Identifying Principles for the Construction of an Ontology-Based Knowledge Base: A Case Study Approach

Abstract

BackgroundOntologies are key enabling technologies for the Semantic Web. The Web Ontology Language (OWL) is a semantic markup language for publishing and sharing ontologies.ObjectiveThe supply of customizable, computable, and formally represented molecular genetics information and health information, via electronic health record (EHR) interfaces, can play a critical role in achieving precision medicine. In this study, we used cystic fibrosis as an example to build an Ontology-based Knowledge Base prototype on Cystic Fibrobis (OntoKBCF) to supply such information via an EHR prototype. In addition, we elaborate on the construction and representation principles, approaches, applications, and representation challenges that we faced in the construction of OntoKBCF. The principles and approaches can be referenced and applied in constructing other ontology-based domain knowledge bases.MethodsFirst, we defined the scope of OntoKBCF according to possible clinical information needs about cystic fibrosis on both a molecular level and a clinical phenotype level. We then selected the knowledge sources to be represented in OntoKBCF. We utilized top-to-bottom content analysis and bottom-up construction to build OntoKBCF. Protégé-OWL was used to construct OntoKBCF. The construction principles included (1) to use existing basic terms as much as possible; (2) to use intersection and combination in representations; (3) to represent as many different types of facts as possible; and (4) to provide 2-5 examples for each type. HermiT 1.3.8.413 within Protégé-5.1.0 was used to check the consistency of OntoKBCF.ResultsOntoKBCF was constructed successfully, with the inclusion of 408 classes, 35 properties, and 113 equivalent classes. OntoKBCF includes both atomic concepts (such as amino acid) and complex concepts (such as “adolescent female cystic fibrosis patient”) and their descriptions. We demonstrated that OntoKBCF could make customizable molecular and health information available automatically and usable via an EHR prototype. The main challenges include the provision of a more comprehensive account of different patient groups as well as the representation of uncertain knowledge, ambiguous concepts, and negative statements and more complicated and detailed molecular mechanisms or pathway information about cystic fibrosis.ConclusionsAlthough cystic fibrosis is just one example, based on the current structure of OntoKBCF, it should be relatively straightforward to extend the prototype to cover different topics. Moreover, the principles underpinning its development could be reused for building alternative human monogenetic diseases knowledge bases.

Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes

“…Section editors achieved a first selection of 100 papers based on title and abstract. After a second review of this set of papers, including full text reviews, a selection of 15 candidate best papers was established 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 . Five reviewers reviewed these papers and four papers were finally selected as the best papers 2 3 4 5 .…”

“…Five of the candidate best papers present research in the ontology design domain 6 , 10 11 12 , 16 . This theme is common in the KRM section.…”

“…Jing et al , 6 described the motivation and the building of OntoKBCF, an ontology for the cystic fibrosis domain. They illustrated the lack of sufficient clinical actionable knowledge that is related to molecular genetic information.…”

XML Data and Knowledge-Encoding Structure for a Web-Based and Mobile Antenatal Clinical Decision Support System: Development Study

“…There are many ways to computationally model knowledge and behavior in AI systems, and the use of state machines is one of the oldest and best known [ 14 ]. State machines model a system’s states, or features of a system, at a particular point in time and characterize its future behavior based on these states [ 15 ].…”