Ontological Modeling Approach Research Articles

A modular ontology modeling approach to developing digital product passports to promote circular economy in the built environment

The significant impact of the built environment on resource consumption and waste production has led to calls for a shift towards a circular economy model that maximizes the efficient use of resources. This study explores the use of digital product passports (DPPs) to improve how we manage products throughout their lifecycle. However, dealing with the complexity and large volume of data in DPPs can be challenging in terms of effective information management and utilization. We address this issue by adopting a modular ontological approach to systematically capture product lifecycle information from its origin to its end-of-life phase. To ensure interoperability and reusability of the ontology, we annotate key concepts and relationships using International Organization for Standardization (ISO) standards that promote circular economy. Our research led to the development of several ontology modules derived from literature reviews and interviews conducted with industry and academia experts who specialize in sustainability. These modules were then integrated to create a digital product passport ontology. The study demonstrates the feasibility of using a modular ontology approach to manage the complex information inherent in DPPs paving the way for more sustainable management practices in the built environment sector.

An Ontological Behavioral Modeling Approach With SHACL, SPARQL, and RDF Applied to Smart Grids

An Ontological Behavioral Modeling Approach With SHACL, SPARQL, and RDF Applied to Smart Grids

Fostering Cyber-Physical Social Systems through an Ontological Approach to Personality Classification Based on Social Media Posts.

The exponential increase in social networks has led to emergent convergence of cyber-physical systems (CPS) and social computing, accelerating the creation of smart communities and smart organizations and enabling the concept of cyber-physical social systems. Social media platforms have made a significant contribution to what we call human behavior modeling. This paper presents a novel approach to developing a users’ segmentation tool for the Romanian language, based on the four DISC personality types, based on social media statement analysis. We propose and design the ontological modeling approach of the specific vocabulary for each personality and its mapping with text from posts on social networks. This research proposal adds significant value both in terms of scientific and technological contributions (by developing semantic technologies and tools), as well as in terms of business, social and economic impact (by supporting the investigation of smart communities in the context of cyber-physical social systems). For the validation of the model developed we used a dataset of almost 2000 posts retrieved from 10 social medial accounts (Facebook and Twitter) and we have obtained an accuracy of over 90% in identifying the personality profile of the users.

A Multidomain Layered Approach in Development of Industrial Ontology to Support Domain Identification for Unstructured Text

Due to the emergence of digital revolution and competitiveness in recent decades, almost all organizations and industries intend to develop solutions to extract information from unstructured documents. These documents comprise of information related to multiple divergent domains, and therefore, there is a need of a multidomain knowledge base. Since recent research works suggest ontology as the predominant model, it is proposed to evolve a unified ontology modeling approach with multiple layers and divergent domains to support information processing from unstructured documents. The model is evolved by integrating relevant domains to facilitate cross domain query. Further as the features of unstructured documents span across multiple domains, domain identification is to be performed prior to any information processing. Hence, an attempt is made to identify the domain using the proposed ontology model. The proposed ontology is developed for the Thermal Power Plant Industry and domain identification is demonstrated with an example. A statistical similarity index is proposed to associate divergent volatile features of unstructured text with ontology knowledge for domain identification. The outcome of the proposal is evaluated using the proposed similarity index. A subsequent study to extract information using classified content with the support of directed acyclic graph relationship is under progress. The merit of the proposal is its ability to extend its usage across multiple stages of information processing with distinctive purpose.

Know-Ont Based Ontology Modeling Approach for Skill Knowledge Extraction

Know-Ont Based Ontology Modeling Approach for Skill Knowledge Extraction

Trajectory ontology inference considering domain and temporal dimensions—Application to marine mammals

Capture devices rise large scale trajectory data from moving objects. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Huge trajectory data are available today. In this paper, we use an ontological data modeling approach to build a trajectory ontology from such large data. To accomplish reasoning over trajectories, the ontology must consider mobile object, domain and other knowledge. In our approach, we suggest expressing this knowledge in the form of rules. To annotate data with these rules, we need an inference mechanism over trajectory ontology. Experiments over our model using domain and temporal rules address an inference computation complexity. This complexity has two important factors: time computations and space storage. In order to reduce the inference complexity, we proposed optimizations, such as domain constraints and temporal neighbor refinements. In this paper, we define a refinement specifically for the application domain. Then, we evaluate our contribution over real trajectory data. Finally, the results show the positive impact of the last refinement on reducing the complexity of the inference mechanism. This refinement reduces half of the time computation and then allows considering larger data sets.

OntoSOC: Sociocultural Knowledge Ontology

This paper presents a sociocultural knowledge ontology (OntoSOC) modeling approach. OntoSOC modeling approach is based on Engestrom Human Activity Theory (HAT). That Theory allowed us to identify fundamental concepts and relationships between them. The top-down precess has been used to define differents sub-concepts. The modeled vocabulary permits us to organise data, to facilitate information retrieval by introducing a semantic layer in social web platform architecture, we project to implement. This platform can be considered as a collective memory and Participative and Distributed Information System (PDIS) which will allow Cameroonian communities to share an co-construct knowledge on permanent organized activities.

An ontological modeling approach for abnormal states and its application in the medical domain.

BackgroundRecently, exchanging data and information has become a significant challenge in medicine. Such data include abnormal states. Establishing a unified representation framework of abnormal states can be a difficult task because of the diverse and heterogeneous nature of these states. Furthermore, in the definition of diseases found in several textbooks or dictionaries, abnormal states are not directly associated with the corresponding quantitative values of clinical test data, making the processing of such data by computers difficult.ResultsWe focused on abnormal states in the definition of diseases and proposed a unified form to describe an abnormal state as a “property,” which can be decomposed into an “attribute” and a “value” in a qualitative representation. We have developed a three-layer ontological model of abnormal states from the generic to disease-specific level. By developing an is-a hierarchy and combining causal chains of diseases, 21,000 abnormal states from 6000 diseases have been captured as generic causal relations and commonalities have been found among diseases across 13 medical departments.ConclusionsOur results showed that our representation framework promotes interoperability and flexibility of the quantitative raw data, qualitative information, and generic/conceptual knowledge of abnormal states. In addition, the results showed that our ontological model have found commonalities in abnormal states among diseases across 13 medical departments.

Personalized Mobile Catering Recommender System Based on Context Ontology Model and Rule Inference

With the development of smart mobile terminals and pervasive computing, mobile recommender systems are proposed to realize context-aware personalized recommendation services. A context model plays a key role in a mobile recommender. We discussed the context ontology modeling approach specific for mobile recommendation, and developed a two-level context model including a upper ontology and a domain ontology. We also designed a personalized mobile catering recommender system based on the context ontology model and rule inference. The framework of the system is depicted. And the process of rule generation and rule inference based on the context ontologies is demonstrated.

Constructing a New-Style Conceptual Model of Brain Data for Systematic Brain Informatics

The development of brain science has led to a vast increase of brain data. To meet requirements of a systematic methodology of Brain Informatics (BI), this paper proposes a new conceptual model of brain data, namely Data-Brain, which explicitly represents various relationships among multiple human brain data sources, with respect to all major aspects and capabilities of human information processing systems (HIPS). A multidimension framework and a BI methodology-based ontological modeling approach have been developed to implement a Data-Brain. The Data-Brain, Data-Brain-based BI provenances, and heterogeneous brain data can be used to construct a Data-Brain-based brain data center which provides a global framework to integrate data, information, and knowledge coming from the whole research process for systematic BI study. Such a Data-Brain modeling approach represents a radically new way for domain-driven conceptual modeling of brain data, which models a whole process of systematically investigating human information processing mechanisms.

An ontological modeling approach to cerebrovascular disease studies: The NEUROWEB case

The NEUROWEB project supports cerebrovascular researchers’ association studies, intended as the search for statistical correlations between a feature (e.g., a genotype) and a phenotype. In this project the phenotype refers to the patients’ pathological state, and thus it is formulated on the basis of the clinical data collected during the diagnostic activity. In order to enhance the statistical robustness of the association inquiries, the project involves four European Union clinical institutions. Each institution provides its proprietary repository, storing patients’ data. Although all sites comply with common diagnostic guidelines, they also adopt specific protocols, resulting in partially discrepant repository contents. Therefore, in order to effectively exploit NEUROWEB data for association studies, it is necessary to provide a framework for the phenotype formulation, grounded on the clinical repository content which explicitly addresses the inherent integration problem. To that end, we developed an ontological model for cerebrovascular phenotypes, the NEUROWEB Reference Ontology, composed of three layers. The top-layer ( Top Phenotypes) is an expert-based cerebrovascular disease taxonomy. The middle-layer deconstructs the Top Phenotypes into more elementary phenotypes ( Low Phenotypes) and general-use medical concepts such as anatomical parts and topological concepts. The bottom-layer ( Core Data Set, or CDS) comprises the clinical indicators required for cerebrovascular disorder diagnosis. Low Phenotypes are connected to the bottom-layer (CDS) by specifying what combination of CDS values is required for their existence. Finally, CDS elements are mapped to the local repositories of clinical data. The NEUROWEB system exploits the Reference Ontology to query the different repositories and to retrieve patients characterized by a common phenotype.

ONTOLOGICAL MACHINING PROCESS DATA MODELLING FOR POWERTRAIN PRODUCTION IN EXTENDED ENTERPRISE

As a new CAx concept, Digital Factory points out digital design of product & virtual planning of production. In this context, feature technology builds up the entire CAx process chain in Digital Factory, and facilitates STEP based engineering data generation and exchange. The collaborative computer-aided machining process planning of a transfer line is an element in process chain of extended enterprises Automotive Engineering. In practice, it is an approach engaged by both OEMs and their long-term machine suppliers. Application of a CAD-based Digital Factory planning system to design a virtual transfer line needs planning standard data and exchange with supplier. This paper highlights STEP-referenced ontological data modeling approach in the standardized data exchange for collaborative machining process planning, discusses the aspects of its prospective application advantages in machining process planning by OEMs and suppliers for Powertrain production in Automotive Industry.