Biomedical ontologies and their development, management, and applications in and beyond China

Pan, Hongjie; Zhu, Yan; Yang, Sheng; Wang, Zhigang; Zhou, Wei; He, Yongqun; Yang, Xiaolin

doi:10.1097/jbr.0000000000000051

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…There are several standard online ontologies representing concepts of medical domain. The importance of an ontology provides a predefined and rich vocabulary serving a stable interface for concepts of the data sources and at the same time is independent of the database schemas [4]. In this paper, the framework proposed focus on relating two medical ontologies which are the Human disease ontology (DO) [5,6] representing diseases' concepts only and their subsumption relationships, and the Symptoms ontology (SYMP) [7] representing symptoms' concepts only and their subsumption relationships.…”

Section: Introductionmentioning

confidence: 99%

Weighted Entity-Linking and Integration Algorithm for Medical Knowledge Graph Generation

Maghawry

Ghoniemy²,

shaaban³

et al. 2023

IJICIS

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Semantic data integration is the process of interrelating information from multiple heterogeneous resources. There is a need for representing data concepts and their relationships to eliminate heterogeneity among different data sources in healthcare management systems. Standardized medical ontologies provide predefined medical vocabulary serving as a stable interface for concepts related to medical data sources. However, different ontologies have different concepts although these concepts have logical relations between them such as the Human Disease Ontology and the Symptoms ontology. There aroused a need for a knowledge graph providing a reliable knowledge base for any intelligent healthcare expert advisor disease prediction system. The knowledge graph provides a model for linking and integrating different concepts having logical relationships such as diseases and their symptoms. Medical online website and encyclopedia provides a reliable source for building such a knowledge graph. The knowledge graph is enriched with social networks data where information extracted reflects a major source of data based on user experiences. The paper proposes a framework for constructing a disease-symptom entity linked knowledge graph based on online medical encyclopedia and social networks user experiences. Entity linking such an integrated knowledge graph with standardized medical ontologies makes it a reliable knowledge base for a standard system that could be used by social networks user and the professional staff.

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Section: Introductionmentioning

confidence: 99%

Weighted Entity-Linking and Integration Algorithm for Medical Knowledge Graph Generation

Maghawry

Ghoniemy²,

shaaban³

et al. 2023

IJICIS

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“…Ontologies are divided into three categories based on the application’s outlook. The first is referred to as an upper ontology [ 3 ]. This ontology supports in the determination and organization of terms and associations in a specific area by providing a basic framework.…”

Section: Introductionmentioning

confidence: 99%

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19

Thirumahal¹,

Sadasivam²,

Shruti³

2022

SN COMPUT. SCI.

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The enormous outbreak of biomedical knowledge, the aim of reducing computation and processing costs and the widespread availability of internet connection have created a profuse amount of electronic data. Such data are stored across the globe in various data sources that are semantically, structurally and syntactically different. This decentralized nature of biomedical data has made it difficult to obtain a unified view of the data. Data integration plays a crucial role in enhancing access to heterogeneous data making the retrieval easier and faster. A variety of ontology, machine learning, deep learning and fuzzy logic-based solutions are being developed for heterogeneous data integration. The proposed model concentrates on the automatic ontology-based data integration method that can be effectively deployed and used in the healthcare domain. The proposed model is divided into three phases. The first phase includes the automatic mapping of data and generation of local ontology across heterogeneous data sources, the second phase combines the local ontology models developed in the first phase to create a root global schema mapping and the third phase queries diverse databases to retrieve semantically analogous records. The model is created based on the medical records, chest X-ray details and COVID-19 symptom questionnaire data of various patients distributed across three data sources (SQL, mongodb and excel). Based on the data, the patients who have moderate/higher risk of developing serious illness from COVID-19 are retrieved.

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“…Based on the outlook of the application, ontologies can be classified into three major types [3]. The first type is called an upper ontology.…”

Section: Introductionmentioning

confidence: 99%

A survey on ontology techniques deployed in the biomedical domain

Shruti¹,

Thirumahal²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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The number of studies in big data aspects of biomedical domain are tremendously increasing because of the growing technical knowledge, need for reduced computation costs and the availability of internet facilities almost over everywhere. A considerable amount of data in the biomedical domain are stored across platforms that are semantically, structurally and semantically different. With this heterogeneity, it becomes extremely difficult to access and derive meaningful insights from data. Data Integration plays a significant role in merging the data and making access to these data faster and easier. Ontology, a form of knowledge representation, is widely used in data integration to denote the semantic relationship between the data stored in heterogeneous data sources and aid in easier retrieval. This paper surveys the ontology engineering methods used in various biomedical domains like cardiology, nephrology, diabetes, Covid-19, traditional medicine and the recent advancements in ontology development.

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Biomedical ontologies and their development, management, and applications in and beyond China

Cited by 5 publications

References 43 publications

Weighted Entity-Linking and Integration Algorithm for Medical Knowledge Graph Generation

Weighted Entity-Linking and Integration Algorithm for Medical Knowledge Graph Generation

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19

A survey on ontology techniques deployed in the biomedical domain

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