Authored by Riad Jabri*
Abstract
Ontologies are developed to share the knowledge among the researchers working on the same domain. In medical domain it is becoming an important aspect of medical informatics. The objectives of building medical ontology are constructing more powerful and more interoperable information systems in health care. This paper builds a tooth-diseases ontology that represents the anatomical structure to the diagnosis of diseases. We consider the tooth diseases among Jordanians. Frequent diseases were selected to be represented after discussions with a dentist’s specialist. Their symptoms and respective treatments are then formalized. Finally, OWL based ontology is constructed. Thus, obtaining a formal diagnostic model for tooth diseases. This enables better understanding and diagnosis of such diseases by dentists, dental students and the ones who lack field experiences.
Keywords: Disease; Ontology; Medical; Tooth; Description logic
Introduction
The term “ontology” has its roots in philosophy which has been defined as a particular theory about the nature of being or the kinds of existence. In the computer science community, ontology becomes an important issue. Many research areas study ontology, such as Artificial Intelligence, knowledge-based systems, language engineering, multi-database systems, Medical area, agent-based systems, information systems, etc [1].
Medical ontologies have become prominent in recent years, not only for medical researchers but also physicians, hospitals and insurance companies. Medical ontologies linked disease concepts and properties together in a coherent way. This paper focused on developing tooth-disease ontology which contains diseases of tooth, symptoms, cause, and classification information related to their treatment. In particular, we develop tooth-diseases ontology (TDO) with frequently occurring tooth diseases in Jordan. We identify frequent tooth diseases and formulate their symptoms and treatment, using description and defeasible logics. Protege OWL ontology is then constructed to diagnose an appropriate treatment of the selected diseases. Thus, achieving a better understanding of the tooth diseases for ones who have less experiences. Further, this ontology can be linked to a simulation model for education of dental students by offering the necessary information in their fields.
This paper is structured as follows: section two provides the background and motivation for developing TDO. Section three identifies TDO. Section 4 presents a formalization of TDO. Section 5 presents the construction of TDO. Finally, in Section 6, we draw our conclusions.
Background and Motivation
The lack of deep knowledge in a domain is the major bottleneck preventing the rapid spread in knowledge bases. Nowadays, ontology systems have appealed more and more attention in several research areas such as medical vision. Where a lot of ontologies vision systems have been presented and have achieved great success for handling complicated medical domains. In spite of an ever-increasing number of biomedical ontologies, there are relatively little ontology available for use by the dental community at the present time, one reason for this may be that the potential uses, and applications of dental ontologies have not been adequately described. This paper represents an attempt to address this issue. We choose to work in dental diseases for Jordanians cases as an opportunity to represent these cases. In addition to the fact that Jordan have many distinctive cases in dentistry such as the case found by Haddadin et al. [2] and Faiez N. Hattab [3]. Concisely, our research interest lies on the area of dental community which consequently enriches the semantics of the knowledge to be utilized as knowledge base.
In recent years, many researchers have been carried out to propose new medical ontology, Khoo et al. [4] have state initial version of disease-treatment ontology developed based on an analysis of 50 medical abstracts on colon cancer therapy retrieved from the Medline database.
Information technology today is widely adopted in modern medical practice, especially supporting digitized equipment, administrative tasks, and data management but less has been achieved in the use of computational techniques to exploit the medical information in research. Tokosumi et al. [1] have suggested locality of knowledge for a practical use of ontologies, coordination of large integrated ontologies and localized community ontologies were proposed. Constructing knowledge repository in ontological expressions, that is easy understandable for knowledge agent in the community, enables to maintain safety on the medical communication and to improve the quality of medical care.
In the dental case Park et al. [5] presents a dental case study, calling for reasoning with an OWL-DL ontology and SWRL-based rules to helping decision of restoring a missing tooth. While Kiani et al. [6] proposed system to negotiation between dental experts over the treatment of wisdom teeth. These systems are designed to provide help to the dentist in improving oral health status and to potentially reduce errors in practice.
Jensen et al. [7] develop Neurological Disease Ontology (ND) to provide a framework to enable representation of aspects of neurological diseases that are relevant to their treatment and study. While Ashburner et al. [8] show the success of the Gene Ontology in how a controlled and properly curated ontology can benefit and extend research in medicine. In Scheuermann et al. [9] represent Ontology for General Medical Science demonstrate entities in the domain of medicine and disease and addresses the need to integrate biomedical data. The description logic as we use in this paper is expressive enough for defining the relevant concepts in enough detail, but not too expressive to make reasoning infeasible. The Gomes et al.[10] describe structure of description logics and its approach in the cardiology medical environment, and we compare the use of description logics in the pathology environment by using a practical model of description logics use in terms of diseases related to the circulatory system of the human body.
Tooth- Disease Ontology
Several methodologies for constructing ontology have been suggested [11]. In this section, we build tooth- disease ontology (TDO) following a well-accepted method [11] as shown in (Figure 1).
Knowledge domain
We define the domain of TDO as consisting of tooth diseases, symptoms, and their respective treatments. TDO is new one, where there is not much research done on it, especially in Jordan. Thus, we define our scope as to develop TDO in reusable form of knowledge. Dentists, especially the ones who lack experiences, will be able to get help for their decision making and to learn what terms in the domain mean.
Selecting tooth diseases
The tooth diseases were selected based on the ones that are classified as a highly occurring among Jordanian, as shown in Table I, The terms used to express these dieses and TDO are the same as the ones used in medical dictionaries and schools for education (Table 1).
Ontology capture
Building the ontology is reduced to identification of the key concepts and their respective relationships. Thus, the top-level classes or concepts of TDO are shown in Figure 2. Each node represents a class or concept. Each directed arc or arrow represents a property or relation. The top-class in the ontology is TDO which represents specific treatments that are considered for a particular disease, as described in the paper (Figure 2).
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