Ontology-based Methodology Research Articles

Advancing radiation therapy safety in cancer-care: Leveraging AI for adverse reaction prediction

The development of advanced technologies for radiation cancer treatment plays a pivotal role in anticipating potential complications, safeguarding patient well-being, and elevating treatment satisfaction. Access to curated medical data coupled with specialized knowledge in radiation oncology is indispensable for equipping healthcare professionals with the insights needed to make informed treatment decisions. This study endeavors to provide systematic support to practitioners in predicting personalized side effects of radiation oncology treatments before their administration to patients. For this, a pioneering ontology-based methodology for evidence-based medicine is introduced, drawing upon information and evidence from various scientific outlets. This approach utilizes semantic modeling and rule-based reasoning to assess radiation therapy pathways, ensuring adjustments are made when necessary to mitigate health risks and enhance safety. Focused on radiation oncology, the intricacies of knowledge are simplified and validated through real reported cases obtained from scientific literature. The resulted predictive model demonstrated a notable compatibility rate of 76.7% with real reported risks, surpassing conventional methods by 4% during testing achieves 89% success with single therapies but faces challenges with complex regimens. Data enrichment may increase accuracy by 20% over five years. Current method accuracy is 92%, with potential for 97% through quantum-inspired methods. Testing reveals 80.1% compatibility, exceeding conventional approaches by 3%. These findings hold significant potential of ontology-based models for revolutionizing radiation oncology treatment strategies in the realm of radiation protection.

Ontology-based methodology for the intelligent detection of product manufacturing information semantic representation errors in the model-based definition

Ontology-based methodology for the intelligent detection of product manufacturing information semantic representation errors in the model-based definition

The Balinese Lontar Manuscript Metadata Model: An Ontology-Based Approach

The digitization of cultural heritage, particularly lontar manuscripts, is a focus of research to aid stakeholders in the management of lontar metadata. Managing the metadata of Balinese Lontar manuscripts through the application of ontology is one method for ensuring the preservation and accessibility of Balinese Lontar manuscripts. This research aims to apply ontology technology to the preservation of the cultural heritage of Balinese lontar manuscripts so that all information about lontar details can be categorized according to their respective properties and relevant information can be presented based on the user's preferences. This research method employs the stages of the Design Science Research Methodology (DSRM) when devising the ontology for the Balinese lontar manuscript. The results demonstrated that the construction of metadata using an ontology-based methodology can provide the information required to describe, categorize, and connect ontology entities. Metadata consists of entity descriptions, hierarchies and classifications, relationships and properties, as well as the necessary semantics for constructing effective ontologies

An ontology-based methodology to establish city information model of digital twin city by merging BIM, GIS and IoT

With the development of digital city and smart city construction, the City Information Model (CIM) has played a critical role as a container of spatial–temporal data to establish the Digital Twin City. For a digital twin city, a virtual high-fidelity CIM model that corresponds closely to the real physical world is the premise and cornerstone of its construction. Therefore, the integration of BIM, GIS and IoT has become the preferred topic for researchers and has received much more attention from a wide academic circle. However, traditional integration mainly focuses on the conversion of both IFC and CityGML, and IoT data are also often used as visualizations. More importantly, the underlying data formats of GIS, BIM and IoT are still independent of each other without a unified data structure expression, so real data-driven analysis and decision-making cannot be implemented. This study aims to establish a general CIM ontology to integrate heterogeneous BIM, GIS and IoT data. First, the related work of BIM, GIS and IoT integration is studied and analyzed. A comparison of three mainstream approaches, data conversion, standard extension and data linking, is conducted, and it illustrates the advantages of ontology techniques in solving data interoperability problems. Second, a technical framework of BIM, GIS and IoT data integration based on ontology technology is proposed. The approach is mainly divided into five steps: geometry processing, data instantiation, ontology construction, ontology mapping and querying application. On the basis of the CIM ontology, an application ontology is built for a specific application domain to illustrate rule-based mapping, querying and inferring. Finally, the case study shows that the Ontology-based methodology in this paper has contributed to establish a general pattern for CIM data integration by mapping and linking concepts from the semantic level. It avoids changes in the original data sources and the missing data problem.

An Efficient Ontology Based Machine Learning for Classifying High Definition Satellite Images (Chandrayaan-3)

The analysis of satellite images to determine the kind of land, forest cover, plant type, and other aspects of a particular image has become a standard practice. Object-based image classification for land-cover mapping with remote-sensing data has received a lot of interest in recent years. Several researches have looked into a range of sensors, feature extraction, classifiers, and other important elements throughout the last decade. These data, however, have yet to be gathered into a complete reference on the implications of different guided object-based land-cover categorization methods. To solve this issue, we provide an ontology-based conceptual UKNN classification algorithm in this study. We create an 18-field database from the rit18 database's qualitative and quantitative data in this research. Instead of unexplainable characteristics provided by a CNN, this proposed study uses an ontology-based methodology framework to enable picture classification using features extracted from hyper spectral image data. The image is initially given a Gaussian filter. The suggested UNet K nearest neighbours (UKNN) classifier is utilised to translate the ontology components into image-based parameters, which are then used to improve the classification process. When a typical support vector machine (SVM) is compared to UKNN, we find that UKNN outperforms SVM in classification accuracy

Ontology-Based Methodology for Knowledge Acquisition from Groupware

Groupware exist, and they contain expertise knowledge (explicit and tacit) that is primarily for solving problems, and it is collected on-the-job through virtual teams; such knowledge should be harvested. A system to acquire on-the-job knowledge of experts from groupware in view of the enrichment of intelligent agents has become one of the important technologies that is very much in demand in the field of knowledge technology, especially in this era of textual data explosion including due to the ever-increasing remote work culture. Before acquiring new knowledge from sentences in groupware into an existing ontology, it is vital to process the groupware discussions to recognise concepts (especially new ones), as well as to find the appropriate mappings between the said concepts and the destination ontology. There are several mapping procedures in the literature, but these have been formulated on the basis of mapping two or more independent ontologies using concept-similarities and it requires a significant amount of computation. With the goal of lowering computational complexities, identification difficulties, and complications of insertion (hooking) of a concept into an existing ontology, this paper proposes: (1) an ontology-based framework with changeable modules to harvest knowledge from groupware discussions; and (2) a facts enrichment approach (FEA) for the identification of new concepts and the insertion/hooking of new concepts from sentences into an existing ontology. This takes into consideration the notions of equality, similarity, and equivalence of concepts. This unique approach can be implemented on any platform of choice using current or newly constructed modules that can be constantly revised with enhanced sophistication or extensions. In general, textual data is taken and analysed in view of the creation of an ontology that can be utilised to power intelligent agents. The complete architecture of the framework is provided and the evaluation of the results reveal that the proposed methodology performs significantly better compared to the universally recommended thresholds as well as the existing works. Our technique shows a notable high improvement on the F1 score that measures precision and recall. In terms of future work, the study recommends the development of algorithms to fully automate the framework as well as for harvesting tacit knowledge from groupware.

Understanding the Gap Between Information Models and Realism-Based Ontologies Using the Generic Component Model.

The wide-spread use of Common Data Models and information models in biomedical informatics encourages assumptions that those models could provide the entirety of what is needed for knowledge representation purposes. Based on the lack of computable semantics in frequently used Common Data Models, there appears to be a gap between knowledge representation requirements and these models. In this use-case oriented approach, we explore how a system-theoretic, architecture-centric, ontology-based methodology can help to better understand this gap. We show how using the Generic Component Model helps to analyze the data management system in a way that allows accounting for data management procedures inside the system and knowledge representation of the real world at the same time.

An Ontology-Based Framework for Complex Urban Object Recognition through Integrating Visual Features and Interpretable Semantics

Although previous works have proposed sophisticatedly probabilistic models that has strong capability of extracting features from remote sensing data (e.g., convolutional neural networks, CNN), the efforts that focus on exploring the human’s semantics on the object to be recognized are required more explorations. Moreover, interpretability of feature extraction becomes a major disadvantage of the state-of-the-art CNN. Especially for the complex urban objects, which varies in geometrical shapes, functional structures, environmental contexts, etc, due to the heterogeneity between low-level data features and high-level semantics, the features derived from remote sensing data alone are limited to facilitate an accurate recognition. In this paper, we present an ontology-based methodology framework for enabling object recognition through rules extracted from the high-level semantics, rather than unexplainable features extracted from a CNN. Firstly, we semantically organize the descriptions and definitions of the object as semantics (RDF-triple rules) through our developed domain ontology. Secondly, we exploit semantic web rule language to propose an encoder model for decomposing the RDF-triple rules based on a multilayer strategy. Then, we map the low-level data features, which are defined from optical satellite image and LiDAR height, to the decomposed parts of RDF-triple rules. Eventually, we apply a probabilistic belief network (PBN) to probabilistically represent the relationships between low-level data features and high-level semantics, as well as a modified TanH function is used to optimize the recognition result. The experimental results on lacking of the training process based on data samples show that our proposed approach can reach an accurate recognition with high-level semantics. This work is conducive to the development of complex urban object recognition toward the fields including multilayer learning algorithms and knowledge graph-based relational reinforcement learning.

Existing Business Process Models for Model Construction of Multidimensional Business Knowledge: An MDA-Based Transformation Methodology

Owing to the absolute significance of the business process management, companies have become thoroughly oriented toward a good modeling of the business processes. Although the business process modeling is a substantial part in workflow automation, business process designers often misunderstand domain concepts or relationships due to their lack of precise business knowledge. Such a semantic equivocation impinges the efficiency and quality of business process modeling. For the purpose of addressing this issue, a multidimensional business ontology-based approach is conceived to remove the semantic ambiguity. In this regard, a multidimensional business knowledge model is proposed so as to get rich and semantic information through business process existing models. The construction of the model proposed is set up in accordance with an MDA-based transformation methodology referring to business process models to define business concepts and to determine the related semantic relationships. Eventually, in terms of completeness and clarity, the model suggested is assessed, using an ontology-based analysis methodology.

Big Data and Automatic Detection of Topics: Social Network Texts

This paper proposes the analysis of the influence of terms that express feelings in the automatic detection of topics in social networks. This proposal uses an ontology-based methodology which incorporates the ability to identify and eliminate those terms that present a sentimental orientation in social network texts, which can negatively influence the detection of topics. To this end, two resources were used to analyze feelings in order to detect these terms. The proposed system was evaluated with real data sets from the Twitter and Facebook social networks in English and Spanish respectively, demonstrating in both cases the influence of sentimentally oriented terms in the detection of topics in social network texts.

An ontology-based methodology for hazard identification and causation analysis

This article presents a dynamic hazard identification methodology founded on an ontology-based knowledge modeling framework coupled with probabilistic assessment. The objective is to develop an efficient and effective knowledge-based tool for process industries to screen hazards and conduct rapid risk estimation. The proposed generic model can translate an undesired process event (state of the process) into a graphical model, demonstrating potential pathways to the process event, linking causation to the transition of states. The Semantic web-based Web Ontology Language (OWL) is used to capture knowledge about unwanted process events. The resulting knowledge model is then transformed into Probabilistic-OWL (PR-OWL) based Multi-Entity Bayesian Network (MEBN). Upon queries, the MEBNs produce Situation Specific Bayesian Networks (SSBN) to identify hazards and their pathways along with probabilities. Two open-source software programs, Protégé and UnBBayes, are used. The developed model is validated against 45 industrial accidental events extracted from the U.S. Chemical Safety Board's (CSB) database. The model is further extended to conduct causality analysis.

Service-Oriented Context-Aware Messaging System

In services oriented computing, location or spatial models are required to model the domain environment whenever location or spatial relationships are utilised by users and/or services. This research presents an ontology-based methodol-ogy for context-aware messaging service. There are five main contributions to this research. First, the research provides a service oriented methodology for modelling and building context-aware messaging systems based on ontological principles. Second, it describes a method that assists understanding the domain’s spatial environment. Third, it includes a proposal of the generic Mona-ServOnt core service ontology that offers context-aware reasoning for capture and use of context. Mona-ServOnt is able to support the deployment of context-aware messaging services in both indoor and outdoor environments. Fourth, a novel generic architecture that captures the requirements for context-aware messaging services is given. Fifth, the generic messaging protocols that describe the exchange of messages within context-aware mes-saging services is modelled. A few experiments were completed to measure the performance of the peer-to-peer services using actual smartphone with Bluetooth capability. In addition, the methodology’s main steps have been validated individually in various context-aware messaging domains. It has been evaluated using competency questions that gauge the scope of the proposed ontology. Furthermore, the generic architecture and messaging protocols have been verified in constructing for each domain.

An ontology-based hybrid methodology for image synthesis and identification with convex objects

ABSTRACTOne of the core challenges in developing a computer system for machine learning is to make the system learn efficiently and effectively like a real human by grasping the domain knowledge exemplified by human experts. In this challenge, we have introduced a hybrid image synthesis model that can simulate one of the human’s learning capabilities in the vision field – the ability to synthesize images of convex objects by identifying solid geometries and textures of specific objects using few photographs. We have incorporated an ontology-based, domain knowledge on solid geometries into our model to synthesize large number of training images with only a minimum number of input images. Our initial experiments have shown that our model has convincing improvements by demonstrating a substantially better FAR/FRR/EER results when it is compared with a smaller set of non-synthetic images.

The Ontology-Based Methodology Phases To Develop Multi-Agent System (OmMAS)

Semantic aspect on methodology phase is a significant issue to develop multi-agent system in the current days. There are a lot of methodologies to develop multi-agent system, but the current problem is how to choose the best methodology phase to develop current multi-agent system. The development of multi-agent system currently is to be more complex and difficult. Many aspects that contains on multi-agent system, the one of the famous issue now is about semantic aspect on multi-agent system. The old methodology phases are not suitable to develop current multi-agent system. Nowadays, many researchers start to improve and customize the obsolete methodology to adjust with the current needed. There are two research steps contains in this paper, the first step is to review and criticize previous methodologies especially about MOMA (Methodology for Developing Ontology-Based Multi-Agent System) was introduced in 2013. The second step is the main contribution of this paper is to improve previous methodology phases with the new methodology phases named OmMas (The Ontology-Based Methodology phases to Develop Multi-Agent System), and using semantic aspect as the main focus of this methodology. The result of this research is improved ontology-based methodology phases as a representation of semantic aspect on the ontology development process.

Керований онтологіею інструмент для застосування стилей програмування

Діяльність програміста буде більш ефективною, а програмне забезпечення зрозумілим коли при створенні програмного забезпечення застосовуються стилі (стандарти) програмування, які забезпечують зрозумілість програмних текстів. Мета: В цьому дослідженні представлено засіб для реалізації нового методу, який застосовує онтології і автоматизує процеси використання стилів програмування. Зокрема розглядається представлення стандартів в формі онтології і застосування ризонеру дескриптивної логіки для програміста. Метою статті є розробка засобу для підтримки керованого онтологією застосування стилів програмування. Методи дослідження: онтологічне представлення стилів програмування; об’єктно-орієнтоване програмування; кероване онтологією застосування стилів програмування. Результати: отримано архітектуру та мовою Java реалізовано засоби які забезпечують інструментальну підтримку методу керованого онтологією застосування стилів програмування. На прикладі іменування стандарту програмування мови Java надано особливості реалізації і застосування засобу. Обговорення: Застосування стандартів програмування при конструюванні програмного забезпечення, відсутність засобів автоматизації процесів застосування стандартів програмування, засіб на основі нового методу керованого онтологією застосування стилів програмування, приклад реалізації архітектури засобу для іменувань стандарту мови Java.

A goal-oriented, ontology-based methodology to support the design of AAL environments

One of the most critical issues in Ambient Assisted Living (AAL) is the design of systems that can evolve to meet the requirements of individuals as their needs and health conditions change. Although much work has been done on home and building automation systems for AAL, often referred to as assistive domotics, there is in fact still a substantial lack of solutions capable to support system designers in the early stage of development of such assistive systems. To this aim, the work contributes to the research on design of assistive domotic systems by presenting an ontology-driven methodology aimed to guide the development process. The novel contributions of the paper include the goal-oriented approach of the methodology, which involves the elicitation and analysis of AAL requirements and their formal representation in an ontology, where high-level goals are described in terms of subgoals and tasks, that are then linked to corresponding measures and devices. Moreover, logic-based reasoning enables more advanced functionalities useful at design time. We present a validation of the methodology showing typical use cases both related to the development from scratch of a domotic system with assistive capabilities starting from a set of high-level user requirements and the redesign of existing implementations according to changed requirements.

Supporting SW-FMEA through an ontology-based methodology

Failure mode and effects analysis FMEA is a widely used dependability and safety technique aiming at systematically identifying failure modes, their causes, their effects, and the countermeasures that could mitigate their impact. Although FMEA has been thought for hardware, its use is also advocated for software SW-FMEA. However, this involves several major hurdles such as the complexity of functional requirements and the difficulty to identify failure modes of SW components. We present an approach for efficient and effective manipulation of data involved in the SW-FMEA process, introducing an ontological model that formalises concepts comprised in the analysis and provides a common conceptual framework supporting cohesion across different stages of a development life-cycle. The ontological model opened the way to the implementation of a tool, which automates SW-FMEA providing support for basic and advanced functionalities, fastening the analysis. Use of the tool in a real SW development process is discussed at the end of the paper.

Ontology-Based Methodology for Information Integration and Service Discovery

Finding suitable information in the open and distributed environment of current simulation information is a crucial task. One of the main challenges is to overcome semantic heterogeneity. This paper draw on the ontology, Semantic Web and cloud computing, put forward an ontology-based methodology for simulation information integration and service discovery, using XML Schema to realize the formatting description of resources, using ontology to realize the formal description of resources, using cloud computing to build an information resources pool, adopting SOA and Web services to realize the automatic discovery, match and combination of information services. Finally, an example is tested to verify the feasibility of the methodology.

Semantic integration of gene expression analysis tools and data sources using software connectors.

BackgroundThe study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data.ResultsWe have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data.ConclusionsThe proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.

Ontology-Based Methodology for Managing Heterogeneous Wireless Sensor Networks

With the growing interest in ubiquitous technologies recently, many studies have been conducted in order to manage wireless sensor network, which is the central component in ubiquitous environment. To manage a certain wireless sensor network, a variety of techniques can be applied and thereby the network can be managed. However, it is very complicated and time-consuming to incorporate and manage more than a heterogeneous wireless sensor network since each network has different data, meanings, and terms. Instead of conducting verification through examining the detailed technology or simulation in order to manage a variety of wireless sensor networks, we suggest a methodology which manages data coming from the various sensors mentioned above, a technique by which those data from various sensor networks are managed in each different network, and a methodology which manages sensor networks where management languages exist. This paper defined the network-related terminology using Protégé for the tools and definitions necessary for ontology-based methodology, thereby defined OWL and the related rules, and suggested a methodology for incorporated management. In addition, this study showed that the methodology presented in this study has expandability and it can incorporate and manage a variety of wireless sensor networks.