Original Article International Journal of Fuzzy Logic and Intelligent Systems Vol. 17, No. 4, December 2017, pp. 307-314 http://dx.doi.org/10.5391/IJFIS.2017.17.4.307 ISSN(Print) 1598-2645 ISSN(Online) 2093-744X Self-evolving Disease Ontology for Medical Domain Based on Web Ishara Sandun, Sagara Sumathipala, and Gamage Upeksha Ganegoda Faculty of Information Technology, University of Moratuwa, Katubedda, Moratuwa, Sri Lanka Abstract 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. Keywords: Medical ontology, Biomedical relationship extraction, Named entity recognition, Text mining Received: Dec. 11, 2017 Revised : Dec. 18, 2017 Accepted: Dec. 20, 2016 Correspondence to: Sagara Sumathipala (sagaras@uom.lk) ©The Korean Institute of Intelligent Systems cc This is an Open Access article dis- tributed under the terms of the Creative Commons Attribution Non-Commercial Li- cense (http://creativecommons.org/licenses/ by-nc/3.0/) which permits unrestricted non- commercial use, distribution, and reproduc- tion in any medium, provided the original work is properly cited. 1. Introduction There is a famous proverb saying that “Health is Wealth.” Health and well-being are two key factors to live a happy life. They are essential since it affects directly to one’s life and to live happily. 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. With the development of technology, people have come up with new methods and inventions to make the life easy for the professionals. 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. In medical domain new diseases, viruses, genes, etc. are identified very often, and relationships among them are rapidly changing. If the new biomedical relationships are not identified accurately and adequately, it will take a longer duration to identify a disease, its causes, methods to cure, and prevention. Therefore, 307 |