promising results with regard to incurred overheads, intention of use and model interoperability.

Ontology-based modeling and querying of trajectory data

With the evolution of location-sensing devices and associated technologies, mobility data driven scientific discovery approaches became an important paradigm for advanced computing performed in various central areas i.e., Internet of things and social networks. Under this paradigm, trajectory data is considered as a core revealing details of instantaneous behaviors piloted by mobile entities. This forms the need of modeling of such behaviors and the understanding of them, and actually, gave rise to different modeling approaches using either conceptual modeling or ontologies. Modeling and querying of trajectory data are still challenging because of their structural and semantic heterogeneities, and due to the complexity of establishing choices about the domain’ consensual knowledge. Ontologies are promising solutions for the above two problems seeing that they are intended to reduce structural heterogeneity among sources and to specify the semantics of concepts in an unambiguous way. In this paper, we propose a framework for a semantics oriented modeling and querying of trajectory data. We present an ontology-based trajectory pivot model that covers common structures encountered in trajectories associated with links to application and geographic modules. We validate our proposal through a case study dealing with human movement activity.