Ontology for Symptomatic Treatment of Multiple Sclerosis

Misagh Zahiri Esfahani,Maryam Ahmadi,Iman Adibi 대한의료정보학회 2022 Healthcare Informatics Research Vol.28 No.4

Objectives: Symptomatic treatment is an essential component in the overall treatment of multiple sclerosis (MS). However, knowledge in this regard is confusing and scattered. Physicians also have challenges in choosing symptomatic treatment based on the patient’s condition. To share, update, and reuse this knowledge, the aim of this study was to provide an ontology for MS symptomatic treatment. Methods: The Symptomatic Treatment of Multiple Sclerosis Ontology (STMSO) was developed according to Ontology Development 101 and a guideline for developing good ontologies in the biomedical domain. We obtained knowledge and rules through a systematic review and entered this knowledge in the form of classes and subclasses in the ontology. We then mapped the ontology using the Basic Formal Ontology (BFO) and Ontology for General Medical Sciences (OGMS) as reference ontologies. The ontology was built using Protégé Editor in the Web Ontology Language format. Finally, an evaluation was done by experts using criterion-based approaches in terms of accuracy, clarity, consistency, and completeness. Results: The knowledge extraction phase identified 110 articles related to the ontology in the form of 626 classes, 40 object properties, and 139 rules. Five general classes included “patient,” “symptoms,” “pharmacological treatment,” “treatment plan,” and “measurement index.” The evaluation in terms of standards for biomedical ontology showed that STMSO was accurate, clear, consistent, and complete. Conclusions: STMSO is the first comprehensive semantic representation of the symptomatic treatment of MS and provides a major step toward the development of intelligent clinical decision support systems for symptomatic MS treatment.

기술논리와 SWRL 기반의 웹 온톨로지 모델링

김수경,안기홍 한국정보관리학회 2008 정보관리학회지 Vol.25 No.1

Actually a diffusion of a Semantic Web application and utilization are situations insufficient extremely. Technology most important in Semantic Web application is construction of the Ontology which contents itself with characteristics of Semantic Web. Proposed a suitable a Method of Building Web Ontology for characteristics of Semantic Web and Web Ontology as we compared the existing Ontology construction and Ontology construction techniques proposed for Web Ontology construction, and we analyzed. And modeling did Ontology to bases to Description Logic and the any axiom rule that used an expression way of SWRL, and established Inference-based Web Ontology according to proposed ways. Verified performance of Ontology established through Ontology inference experiment. Also, established an Web Ontology-based Intelligence Image Retrieval System, to experiment systems for performance evaluation of established Web Ontology, and present an example of implementation of a Semantic Web application and utilization. Demonstrated excellence of a Semantic Web application to be based on Ontology through inference experiment of an experiment system. 차세대 인터넷 기술로 각광받은 시맨틱 웹의 완전한 사용은 도메인 영역의 지식표현과 지식추론의 성능에 달려있다. 특히 표현된 지식을 기계가 이해하여 인간과 도메인들 간의 상호작용을 위해서는 더욱 형식적이고 명시적인 지식과 추론 표현이 기반된 웹 온톨로지 구축이 중요하다. 더구나 웹 온톨로지간의 상호작용은 시맨틱 웹의 기술적 완성을 위한 중요 요소이나 현재 웹 온톨로지의 구축을 위한 표준화된 모델링 방법의 부족으로 인해, 구축된 웹 온톨로지의 상호작용과 이해가 어려운 상황이다. 따라서 이같은 문제를 해결하기 위해 본 논문은 온톨로지의 지식 표현과 추론에 따른 단계를 명확하게 정의하고 정의된 각 단계에 따라 기술논리의 TBox와 ABox의 지식표현 구조와 SWRL 기반의 추론 규칙을 바탕으로 하는 웹 온톨로지 모델링 방법을 제안한다. 제안된 방법의 성능 검증을 위해 제안된 웹 온톨로지 모델링 과정에 따라 웹 온톨로지들을 구축하였고, 구축된 웹 온톨로지들의 추론에 따른 상호작용 성능을 실험하여 본 논문의 유용성을 입증하였다.

Marine Ecological Knowledge Management System Based on Ontology Repository

보안공학연구지원센터(IJUNESST) 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.2

This paper researches semantic retrieval and ontology rule reasoning for marine ecological knowledge management system. Upper ontology plays a critical role in ontology development by giving developers a guideline to view the target domain. By fixing a viewpoint of device-function, we present a domain upper ontology for marine ecosystem, and then construct marine ecology ontology and ontology repository. Based on constructed marine ecology ontology repository, authors design and develop marine ecological knowledge management system, which implements main functions include marine ecological knowledge navigation, term query, knowledge retrieval and ecological crisis early warning. Marine ecology ontology application system provides users a semantic support platform for marine scientific research and cooperation, and it also verifies the rationality and feasibility of ontology modeling method and validity of constructed marine ecology ontology.

Using the METHONTOLOGY Approach to a Graduation Screen Ontology Development: An Experiential Investigation of the METHONTOLOGY Framework

Park, Jin-Soo,Sung, Ki-Moon,Moon, Se-Won The Korea Society of Management Information System 2010 Asia Pacific Journal of Information Systems Vol.20 No.2

Ontologies have been adopted in various business and scientific communities as a key component of the Semantic Web. Despite the increasing importance of ontologies, ontology developers still perceive construction tasks as a challenge. A clearly defined and well-structured methodology can reduce the time required to develop an ontology and increase the probability of success of a project. However, no reliable knowledge-engineering methodology for ontology development currently exists; every methodology has been tailored toward the development of a particular ontology. In this study, we developed a Graduation Screen Ontology (GSO). The graduation screen domain was chosen for the several reasons. First, the graduation screen process is a complicated task requiring a complex reasoning process. Second, GSO may be reused for other universities because the graduation screen process is similar for most universities. Finally, GSO can be built within a given period because the size of the selected domain is reasonable. No standard ontology development methodology exists; thus, one of the existing ontology development methodologies had to be chosen. The most important considerations for selecting the ontology development methodology of GSO included whether it can be applied to a new domain; whether it covers a broader set of development tasks; and whether it gives sufficient explanation of each development task. We evaluated various ontology development methodologies based on the evaluation framework proposed by G$\acute{o}$mez-P$\acute{e}$rez et al. We concluded that METHONTOLOGY was the most applicable to the building of GSO for this study. METHONTOLOGY was derived from the experience of developing Chemical Ontology at the Polytechnic University of Madrid by Fern$\acute{a}$ndez-L$\acute{o}$pez et al. and is regarded as the most mature ontology development methodology. METHONTOLOGY describes a very detailed approach for building an ontology under a centralized development environment at the conceptual level. This methodology consists of three broad processes, with each process containing specific sub-processes: management (scheduling, control, and quality assurance); development (specification, conceptualization, formalization, implementation, and maintenance); and support process (knowledge acquisition, evaluation, documentation, configuration management, and integration). An ontology development language and ontology development tool for GSO construction also had to be selected. We adopted OWL-DL as the ontology development language. OWL was selected because of its computational quality of consistency in checking and classification, which is crucial in developing coherent and useful ontological models for very complex domains. In addition, Protege-OWL was chosen for an ontology development tool because it is supported by METHONTOLOGY and is widely used because of its platform-independent characteristics. Based on the GSO development experience of the researchers, some issues relating to the METHONTOLOGY, OWL-DL, and Prot$\acute{e}$g$\acute{e}$-OWL were identified. We focused on presenting drawbacks of METHONTOLOGY and discussing how each weakness could be addressed. First, METHONTOLOGY insists that domain experts who do not have ontology construction experience can easily build ontologies. However, it is still difficult for these domain experts to develop a sophisticated ontology, especially if they have insufficient background knowledge related to the ontology. Second, METHONTOLOGY does not include a development stage called the "feasibility study." This pre-development stage helps developers ensure not only that a planned ontology is necessary and sufficiently valuable to begin an ontology building project, but also to determine whether the project will be successful. Third, METHONTOLOGY excludes an explanation on the use and integration of existing ontologies. If an

A WEB-BASED DOMAIN ONTOLOGY CONSTRUCTION MODELING AND APPLICATION IN THE WETLAND DOMAIN

Jun Xing,Min Han 한국멀티미디어학회 2006 한국멀티미디어학회 국제학술대회 Vol.2006 No.-

Methodology of ontology building based on Web resources will not only reduce significantly the ontology construction period, but also enhance the quality of the ontology. Remarkable progress has been achieved in this regard, but they encounter similar difficulties, such as the Web data extraction and knowledge acquisition. This paper researches on the characteristics of ontology construction data, including dynamics, largeness, variation and openness and other features, and the fundamental issue of ontology construction - formalized representation method. Then, the key technologies used in and the difficulties with ontology construction are summarized. A software Model - Onto Maker (Ontology Maker) is designed. The model is innovative in two regards: (1) the improvement of generality: the meta learning machine will dynamically pick appropriate ontology learning methodologies for data of different domains, thus optimizing the results; (2) the merged processing of (semi-) structural and non-structural data. In addition, as known to all wetland researchers, information sharing is vital to wetland exploitation and protection, while wetland ontology construction is the basic task for information sharing. Onto Maker constructs the wetland ontologisms and the model in this work can also be referred to other environmental domains.

축구 선수의 소셜 미디어 내용 분석을 위한 온톨로지 모형 연구

김주학,조선미,강지연 국민체육진흥공단 한국스포츠정책과학원 2020 체육과학연구 Vol.31 No.4

[Purpose] Soccer-related social media BigData includes complex information related to soccer players and is continuously and instantly generated. Text mining research is actively carried out for this kind of social media contents analysis, but it tends to be analyzed with limited linguistic characteristics such as understanding of language complexity and context, ambiguous terms, rhetoric, and new terms. This can be attributed to the fact that the tools commonly used for text mining use universal terminology dictionaries and packages that exclude the peculiarities of the analysis themes. The purpose of this study is to develop an Ontology model, which are representative tools for defining semantic ambiguity and relationships and systems between terms of text data. [Methods] In order to achieve the research objectives, we applied the 7-step development method of ‘Ontology Development 101: A Guide to Creating Your First Ontology’, which is useful for ontology development. Each step includes 1) Determine the domain and scope of the ontology 2) Consider reusing existing ontology 3) Enumerate important terms in the ontology 4) Define the classes and the class hierarchy 5) Define the properties of classes-slots 6) Define the facts of the slots 7) Create instances. In particular, the 3rd-step of this study, the glossary stage, is to extract core terms that make up he ontology, but since the goal of this study is to develop the ontology that can be used in social media contents analysis of soccer players, we conducted a social media text analysis related to actual soccer players and selected 484 core terms. [Results] The ontology which was developed in this research for social media contents analysis of soccer players consisted largely of four parts(General terms, performance results terms, common terms, and Characteristic term) and classified according to the content characteristics of the term. [Conclusion] Developed ontology in this study is object-oriented that defining classes and objects to define divisions and relationships between terms and also means a social media contents knowledge system of soccer players. In addition, it performs a function as a secondary tool which can be utilized for atypical data analysis. [목적] 축구와 관련한 소셜 미디어 빅데이터는 축구 선수와 관련된 복합적 차원의 정보를 내포하며 연속적으로 빠르게 생성되고 있다. 이러한 소셜 미디어 내용 분석을 위해 텍스트 마이닝 연구가 활발히 진행되고 있으나 언어의 복잡성과 문맥에 대한 이해, 중의어, 수사어, 신조어 등 언어적 특성으로 다소 제한적으로 분석되는 경향이 있다. 이는 일반적으로 텍스트 마이닝에 사용되는 도구가 분석 주제의 특수성을 배제한 보편적인 용어 사전이나 패키지를 사용하기 때문이라 볼 수 있다. 이 연구는 텍스트 데이터의 의미적 모호성과 용어 간 관계 및 체계를 정의하는 대표적인 도구인 온톨로지(Ontology) 모형을 개발하는 데 그 목적이 있다. [방법] 연구의 목적 달성을 위해, 초기 온톨로지 개발에 유용한 'Ontology Development 101: A Guide to Creating Your First Ontology’의 7단계 개발방법을 적용하였다. 각 7단계는 1)온톨로지 대상 분야와 범위 규정, 2)선행 온톨로지 검토, 3)용어 나열, 4)클래스 정의 및 계층 정의, 5)클래스의 속성 정의, 6)슬롯의 패싯 정의, 7)개별 사례 생성의 단계를 포함한다. 특히, 이 연구의 세 번째 단계인 용어 나열 단계는 온톨로지를 구성하는 핵심 용어를 추출하는 단계인데, 이 연구의 목표가 축구 선수의 소셜 미디어 내용 분석에 활용되는 온톨로지를 개발하는 것이기 때문에 실제 축구 선수와 관련한 소셜 미디어에 등장한 텍스트 분석을 진행하여 484개의 핵심용어를 선정하였다. [결과] 개발한 축구 선수의 소셜 미디어 내용 분석을 위한 온톨로지는 크게 인물, 수행결과, 공통용어, 특정어의 4가지의 영역으로 구성되며 용어의 내용적 특성에 따라 분류되었다. 각 영역에 구성된 484개의 용어에 대하여 관계 및 정의, 속성값을 기술하였다. [결론]개발 온톨로지는 클래스와 객체를 정의하여 용어 간 구분 및 관계를 정의한 객체 지향적 온톨로지 모형이며 축구 선수의 소셜 미디어에서 나타난 지식체계를 대변한다. 또한, 비정형 데이터 분석에 활용될 수 있는 2차 도구로써의 기능을 수행한다.

Conceptual Cluster-based Large-scale Ontology Compression Approach

Yang Feng,Qin Ziting 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.7

With the development of semantic web and ontology application, there is a large number of ontology whose scale is large and the structure is complex in different fields. The existing mapping method and mapping system perform well when dealing with the mapping between a lightweight small ontology. However, when comes to the large-scale ontology, it is full of challenges to the methods and systems。To this end, this paper proposes a method of ontology compression based on conceptual cluster to compress. Firstly, it calculates the semantic similarity and semantic correlation of ontology concepts with the DICE coefficient method and the information entropy technology to get semantic relation. Secondly according to the semantic relations, it carries on the conceptual cluster in the concept space so that the concept of semantic relations closely together in groups. The concept of cluster in space is reduced, and the "noise concept" which is independent of the mapping is removed, and the purpose of the large-scale ontology compression is realized. Experimental results show that the method is so effective that it can compress the volume of large-scale ontology in the mapping problems.

Using the METHONTOLOGY Approach to a Graduation Screen Ontology Development: An Experiential Investigation of the METHONTOLOGY Framework

박진수,성기문,문세원 한국경영정보학회 2010 Asia Pacific Journal of Information Systems Vol.20 No.2

Ontologies have been adopted in various business and scientific communities as a key component of the Semantic Web. Despite the increasing importance of ontologies, ontology developers still perceive construction tasks as a challenge. A clearly defined and well-structured methodology can reduce the time required to develop an ontology and increase the probability of success of a project. However, no reliable knowledge-engineering methodology for ontology development currently exists; every methodology has been tailored toward the development of a particular ontology. In this study, we developed a Graduation Screen Ontology (GSO). The graduation screen domain was chosen for the several reasons. First, the graduation screen process is a complicated task requiring a complex reasoning process. Second, GSO may be reused for other universities because the graduation screen process is similar for most universities. Finally, GSO can be built within a given period because the size of the selected domain is reasonable. No standard ontology development methodology exists; thus, one of the existing ontology development methodologies had to be chosen. The most important considerations for selecting the ontology development methodology of GSO included whether it can be applied to a new domain; whether it covers a broader set of development tasks; and whether it gives sufficient explanation of each development task. We evaluated various ontology development methodologies based on the evaluation framework proposed by Gómez-Pérez et al. We concluded that METHONTOLOGY was the most applicable to the building of GSO for this study. METHONTOLOGY was derived from the experience of developing Chemical Ontology at the Polytechnic University of Madrid by Fernández-López et al. and is regarded as the most mature ontology development methodology. METHONTOLOGY describes a very detailed approach for building an ontology under a centralized development environment at the conceptual level. This methodology consists of three broad processes,with each process containing specific sub-processes: management (scheduling, control, and quality assurance); development (specification, conceptualization, formalization, implementation, and maintenance); and support process (knowledge acquisition, evaluation, documentation, configuration management, and integration). An ontology development language and ontology development tool for GSO construction also had to be selected. We adopted OWL-DL as the ontology development language. OWL was selected because of its computational quality of consistency in checking and classification, which is crucial in developing coherent and useful ontological models for very complex domains. In addition, Protégé-OWL was chosen for an ontology development tool because it is supported by METHONTOLOGY and is widely used because of its platform-independent characteristics. Based on the GSO development experience of the researchers, some issues relating to the METHONTOLOGY, OWL-DL, and Protégé-OWL were identified. We focused on presenting drawbacks of METHONTOLOGY and discussing how each weakness could be addressed. First, METHONTOLOGY insists that domain experts who do not have ontology construction experience can easily build ontologies. However, it is still difficult for these domain experts to develop a sophisticated ontology, especially if they have insufficient background knowledge related to the ontology. Second, METHONTOLOGY does not include a development stage called the "feasibility study." This pre-development stage helps developers ensure not only that a planned ontology is necessary and sufficiently valuable to begin an ontology building project, but also to determine whether the project will be successful. Third, METHONTOLOGY excludes an explanation on the use and integration of existing ontologies. If an additional stage for considering reuse is introduced, developers might share benefits of reuse. Fourth, METHONTOLOG...

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals

Jung, Hyesil,Park, Hyeoun-Ae,Song, Tae-Min JMIR Publications 2017 Journal of medical Internet research Vol.19 No.7

<P><B>Background</B></P><P>Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics.</P><P><B>Objective</B></P><P>The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis.</P><P><B>Methods</B></P><P>The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts.</P><P><B>Results</B></P><P>We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, “academic stresses” and “suicide” contributed negatively to the sentiment of adolescent depression.</P><P><B>Conclusions</B></P><P>The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology.</P>

A Web-Based Domain Ontology Construction Modelling and Application in the Wetland Domain

Jun Xing,Min Han 한국멀티미디어학회 2007 멀티미디어학회논문지 Vol.10 No.6

Methodology of ontology building based on Web resources will not only reduce significantly the ontology construction period, but also enhance the quality of the ontology. Remarkable progress has been achieved in this regard, but they encounter similar difficulties, such as the Web data extraction and knowledge acquisition. This paper researches on the characteristics of ontology construction data, including dynamics, largeness, variation and openness and other features, and the fundamental issue of ontology construction - formalized representation method. Then, the key technologies used in and the difficulties with ontology construction are summarized. A software Model-OntoMaker (Ontology Maker) is designed. The model is innovative in two regards: (1) the improvement of generality: the meta learning machine will dynamically pick appropriate ontology learning methodologies for data of different domains, thus optimizing the results; (2) the merged processing of (semi-) structural and non-structural data. In addition, as known to all wetland researchers, information sharing is vital to wetland exploitation and protection, while wetland ontology construction is the basic task for information sharing. OntoMaker constructs the wetland ontologies, and the model in this work can also be referred to other environmental domains.