Ontology-based Model Research Articles

Ontology-based modelling for content delivery systems

Instead of managing metadata in a content management system (CMS) for content delivery, this research uses an ontology to make use of augmented intelligence. Hence, the retrieval of specific information in a content delivery system (CDS) is provided by a concise metadata concept. Ontology-based metadata concepts can have advantages in comparison to taxonomy-based metadata concepts because of the possibilities they offer to display linked information. The creation of an ontology depends on the vendor’s system, the vendor’s approach and most importantly the particular use case. Due to the ontology modelling, a data transformation for an external CDS is needed. When working with different vendors, requirements and considerations need to be discussed because the outcome can add value to user experience. The goal of this research is to identify the advantages of an ontology system over metadata management in a CMS. These advantages can be extracted by opportunities offered by the ontology modelling and by the transformation process.

Initial Ontology-Based Model for Workers Portfolio Resources

The development of electronic portfolio leads to the increase of the amount of data rapidly, scattered in many types of portfolio systems and represented in an unstructured manner. Those data cannot be reused because there are no structured-standard to manage and integrate them well. Many researchers have been developed ontology-based model for various resource domain and purpose. There is not much focus on the integration of other ontology domain like education, work histories and so forth. This paper will focus on how the competency representation and related ontology domain is modeled as initial comprehensive model for representing worker’s portfolio resources. This study employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for complete this study. The study is including Systematic Literature Review (SLR) to find out and review relevant literature of research object. As the result, we proposed model ontology that contains 4 classes with its subclass. Class of competence have sub class competency evaluation, proficiency level, domain and attitude. Class of artefact have subclass supplementary, training, indirect and direct. Class of Organization Entities have subclass tasks, projects and departments. Class of personal network have subclass group and network.

An efficient ontology-based topic-specific article recommendation model for best-fit reviewers

In general peer review is accredited as the vital and utmost cornerstone of the scientific publishing and research developments. Undeniably, the reviewers play a decisive role in ensuring the qualitative scientific developments published in any venue (Journals, conferences). The conventional time-tested method of double-blind peer review has been criticized having the flaws of inability to find the novelty, paucity of clarity, paucity of soundness, prone to be biased, the paucity of impartiality, discrepancies amongst reviewers, the paucity of acknowledgment and inspiration to reviewers. In order to cope with some of its flaws and to ensure the excellence of peer review, it is indispensable to delve into the process of article recommendation to the best fit reviewers. Typically, this recommendation is done by the human expert, so less accurate as the manual recommendation is incapable of initial scrutinizing of the tome of articles submitted and best fitting reviewer’s profile. This work proposes ontology and topic-specific personalized recommendation system to recommend the articles to the best-fit reviewers. In this proposed ontology-based model, latent semantic analysis and entropy have been deployed for similarity measure and topic-specificity indicator, thus to fetch the information of the best-fitted reviewer’s profile. In this work, an experimental arrangement has been set up relying on the primary dataset related to the reviewer’s profile and article reviewed. Results show the feasibility of the proposed model and the correlational relationship between the semantics and the topic-specificity of the articles which could be adopted as an automatic article recommendation to best fitting reviewers.

A traceability proposal for industry 4.0

This research examines traceability and its model in industry 4.0. Hence, this paper introduces the main design features of this model. The fourth industrial revolution is an environment that combines manufacturing with the Internet of Things and cyber-physical Systems. In such an environment, various sources (i.e., smart products, intelligent agents, and sensors) generate an increasing amount of data, which is essential for effective traceability. However, due to these heterogeneous sources, a traceability system should face the interoperability challenge and overcome the data integration issue. Moreover, the incorporation of this information in a traceability tool is motivated by the requirement to have access to a maximum amount of accurate product data. Thus, this article proposes to take advantage of industry 4.0 information. Also, the present study advocates that traceability should not only allow trace and track but also ensure product safety and quality. Accordingly, the proposal includes an intelligent traceability description, an ontology-based modeling, and a cloud-based application. This system provides users with a common knowledge base to access and represent data. Also, this model enables users to share and query remotely the traceable information using the cloud.

An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example.

In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of the proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book, and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction.

GRALD: an Approach for Goal and Risk Analysis in the Development of Information Systems for the Web of Data

The publication of Linked Data on the Web regarding several application domains leads to new problems related to Requirements Engineering, which needs to take into account aspects related to new ways of developing systems and delivering information integrated with the Web of Data. Tasks such as (functional and non-functional) requirements elicitation and ontology-based conceptual modeling can be applied to the development of systems that publish Linked Data, in order to obtain a better shared conceptualization (i.e., a domain ontology) of the published data. The use of vocabularies is an intrinsic activity when publishing or consuming Linked Data and their choice can be supported by the elicited requirements and domain ontology. However, it is important to assess the risk when choosing external vocabularies, as their use can lead to problems, such as misinterpretation of meanings due to poor documentation, connection timeouts due to infrastructure problems, etc. Thus, risk identification, modeling and analysis techniques can be employed, in order to identify risks and their impacts on stakeholder goals. In this work, we propose GRALD: Goals and Risks Analysis for Linked Data, an approach for modeling goals and risks for information systems for the Web of Data.

Ontology-based venous thromboembolism risk assessment model developing from medical records

BackgroundPadua linear model is widely used for the risk assessment of venous thromboembolism (VTE), a common but preventable complication for inpatients. However, genetic and environmental differences between Western and Chinese population limit the validity of Padua model in Chinese patients. Medical records which contain rich information about disease progression, are useful in mining new risk factors related to Chinese VTE patients. Furthermore, machine learning (ML) methods provide new opportunities to build precise risk prediction model by automatic selection of risk factors based on original medical records.MethodsMedical records of 3,106 inpatients including 224 VTE patients were collected and various types of ontologies were integrated to parse unstructured text. A workflow of ontology-based VTE risk prediction model, that combines natural language processing (NLP) and machine learning (ML) technologies, was proposed. Firstly ontology terms were extracted from medical records, then sorted according to their calculated weights. Next importance of each term in the unit of section was evaluated and finally a ML model was built based on a subset of terms. Four ML methods were tested, and the best model was decided by comparing area under the receiver operating characteristic curve (AUROC).ResultsMedical records were first split into different sections and subsequently, terms from each section were sorted by their weights calculated by multiple types of information. Greedy selection algorithm was used to obtain significant sections and terms. Top terms in each section were selected to construct patients’ distributed representations by word embedding technique. Using top 300 terms of two important sections, namely the ‘Progress Note’ section and ‘Admitting Diagnosis’ section, the model showed relatively better predictive performance. Then ML model which utilizes a subset of terms from two sections, about 110 terms, achieved the best AUC score, of 0.973 ± 0.006, which was significantly better compared to the Padua’s performance of 0.791 ± 0.022. Terms found by the model showed their potential to help clinicians explore new risk factors.ConclusionsIn this study, a new VTE risk assessment model based on ontologies extraction from raw medical records is developed and its performance is verified on real clinical dataset. Results of selected terms can help clinicians to discover meaningful risk factors.

An Ontology-Based Model for Treatment Guidelines of Internet and Games Addiction

Internet and games addiction will become a difficult problem for the parents, because the internet and games easier to access and has more contents. Thus, the number of internet and games addiction will be increasing in the future. This study recommends ontology expansion for treatment guidelines of internet and games addiction that will use as the component of recommendation system in web technology. This study’s methodology can be condensed into three states; data collection ontology development, and evaluation. This ontology included seven main classes, there are profile, characteristics, risk factors, devices, treatment, and GAST. The evaluation result that conducted by domain experts included a highly-superior concentration of 88.34%, which confirms that this ontology may be employed for developing a recommendation system.

OMDP: An ontology-based model for diagnosis and treatment of diabetes patients in remote healthcare systems

Millions of adults have diabetes across the globe and the overall cost for managing diabetic patients has reached up to approximately 250 million. A major constraint in existing ontology-based systems for diagnosing and treating diabetes is the presence of semantic inconsistencies and lack of a comprehensive clinical approach primarily due to consideration of a limited number of classes in the model. In this research, we are focused on building an ontology-based model for diabetic patients by collecting detailed diabetic knowledge of subjects for further diagnosis and treatment. The concept of semantic resources to electronic health record standards is an essential factor for semantic interoperability in remote health monitoring. This study applies semantic web ontology language for developing ontology-based model for diabetic patients to aid doctors in reaching an efficient diagnostic decision about the status of diabetes by applying Semantic Web Rule Language. A total of 766 medical records from clinical environment were selected in this study, and 269 of them were known for developing diabetes. The experimental results suggest that the proposed solution is more accurate in managing diabetes compared to other medical applications. The performance analysis of the ontology-based model for diabetic patients regarding the accuracy of disease prediction, diagnosing diabetes, and recommending medicine is 95%, 98%, and 85%, respectively.

A Framework for Ontology-based Model of Information Management Assessment Criteria based on Syariah Compliance

The Holy Quran and Hadith ontology models are gaining popularity among researchers due to people's demands in understanding these divine books. Due to this, there are many studies have been conducted in this area to facilitate people's understanding of the Quran and Hadith. From the literature, however, most existing Quran and Hadith applications cover either Quran or Hadith only. Some applications focus on a domain of knowledge, while others only apply term matching and do not provide a semantic interpretation of keyword for a query. Thus, the purpose of this paper is to propose a Quran and Hadith application that covers both prime reference sources in Islam. It will focus on one domain of knowledge that is information management assessment criteria, where there is still a lack of research in this domain. It will also apply the semantic interpretation of the keyword to obtain results that are more comprehensive related to the context of the keyword. Evidence from the Quran and Hadith (Sahih Bukhari) related to the criteria of information management assessment will also be provided. The expected outcome for this paper is a framework of an ontology-based model for information management assessment criteria based on Syariah compliance that will be a useful guide for Muslims and all to assess their daily routine of managing information whether it complies with Islamic rules or vice versa.

A prediction method of building seismic loss based on BIM and FEMA P-58

Abstract Predicting the seismic loss of a building is critical for its resilience. A prediction method for building seismic loss based on the building information model (BIM) and FEMA P-58 is proposed in this study. First, a component-level damage prediction algorithm is designed to establish the mapping from BIM components to the performance groups (PGs) in FEMA P-58, and to predict the component damage using the BIM-based time-history analysis (THA) and the fragility curves of PGs. Subsequently, an ontology-based model considering the deduction rules in the local unit-repair-cost database is created for obtaining exact measurement data of components in a BIM. Meanwhile, a component-level loss prediction algorithm is developed using the measurement data and the unit repair costs corresponding to damage states, by which the predicted seismic losses can agree with the actual situation of the specific region. Finally, a component-level visualization algorithm is designed to display the seismic damage and loss in a virtual reality (VR) environment. A six-story office building in Beijing is used as a pilot test to demonstrate the advantages of the proposed method. The outcome of this study produces a component-level and visual loss prediction result that agrees with the actual situation of the specific region, which can be used to evaluate the post-earthquake economic resilience of different buildings.

Methods Supporting Supplier Selection Processes – Knowledge-based Approach

Nowadays, companies have to improve their practices in the management of sustainable supply chain with increased awareness of environmental, economic and social issues globally. Selecting the optimum sustainable supplier is crucial for sustainable supply chain management, which is a challenging multi-dimensional problem. From a systematic perspective, sustainable supplier selection problem can be separated into two parts, including criteria and methods. This paper concentrates on the method selection aspects, referring to their application examples. Thus, the objectives of this paper are twofold: one is to summarize the literature on supplier selection issues from related articles, then to present an attempt to ontology-based approach to handling knowledge about sustainable supplier selection methods. To meet this aim, ontology-based model is proposed to synthetize the knowledge of analyzed methods and related papers. Finally, the proposed ontology-based model is demonstrated by an empirical example of using competency questions to confirm its correctness and effectiveness.

Towards AI-Powered Multiple Cloud Management

Cloud users worldwide are looking at the next generation of artificial intelligence (AI) powered cloud management tools to automate cloud performance tuning and anomaly detection. To be effective across clouds, AI tools need a common representation of cloud services and support for machine learning optimization targeting multiple objectives. We put forward the notion that ontology-based models can support both.

Ontology-based modelling of extended web service secure conversation pattern

Securing an application based on Service Oriented Architecture provides defences against a number of security threats arising from exposing applications and data to the internet. Various security guidelines are available to apply security in web applications. But these guidelines are sometimes difficult to understand and generate inconsistencies. In this study, an extended web service secure conversation pattern is presented in the presence of a man-in-the-middle attack. An ontology-based modelling and refinement framework is presented for semantically analysing an extended web service secure conversation pattern. A metamodel is introduced to provide rigorous modelling of security services in terms of concepts, properties, and relationships. At the end of this study, an evaluation of the proposed approach has been made by performing experiments for security requirements against security policies in presence of proposed description logic rules.

An ontology-based approach to improve access policy administration of attribute-based access control

Attribute-based access control (ABAC) needs a large number of policies to function by using attributes of visitors, resources, environmental conditions, etc. Efficient policy administration is vital for implementation of ABAC models. In this paper, an ontology-based approach is proposed to build up an ABAC model, which is named as an ontology-based ABAC model, OABACM. Underlying relationships among things such as attributes hierarchies in OABACM are identified and described in OABACM, which if treated improperly can directly lead to problems in policy administration. In addition, policy representation and reasoning mechanism are discussed within OABACM and inherent logical properties of this model are formalised in rules. With proper reasoners, these properties can be utilised to logically improve access policy administration by reducing policy redundancy and detecting policy conflicts. In experiments, a sample ontology is created and several enterprise access examples are tested upon OABACM, which validates the effects of our model on policy administration.

Ontology-based decision tree model for prediction in a manufacturing network

ABSTRACTThis paper aims to create a predictive model, which will assist in the allocation of newly received orders in a manufacturing network. The manufacturing network, which is taken as a case study in this research, consists of more than 300 small manufacturing enterprises with a central company as the project managing integrator. The methodology presents the mapping of a PROSA (Product-Resource-Order-Staff Architecture) based ontology model on a decision tree, which was created with the Waikato Environment for Knowledge Analysis (WEKA) application. Furthermore, the methodology also demonstrates the formulation of the Semantic Web Rule Language (SWRL) rules from the WEKA decision tree with the help of MATLAB programming. The paper validated the result generated by the ontology model with the results of the decision tree model.

An Improved Route‐Finding Algorithm Using Ubiquitous Ontology‐Based Experiences Modeling

Every day, people are hired in different organizations and old and retiring employees are eliminated from enterprise systems. Eliminating these individuals from organizations leads to the loss of their spatial experiences. In addition, since new employees lack relevant experience, they need a long time to develop the correct skills for the company and may even cause damage to the organization during this learning process. Therefore, storing the spatial experience of individuals is a critical issue. Due to the intelligence of ubiquitous Geospatial Information System (GIS), any experience from any user can be received and stored. In the future, based on these experiences, an appropriate service to each user may be provided as needed. This paper aims to propose an ontology‐based model to store spatial experiences in the field of ubiquitous GIS route finding. For this purpose, first ontology is designed for route finding, and then according to this ontology, an ontology‐based route‐finding algorithm is developed for ubiquitous GIS. Finally, this algorithm is implemented for Tehran, Iran, and its results are compared with the shortest path algorithm (Dijkstra’s algorithm) in terms of the route length and travel time for peak traffic time. The results show that while the route length obtained from the ontology‐based algorithm is more than Dijkstra’s algorithm, the travel time is lower, and on some routes the difference in travel time saved reaches 35 minutes.

Ontology Supporting Green Supplier Selection Process

At present, companies need to consider and include so-called ‘green strategies’ in order to retain competitive advantage. The common understanding of functionality of supply chain as well as collaborations among this between suppliers and partners means that companies need to do much more in order to ensure the greener effect. On the other hand, a scattered model of conducting businesses and global competition force companies to consider the necessity for development the activities of proper selection and evaluation of beneficial supply chain partnerships. Due to large amount of literature-based references of green supplier selection and evaluation, knowledge scattering of greener collaboration is noticeable. In addressing this research challenge, capturing knowledge in one place in the form of ontology for enabling selection and evaluation criteria of suppliers is proposed. Within this paper, an attempt to application of ontology-based model for supplier’ selection and evaluation criteria is developed. This model is implemented on base of distinctive set of criteria derived from revised literature, nevertheless the public and common availability of the proposed model suggests to other researches to collaborate in this field by adding, sharing and reusing knowledge of alternative criteria of green supplier’ selection.

ПРОЕКТИРОВАНИЕ СИСТЕМ ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ ПОД УПРАВЛЕНИЕМ ОНТОЛОГИЙ: МОДЕЛИ, МЕТОДЫ, РЕАЛИЗАЦИИ

Ontology-driven software engineering is the scope of the paper. A retrospective analysis of the approaches, models and methods is carried out. In the first section, a brief overview of "classical" technologies, approaches and methods for software engineering is presented. It is shown that the emphasis of research and development is currently shifting from the field of technologies of programming towards the modeling of the software design processes, as well as towards the specifications and testing of the developed systems. It is outlined that the general trend in the domain is the use of methods and tools of ontology modeling of both the design processes per se and the specifications of the developed systems. The second section is devoted to a review of publications in the field of ontology-based software engineering. Such paradigms for modeling software development processes as Software Product Lines, plug-and-play software development - Component-Based Development, model-driven software engineering - Model-Driven Engineering, etc. are discussed. It is shown that "programming through generation", i.e. Generative Programming, is a general approach supporting the considered paradigms. It is shown that R&D in the domain of the semantic models of software engineering is currently focused on a new approach - Ontology-Based (-Driven) Software Engineering. Within the framework of this area, ontology engineering tools are developed along with modeling the software development processes. The scientific and technical perspectives of ontology driven software engineering are specified and discussed in the final part of the paper. In particular, it is outlined that the theoretical foundations of ontology-based modeling of software design processes form the solid basis for shifting the focus from the models of compiling the modules to their pattern-based generation from internal specifications. It is noted that the next trend in the field of OBSE-ODSE will be the usage of machine learning methods and tools to generate ontological models of software design.

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.