scholarly journals A Methodology for an Auto-Generated and Auto-Maintained HL7 FHIR OWL Ontology for Health Data Management

2021 ◽  
Author(s):  
Vassilis Kilintzis ◽  
Vasileios C. Alexandropoulos ◽  
Nikolaos Beredimas ◽  
Nicos Maglaveras
Keyword(s):  
Data Management ◽  
Health Data ◽  
Semantic Model ◽  
Web Ontology Language ◽  
Complete Conversion ◽  
Data Types ◽  
Ontology Language ◽  
Hl7 Fhir ◽  
Health Level Seven ◽  
Owl Ontology

The process of maintenance of an underlying semantic model that supports data management and addresses the interoperability challenges in the domain of telemedicine and integrated care is not a trivial task when performed manually. We present a methodology that leverages the provided serializations of the Health Level Seven International (HL7) Fast Health Interoperability Resources (FHIR) specification to generate a fully functional OWL ontology along with the semantic provisions for maintaining functionality upon future changes of the standard. The developed software makes a complete conversion of the HL7 FHIR Resources along with their properties and their semantics and restrictions. It covers all FHIR data types (primitive and complex) along with all defined resource types. It can operate to build an ontology from scratch or to update an existing ontology, providing the semantics that are needed, to preserve information described using previous versions of the standard. All the results based on the latest version of HL7 FHIR as a Web Ontology Language (OWL-DL) ontology are publicly available for reuse and extension.

Ontology-Based Travel Recommender System

2021 ◽  
pp. 460-476
Author(s):  
Shi Pu ◽  
Isibor Kennedy Ihianle
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Web Ontology Language ◽  
Satisfaction Rate ◽  
Semantic Data ◽  
Ontology Language ◽  
Overall Evaluation ◽  
Owl Ontology ◽  
Travel Recommendation

Recommender systems are designed to suggest information to users according to their preferences. The items could be movies, books, or various kinds of products. Most of the existing recommender systems are based on a database with limited advantages. However, in this chapter, the authors propose a knowledge-driven travel recommender system to integrate semantic data built using web ontology language (OWL) ontology to allow users to find suitable destinations that fulfil users' travel preferences. This work aims to develop a travel recommendation tool and to examine the reliability, the usability of the system, and satisfaction rate of users. They are also able to demonstrate that users can obtain desired results through queries on the ontology-based system. The overall evaluation of the system shows that users are happy and satisfied with the recommendation results.

scholarly journals RDB2OWL: AN IMPROVED METHOD FOR CONVERTING RELATIONAL DATABASES INTO OWL

2017 ◽  
Vol 7 (2) ◽  
pp. 129
Author(s):  
Phạm Thị Thu Thúy
Keyword(s):  
Semantic Web ◽  
Relational Databases ◽  
Web Ontology Language ◽  
Improved Method ◽  
Ontology Language ◽  
Owl Ontology

Một trong những lợi thế của Semantic Web là để mô tả dữ liệu với một ý nghĩa rõ ràng và liên kết giữa các dữ liệu bằng cách sử dụng ngôn ngữ OWL (Web Ontology Language). Ngày nay hầu hết các dữ liệu được lưu trữ trong cơ sở dữ liệu quan hệ. Để tận dụng lại các dữ liệu này, cần thiết phải có phương pháp chuyển dữ liệu lưu trữ trong cơ sở dữ liệu quan hệ vào định dạng của OWL Ontology. Một số phương pháp đã được đề xuất, tuy nhiên, hầu hết các quy tắc chuyển đổi đã không được hoàn chỉnh. Bài báo này đề xuất một số quy tắc cải thiện trong việc chuyển đổi cơ sở dữ liệu quan hệ sang OWL Ontology. Ngoài ra, tất cả các bước chuyển đổi trong thuật toán RDB2OWL được thực hiện tự động mà không cần bất kỳ sự can thiệp của người dùng.

scholarly journals Algorithm of Ontology Transformation to Concept Map for Usage in Semantic Web Expert System

2013 ◽  
Vol 14 (1) ◽  
pp. 80-87
Author(s):  
Olegs Verhodubs ◽  
Janis Grundspenkis
Keyword(s):  
Expert System ◽  
Semantic Web ◽  
Knowledge Base ◽  
Concept Map ◽  
Web Ontology Language ◽  
Subsequent Generation ◽  
Ontology Language ◽  
Owl Ontology ◽  
System Knowledge

Abstract The main purpose of this paper is to present an algorithm of OWL (Web Ontology Language) ontology transformation to concept map for subsequent generation of rules and also to evaluate the efficiency of this algorithm. These generated rules are necessary to supplement and even to develop SWES (Semantic Web Expert System) knowledge base. This paper is a continuation of the earlier research of OWL ontology transformation to rules.

scholarly journals Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1747 ◽  
Author(s):  
Cong Peng ◽  
Prashant Goswami
Keyword(s):  
Home Environment ◽  
Open Data ◽  
Health Data ◽  
Self Management ◽  
Formal Ontology ◽  
Data Interoperability ◽  
Semantic Web Technologies ◽  
Ontology Language ◽  
Integration Architecture ◽  
Hl7 Fhir

The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.

scholarly journals Application of Bayesian networks to generate synthetic health data

2020 ◽  
Author(s):  
Dhamanpreet Kaur ◽  
Matthew Sobiesk ◽  
Shubham Patil ◽  
Jin Liu ◽  
Puran Bhagat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Data Privacy ◽  
Statistical Tests ◽  
Synthetic Data ◽  
Original Data ◽  
Health Data ◽  
Minimal Risk ◽  
Data Types ◽  
Automated Method

Abstract Objective This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data. Materials and Methods We employed Bayesian networks to learn probabilistic graphical structures and simulated synthetic patient records from the learned structure. We used the University of California Irvine (UCI) heart disease and diabetes datasets as well as the MIMIC-III diagnoses database. We evaluated our method through statistical tests, machine learning tasks, preservation of rare events, disclosure risk, and the ability of a machine learning classifier to discriminate between the real and synthetic data. Results Our Bayesian network model outperformed or equaled medBGAN in all key metrics. Notable improvement was achieved in capturing rare variables and preserving association rules. Discussion Bayesian networks generated data sufficiently similar to the original data with minimal risk of disclosure, while offering additional transparency, computational efficiency, and capacity to handle more data types in comparison to existing methods. We hope this method will allow healthcare organizations to efficiently disseminate synthetic health data to researchers, enabling them to generate hypotheses and develop analytical tools. Conclusion We conclude the application of Bayesian networks is a promising option for generating realistic synthetic health data that preserves the features of the original data without compromising data privacy.

tOWL: A Temporal Web Ontology Language

2012 ◽  
Vol 42 (1) ◽  
pp. 268-281 ◽  
Author(s):  
V. Milea ◽  
F. Frasincar ◽  
U. Kaymak
Keyword(s):  
Web Ontology Language ◽  
Ontology Language
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