Ontology-based Techniques Research Articles

Systematic Review of Web-Based Decision Support Systems for Clinical Applications: Enhancing Ontology with Unified Modeling Language and Ontology Web Language

This article demonstrates an ontology-based technique that aids in envisioning the problem domain before software development, presenting an approach to assist decision-making for reliable and optimal healthcare administration. The proposed approach is designed for utilization by a Web-Based Decision Support System (WebDSS). The purpose of the study is to aid readers in comprehending how ontologies in healthcare management platforms can be modeled using UML and OWL. For the conceptualization and the design, service modelling and the semantics of the web language are crucial. We demonstrate the use of the Unified Modeling Language (UML) and the Ontology Web Language (OWL) in the administration of healthcare operations and key elements of the model-base of healthcare administration systems. Highly educated systems with automated assistance will allow decision-makers to more effectively incorporate aspects of sustainability into their decisions. In order to generate semantic ontologies for medical management, the present study proposes an ontology creation technique and a Semantic Web framework called Protégé. Web-based technologies, decision trees, data mining, and computational algorithms are implemented by WebDSS to assess medical resources administration information in real-time. These methods are employed to develop a decision making framework for this study. Time series graphs, email reports, and webpages are used to display the test findings. When healthcare thresholds are surpassed, the system uses text messages as email reports to automatically deliver warnings. Our study's primary contribution is the creation of a useful tool that makes it easier for academics studying medical concerns to locate online sites with information.

Ontological Augmentation and Analytical Paradigms for Elevating Security in Healthcare Web Applications

The 4th and 5th industrial revelations provided huge access to a high volume of information in healthcare sectors. However, the vast majority of web applications are under attack constantly. Due to the huge volume of attack vectors in recent years, technical breaches to the web applications of healthcare sectors are becoming a common issue. Hence, it is essential to develop an effective framework that would help experts and healthcare practitioners in web application security management. In this research, we proposed an ontology-based technique for developing secure online web applications in healthcare sectors. This study presents a conceptual framework with 5 stages namely: idea understanding, requirement identification, design and code, threat classification, and facilitate. The proposed methodology involves several various advantage features such as providing a unified path for future professional applications. Also, the proposed solution provides a clear pathway for implementing the easy-to-use secure web application development in short term in the healthcare sector. Finally, the study used a multi-criteria decision-making methodology to undertake a performance simulation assessment. Out of the evaluated process, Fuzzy-AHP has shown better performance for scheduling each step of the final developed security system better. In the future, the proposed method will be evaluated using more databases in the healthcare sector to improve the applications of the proposed method.

A highly scalable CF recommendation system using ontology and SVD-based incremental approach

In recent years, the need of recommender systems has increased to enhance user engagement, provide personalized services, and increase revenue, especially in the online shopping industry where vast amounts of customer data are generated. Collaborative filtering (CF) is the most widely used and effective approach for generating appropriate recommendations. However, the current CF approach has limitations in addressing common recommendation problems such as data inaccuracy recommendations, sparsity, scalability, and significant errors in prediction. To overcome these challenges, this study proposes a novel hybrid CF method for movie recommendations that combines the incremental singular value decomposition approach with an item-based ontological semantic filtering approach in two phases, online and offline. The ontology-based technique is leveraged to enhance the accuracy of predictions and recommendations. Evaluating our method on a real-world movie recommendation dataset using precision, F1 scores, and mean absolute error (MAE) demonstrates that our system generates accurate predictions while addressing sparsity and scalability issues in recommendation system. Additionally, our method has the advantage of reduced running time.

Enhancing Recommendation System using Ontology-based Similarity and Incremental SVD Prediction

Background: With the explosion of data in recent years, recommender systems have become increasingly important for personalized services and enhancing user engagement in various industries, including e-commerce and entertainment. Collaborative filtering (CF) is a widely used approach for generating recommendations, but it has limitations in addressing issues such as sparsity, scalability, and prediction errors. Methods: To address these challenges, this study proposes a novel hybrid CF method for movie recommendations that combines an incremental singular value decomposition approach with an item-based ontological semantic filtering approach in both online and offline phases. The ontology-based technique improves the accuracy of predictions and recommendations. The proposed method is evaluated on a real-world movie recommendation dataset using several performance metrics, including precision, F1 scores, and MAE. Results: The results demonstrate that the proposed method outperforms existing methods in terms of accuracy while also addressing sparsity and scalability issues in recommender systems. Additionally, our approach has the advantage of reduced running time, making it a promising solution for practical applications. Conclusion: The proposed method offers a promising solution to the challenges faced by traditional CF methods in recommender systems. By combining incremental SVD and ontological semantic filtering, the proposed method not only improves the accuracy of predictions and recommendations but also addresses issues related to scalability and sparsity. Overall, the proposed method has the potential to contribute to the development of more accurate and efficient recommendation systems in various industries, including e-commerce and entertainment.

Development of an Ontology-Based Technique for Labeling Land Cover Classes with Minimum Utilization of SAR Features

Development of an Ontology-Based Technique for Labeling Land Cover Classes with Minimum Utilization of SAR Features

Toward a Knowledge-Based System for African Traditional Herbal Medicine: A Design Science Research Approach.

This article illustrates a design approach for capturing, storing, indexing, and search of African traditional herbal medicine (ATHMed) framed on a hybrid-based knowledge model for efficient preservation and retrieval. By the hybrid approach, the framework was developed to include both the use of machine learning and ontology-based techniques. The search pattern considers ontology design and machine learning techniques for extracting ATHMed data. The framework operates on a semantically annotated corpus and delivers a contextual and multi-word search pattern against its knowledge base. In line with design science research, preliminary data were collected in this study, and a proposed strategy was developed toward processing, storing and retrieving data. While reviewing literature and interview data to reflect on the existing challenges, these findings suggest the need for a system with the capability of retrieving and archiving ATHMed in Ghana. This study contributes to SDG 3 by providing a model and conceptualizing the implementation of ATHMed. We, therefore, envision that the framework will be adopted by relevant stakeholders for the implementation of efficient systems for archival and retrieval of ATHMed.

A Hybrid Recommender System for HCI Design Pattern Recommendations

User interface design patterns are acknowledged as a standard solution to recurring design problems. The heterogeneity of existing design patterns makes the selection of relevant ones difficult. To tackle these concerns, the current work contributes in a twofold manner. The first contribution is the development of a recommender system for selecting the most relevant design patterns in the Human Computer Interaction (HCI) domain. This system introduces a hybrid approach that combines text-based and ontology-based techniques and is aimed at using semantic similarity along with ontology models to retrieve appropriate HCI design patterns. The second contribution addresses the validation of the proposed recommender system regarding the acceptance intention towards our system by assessing the perceived experience and the perceived accuracy. To this purpose, we conducted a user-centric evaluation experiment wherein participants were invited to fill pre-study and post-test questionnaires. The findings of the evaluation study revealed that the perceived experience of the proposed system’s quality and the accuracy of the recommended design patterns were assessed positively.

A STAMP-based ontology approach to support safety and security analyses

Considerations of safety and security in the early stage of system life cycle are essential to collect and prioritize operation needs, determine feasibility of the desired system, and identify technology gaps. Experts from many disciplines are needed to perform the safety and security analyses, ensuring that a system has the necessary attributes. Safety assessment is usually conducted in the concept stage. On the order hand, security assessment is performed in design stage usually when an initial architecture along with the logical and physical components are defined. Systems-Theoretic Process Analysis (STPA) is a new hazard analysis technique based on systems thinking and is built on top of a new causality model of accident, which stands for Systems-Theoretic Accident Model and Processes (STAMP), grounded in systems theory. STPA for Security (STPA-Sec) is an extension of STPA that proposes to include security concerns into the analysis. STPA-Sec helps identifying some hazardous control actions, causal scenarios, and casual factors; however, no emphasis is placed on security threat scenarios. In this paper we propose an ontology-based technique that extends STPA-Sec to improve identification of causal scenarios and associated casual factors, specifically those related to security. We propose an approach that assists safety and security experts conducting safety and security analyses using STPA-Sec with a supporting ontology. First, we present an ontology representing the safety and security knowledge through STPA-Sec process, and provide a tool that implements the proposed ontology. We then propose a process to capture safety and security knowledge into the proposed ontology to identify causal scenarios. We perform a preliminary evaluation of the ontology and the process using an aeronautic case study. The results show that the ontology-based approach helps systems engineers to identify more security scenarios compared to the case where they use only STPA-Sec. Furthermore, some hazardous control actions are not addressed if the systems engineer uses the basic STPA-Sec.

Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniques

The miniaturization and price reduction of sensors have encouraged the proliferation of smart environments, in which multitudinous sensors detect and describe the activities carried out by inhabitants. In this context, the recognition of activities of daily living has represented one of the most developed research areas in recent years. Its objective is to determine what daily activity is developed by the inhabitants of a smart environment. In this field, many proposals have been presented in the literature, many of them being based onad hocontologies to formalize logical rules, which hinders their reuse in other contexts. In this work, we propose the use of class expression learning (CEL), an ontology-based data mining technique, for the recognition of ADL. This technique is based on combining the entities in the ontology, trying to find the expressions that best describe those activities. As far as we know, it is the first time that this technique is applied to this problem. To evaluate the performance of CEL for the automatic recognition of activities, we have first developed a framework that is able to convert many of the available datasets to all the ontology models we have found in the literature for dealing with ADL. Two different CEL algorithms have been employed for the recognition of eighteen activities in two different datasets. Although all the available ontologies in the literature are focused on the description of the context of the activities, the results show that the sequence of the events produced by the sensors is more relevant for their automatic recognition, in general terms.

A Method for Extending Ontologies with Application to the Materials Science Domain

In the materials science domain the data-driven science paradigm has become the focus since the beginning of the 2000s. A large number of research groups and communities are building and developing data-driven workflows. However, much of the data and knowledge is stored in different heterogeneous data sources maintained by different groups. This leads to a reduced availability of the data and poor interoperability between systems in this domain. Ontology-based techniques are an important way to reduce these problems and a number of efforts have started. In this paper we investigate efforts in the materials science, and in particular in the nanotechnology domain, and show how such ontologies developed by domain experts, can be improved. We use a phrase-based topic model approach and formal topical concept analysis on unstructured text in this domain to suggest additional concepts and axioms for the ontology that should be validated by a domain expert. We describe the techniques and show the usefulness of the approach through an experiment where we extend two nanotechnology ontologies using approximately 600 titles and abstracts.

Open Data for Public Administration: Exploitation and semantic organization of institutional web content

The project presented has been financed by Government of Aragon and is part of the `Open Data' initiative promoted by that organization. Given the amount of unstructured information related to the Government of Aragon currently published on the Internet, with slightly or no standardization and decentralized, it emerges the need to gather it systematically to be offered to all interested collectives from a single access point in a public and structured way. Within this context, `Aragon Open Data' project aims to collect, organize, store and maintain updated, Administration's web information by means of human language and semantic technologies. Firstly, crawling is performed over websites in order to retrieve textual data over which Natural Language Processing (NLP) and ontology-based techniques are applied. Thereafter, results are stored into NoSQL databases, allowing future open access and simple data exploitation. NLP techniques used in the project involve named-entities recognition and classification (NERC) and texts semantic classification and summarization.

An ontology-based technique for the specification of domain and user models in multi-agent domain


 
 
 Because of its facilities for the generalization and specialization of concepts and the unambiguous terminology they provide, ontologies are being used for the representation of reusable software artifacts. This work describes GRAMO, an ontology-based technique for the specification of domain and user models in Multi-Agent Domain Engineering. ONTODUM, an ontology-based tool supporting GRAMO is also introduced. ONTODUM represents the knowledge of GRAMO. Some case studies developed to evaluate GRAMO are also briefly described.
 
 

A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home Environment

Accurate recognition of Activities of Daily Living (ADL) plays an important role in providing assistance and support to the elderly and cognitively impaired. Current knowledge-driven and ontology-based techniques model object concepts from assumptions and everyday common knowledge of object use for routine activities. Modelling activities from such information can lead to incorrect recognition of particular routine activities resulting in possible failure to detect abnormal activity trends. In cases where such prior knowledge are not available, such techniques become virtually unemployable. A significant step in the recognition of activities is the accurate discovery of the object usage for specific routine activities. This paper presents a hybrid framework for automatic consumption of sensor data and associating object usage to routine activities using Latent Dirichlet Allocation (LDA) topic modelling. This process enables the recognition of simple activities of daily living from object usage and interactions in the home environment. The evaluation of the proposed framework on the Kasteren and Ordonez datasets show that it yields better results compared to existing techniques.

Data mining and ontology-based techniques in healthcare management

Recently, large amounts of data have been produced due to the achieved advances in biotechnology and health sciences fields. It includes clinical information and genetic data which contained in electronic health records (EHRs). Therefore, there was a need for innovative and effective methods for representing this amount of data. On the other side, it is very important to detect syndromes, which can badly influence the human health in addition to putting financial burdens on their shoulders, in an early stage to avoid many complications. Recently, different data mining techniques in addition to ontology-based techniques have played a great role in building automated systems that have the ability to detect syndromes efficiently and accurately. In this paper, we cover some of the research efforts that have employed either the data mining techniques or ontology-based techniques, or both in detecting syndromes. Additionally, a set of well-known data mining techniques including decision trees (J48), Naive Bayes, multi-layer perceptron (MLP), and random forest (RF) has been assessed in performing the classification task using a publicly available heart diseases dataset.

Data mining and ontology-based techniques in healthcare management

Data mining and ontology-based techniques in healthcare management

Analytical Review on Ontological Human Activity Recognition Approaches

New advancements in pervasive computing technology have turned smart homes into a daily living monitoring tool increasingly used for elderly. Recently, using knowledge driven approaches such as ontology to introduce semantic smart homes has received attention due to their flexibility, reasoning and knowledge representation. Due to the vast number of ontological human activity recognition methods, the proposed ontological human activity recognition framework can be effective in analyzing and evaluating different methods in different applications and dealing with various challenges. Also, due to numerous challenges involved in different aspects of ontology-based human activity recognition in smart homes, this paper offers a classification for challenges in human activity recognition in ontology based systems. Then the proposed ontological human activity recognition framework is evaluated based on the proposed classification and ontology-based techniques which are thought to solve some of the challenges are examined and analyzed.

An assessment on domain ontology-based information extraction techniques

Domain ontology is used in information retrieval to retrieve more relevant information from a collection of unstructured information source. Information retrieval is becoming an important research area in the field of computer science. Information retrieval (IR) is generally concerned with the searching and retrieving of knowledge-based information from database. In this paper, we represent the various models and techniques for information retrieval. Domain ontology is a description of domain concepts with relation and properties to be used in knowledge engineering as a knowledge base. In this paper, various domain ontology-based information retrieval methods have been reviewed. A comparative analysis is made on all the available methods, which will allow the analyst to choose the suitable domain ontology-based information extraction method. There are various methods developed to make the information extraction more efficient. The methods have been classified as Boolean, vector space, semantic-based techniques and probabilistic. Semantic-based information retrieval can still be classified as semantic association, semantic similarity and semantic annotation. This assessment allows the developer to choose the best fit model for their requirement in an efficient way.

Activity Context Modeling in Context-Aware Environment

The explosion of mobile devices has fuelled the advancement of pervasive computing to provide personal assistance in this information-driven world. Pervasive computing takes advantage of context-aware computing to track, use and adapt to contextual information. The context that has attracted the attention of many researchers is the activity context. There are six major techniques that are used to model activity context. These techniques are key-value, logic-based, ontology-based, object-oriented, mark-up schemes and graphical. This paper analyses these techniques in detail by describing how each technique is implemented while reviewing their pros and cons. The paper ends with a hybrid modeling method that fits heterogeneous environment while considering the entire of modeling through data acquisition and utilization stages. The modeling stages of activity context are data sensation, data abstraction and reasoning and planning. The work revealed that mark-up schemes and object-oriented are best applicable at the data sensation stage. Key-value and object-oriented techniques fairly support data abstraction stage whereas the logic-based and ontology-based techniques are the ideal techniques for reasoning and planning stage. In a distributed system, mark-up schemes are very useful in data communication over a network and graphical technique should be used when saving context data into database.

Enabling Data Integration in the Rail Industry Using RDF and OWL: The RaCoOn Ontology

AbstractAs traditionally infrastructure-centric industries such as the railways deploy ever more complex information systems, data interoperability becomes a challenge that must be overcome in order to facilitate effective decision making and management. In this paper, the authors propose a system based on semantic data modeling techniques to allow integration of information from diverse and heterogeneous sources. The results of work, which aimed to demonstrate how semantic data models can be used in the rail industry, are presented. These include a novel domain ontology for the railways, and a proof-of-concept real time passenger information system based on semantic web technologies. Methods and patterns for creating such ontologies and real world systems are discussed, and ontology-based techniques for integrating data with legacy information systems are shown.

Ontology-based text summarization. The case of Texminer

Purpose – The purpose of this paper is to look into the latest advances in ontology-based text summarization systems, with emphasis on the methodologies of a socio-cognitive approach, the structural discourse models and the ontology-based text summarization systems. Design/methodology/approach – The paper analyzes the main literature in this field and presents the structure and features of Texminer, a software that facilitates summarization of texts on Port and Coastal Engineering. Texminer entails a combination of several techniques, including: socio-cognitive user models, Natural Language Processing, disambiguation and ontologies. After processing a corpus, the system was evaluated using as a reference various clustering evaluation experiments conducted by Arco (2008) and Hennig et al. (2008). The results were checked with a support vector machine, Rouge metrics, the F-measure and calculation of precision and recall. Findings – The experiment illustrates the superiority of abstracts obtained through the assistance of ontology-based techniques. Originality/value – The authors were able to corroborate that the summaries obtained using Texminer are more efficient than those derived through other systems whose summarization models do not use ontologies to summarize texts. Thanks to ontologies, main sentences can be selected with a broad rhetorical structure, especially for a specific knowledge domain.