지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.17 No.4
- 발행연도
- 2017.12
- 수록면
- 307 - 314 (8page)
- DOI
- 10.5391/IJFIS.2017.17.4.307
이용수
초록· 키워드
오류제보하기
In last decade information technology has gained a rapid development, and today it plays a crucial role in everyone’s life. It makes the life more comfortable for professional to do their work. Every performance and the innovating task will become more comfortable if there is a proper and accurate knowledge base containing up to date information. It will be an added advantage if the so-called knowledge base could shrink and expand dynamically. Especially in the medical domain, there is a higher demand and necessity for such kind of knowledge base which evolves dynamically with time and data because medical field is rapidly evolving and new biomedical entities such as diseases, symptoms, proteins, and so forth are frequently introducing. This study proposes a mechanism to generate dynamically evolving ontology for the biomedical domain which evolves with new relations explores from web data and patient history records. Proposed approach retrieves information from the ontology and generates probabilistic values for each relationship in the disease ontology. This approach used to create a dynamically evolving ontology for the medical domain to manage the relationship between diseases and symptoms more effectively. Furthermore, it retrieves data from the ontology to answer user queries related to the diseases and symptoms.
목차
Abstract
1. Introduction
2. Related Work
3. Proposed Solution
4. Results and Evaluation
5. Discussion
6. Conclusion
References
참고문헌 (17)
참고문헌 신청
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Z. Yang, H. Lin, and Y. Li, “Exploiting the performance of dictionary-based bio-entity name recognition in biomedical literature,” Computational Biology and Chemistry, vol. 32, no. 4, pp. 287-291, 2008. https://doi.org/10.1016/j.compbiolchem.2008.03.008
S. Sumathipala, K. Yamada, M. Unehara, and I. Suzuki, “Protein named entity identification based on probabilistic features derived from GENIA corpus and medical text on the web,” International Journal of Fuzzy Logic and Intelligent Systems, vol. 15, no. 2, pp. 111-120, 2015. https://doi.org/10.5391/IJFIS.2015.15.2.111
S. Sumathipala, K. Yamada, M. Unehara, and I. Suzuki, “Protein entity name recognition using orthographic, morphological and proteinhood features,” Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 19, no. 6, pp. 843-851, 2015. http://doi.org/10.20965/jaciii.2015.p0843
S. Sumathipala, K. Yamada, and M. Unehara, “Protein named entity classification with probabilistic features derived from GENIA corpus and MEDLINE,” in Proceedings of 15th International Symposium on Soft Computing and Intelligent Systems (SCIS), Kitakyushu, Japan, 2014, pp. 1257-1261. http://doi.org/10.1109/SCISISIS.2014.7044640
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UCI(KEPA) : I410-ECN-0101-2018-003-001712763