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We have developed and implemented an ontology for an intelligent hospital ward. Our aim is to address the pervasiveness of computing applications in healthcare environments, which require: sharing of data across the hospital, including data generated by sensors and embedded in such environments, and dealing with semantic heterogeneity that exists a...
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... most common modeling construct which had to change, is the way we defined relationships between classes and their associated properties. We demonstrate this in Figure 4 where the 'PATIENT' class is the domain for its associated property 'previous treatment name'. This means that PATIENT class is also a super class in OWL. ...
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... Interoperability, the ability of systems to communicate with each other, both syntactically and semantically, remains a critical need for communication amongst programs . Because of the heterogeneous nature of software systems, in which data may be stored in different formats, knowledge sharing and communication are difficult to achieve (Kataria et al. 2008). A high-quality ontology can help with information sharing, and reusability and intercommunication between heterogeneous software systems, by providing a common vocabulary (W. ...
... Individual resources may then be mutually associated, resulting in the logical association of classes and instances. Semantic ontologies are becoming increasingly important because of their capabilities to provide a common representation of a domain among different users by linking concepts and instances, supporting interoperability between heterogeneous data archives, and fostering the reuse and sharing of knowledge [15]. ...
The healthcare environment is made up of highly complicated interactions between many technologies, activities, and people. Ensuring a solid communication between them is vital to ease the healthcare management. Semantic ontologies are knowledge representation tools that implement abstractions to fully describe a given topic in terms of subjects and relations. This scoping review aims to identify and analyse available ontologies which can depict all the available use-cases that describe the hospital environment in relation to the European project ODIN and its future expansion. The review has been conducted on the Scopus database on January 13th, 2023 using the PRISMA extensions for scoping reviews. Two reviewers screened 3,225 documents emerged from the database search. Further filtering led to a final set of 32 articles to be analysed for the results. A set of 34 ontologies extracted by the identified articles has been analysed and discussed as well. The results of this study will lead to the implementation of a common integrated ontology which could hold information about healthcare entities as well as their semantic relationships, strengthen data exchange and interconnections among people, devices and applications in an expanded scenario which include Internet of Things, robots and Artificial Intelligence.
... 15 The use of 'intelligence' in the period 2006-2010 intensifies the mixture of emerging terms (including 'business process', 'semantic management', 'information exchange' and 'context awareness') as the Google revolution truly transforms access to vast amounts of data and these concepts start to be applied in the digitalisation of patients' clinical records. 16 ...
The third industrial revolution has radically impacted the transformation of hospitals. Through the adoption of key digital technologies, hospitals have become more accessible, flexible, organised, responsive and able to deliver more personalised care. The digitalisation of patient health records, one of the most remarkable achievements to date in healthcare management, has enabled new opportunities, including the idea of hospitals evolving to become artificially intelligent. In parallel, the adoption of electronic and mobile internet technologies in hospitals has introduced new structural concepts, seeing a variety of terms blossom such as 'smart', 'intelligent', 'green' and 'liquid'. Now in the early fourth industrial revolution, driven by AI and internet-of-things technologies, this article unveils a new concept adapted to the upcoming era.
... TopBraid [51] is a tool for industry experts for the creation, maintenance, and management of RDF, RDFS, and OWL ontology in semantic web. TopBraid is a knowledge base model and ontology editor that offers graphic editing support, also compatibility with databases and XML. ...
Ontology originated in a branch of artificial intelligence and widely used in various fields such as semantic web, engineering systems, software engineering, biomedical informatics, library science, information engineering, knowledge management, decision-making system, and the industry sector. Ontology supports the knowledge management system and helps get significant features of product design. The lack of using knowledge is the critical challenge in information interoperability of the manufacture, especially, in the early stage of design. This information must be gathered, stored, shared, reused, and managed in a consistent and standardized way. The aim of this study is to use the ontology for reducing the complexity of information and increasing its organization, facilitating sharing and reusing of information, and improving its accuracy. The use of ontology has shown optimistic results to support comprehensive decisions in the industrial field. The researchers have confirmed the importance of using the ontology to improve interoperability over the product life cycle and address the impacts of products through the outputs of the product life cycle assessment tool. This study is expected to contribute to develop an efficient and practicable sustainability tool during product design with a complete view to solve the lack of sharing information in the product life cycle, provide high quality and comprehensive recommendations to support the manufacturing processes for product sustainability.
... Interoperability, the ability of systems to communicate with each other, both syntactically and semantically, remains a critical need for communication amongst programs . Because of the heterogeneous nature of software systems, in which data may be stored in different formats, knowledge sharing and communication are difficult to achieve (Kataria et al. 2008). A high-quality ontology can help with information sharing, and reusability and intercommunication between heterogeneous software systems, by providing a common vocabulary (W. ...
The number of applications being developed that require access to knowledge about the real world has increased rapidly over the past two decades. Domain ontologies, which formalize the terms being used in a discipline, have become essential for research in areas such as Machine Learning, the Internet of Things, Robotics, and Natural Language Processing, because they enable separate systems to exchange information. The quality of these domain ontologies, however, must be ensured for meaningful communication. Assessing the quality of domain ontologies for their suitability to potential applications remains difficult, even though a variety of frameworks and metrics have been developed for doing so. This article reviews domain ontology assessment efforts to highlight the work that has been carried out and to clarify the important issues that remain. These assessment efforts are classified into five distinct evaluation approaches and the state of the art of each described. Challenges associated with domain ontology assessment are outlined and recommendations are made for future research and applications.
... In most publications ontologies are used for information extraction from text [19][20][21], or to enrich the existing data with structural and semantic information, or to build a cross-institutional knowledge base. In [22] the authors describe the usage of ontologies for inter-hospital data extraction to improve patient care and safety by analyzing hospital activities, procedures, and policies. Here, the ontology is strongly connected to the project. ...
In contrast to traditional, industrial applications such as market basket analysis, the process of knowledge discovery in medical research is mostly performed by the medical domain experts themselves. This is mostly due to the high complexity of the research domain, which requires deep domain knowledge. At the same time, these domain experts face major obstacles in handling and analyzing their high-dimensional, heterogeneous, and complex research data. In this paper, we present a generic, ontology-centered data infrastructure for scientific research which actively supports the medical domain experts in data acquisition, processing and exploration. We focus on the system’s capabilities to automatically perform dimensionality reduction algorithms on arbitrary high-dimensional data sets and allow the domain experts to visually explore their high-dimensional data of interest, without needing expert IT or specialized database knowledge.
... In most publications ontologies are used for information extraction from text [10] or to enrich the existing data with structural and semantic information or to build a cross-institutional knowledge base. In [11] the authors describe the usage of ontologies for inter-hospital data extraction to improve patient care and safety by analyzing hospital activities, procedures, and policies. Here, the ontology is strongly connected to the project. ...
Medical research but also quality management is based upon medical data. The integration, validation, processing, and exploration of this data is known to be a technical obstacle for researching medical domain experts and a major pitfall to (bio-)medical research projects. To overcome this pitfall and actively support the medical domain expert in these tasks, we present an ontology-based clinical data warehouse for scientific research. It is completely generic and adapts itself at run-time to the current domain-ontology, which can be freely defined by the domain expert and describes the actual field of research. The whole system adapts is appearance and behavior to this central ontology and appears to the user like a custom made solution. Furthermore, the elaborate structural meta-information from the ontology is used to actively support the user in tasks that usually require profound IT knowledge, such as defining complex search queries or data quality constraints, or applying advanced data visualization algorithms to the data. The proposed warehouse supports the domain expert trough the whole process of knowledge discovery from data integration to exploration.
... In Hospital Intelligent Ward Ontology (HIWO) problem of sharing data across hospital departments, wards and administrator is addressed. It also describes the interoperability difficulty that arises from semantic heterogeneities [28][78]. ...
Traditional medicines can play a major role in global health care, due to its indigenous nature, easy access and cost effectiveness. However, knowledge of this intellectual property is in danger of being lost, due to multiple factors. It is either local in context or undocumented for which reason World Health Organization signifies the necessity to preserve and maintain this knowledge. Unani Medicines, a subfield of Traditional Medicines, has been continuously being practiced in Asia for about 2500 years, and it is facing the same situation. To preserve knowledge of Unani Medicines, initial kind of an effort has been done but a formal machine reusable and readable semantic structure is required to conserve this knowledge efficiently and effectively. This research focuses on conceptual structure of Unani Medicines by presenting domain ontology which includes core principles and philosophy of Unani Medicines, patient's symptoms, diagnosis, diseases, treatment and drugs. Knowledge about fundamentals is captured from expert interviews and books and then this knowledge is converted into semantic format using Protégé. Although it is not an exhaustive ontology, however it may serve as a backbone for any Knowledge Based Application of Unani Medicines.
... In [24], Kataria et al. describe the implementation of an ontology for intelligent hospital wards. It is used to deal with the semantic heterogeneity across different data repositories in a hospital. ...
... In most publications ontologies are used for information extraction from text (Tran and Kameyama, 2007 ) or to enrich the existing data with structural and semantic information or to build a cross-institutional knowledge base. In (Kataria et al., 2008) the authors describe the usage of ontologies for inter-hospital data extraction to improve patient care and safety by analyzing hospital activities , procedures, and policies. Here, the ontology is strongly connected to the project. ...
We created an ontology-based data acquisition infrastructure which is able to store data of almost arbitrary
structure and can be set up for a certain domain of application within hours. An ontology editor helps the
domain expert to define and maintain the domain specific ontology. Based on the user-defined ontology, a
web-based data acquisition system and an ETL data import interface are automatically created at runtime.
Furthermore, rules for semantic data plausibility can be established in the ontology to provide semantic data
quality for subsequent processing of the collected data. After a comprehensive requirement analysis we decided
to use a special meta model instead of standard OWL ontologies. In this paper, we describe our metamodel
and the reason for not using OWL in our case in detail as well as we present the infrastructure and the
project it is currently used for.
