{"title":"基于格式良好的疾病本体的人类疾病相关性的双向传播激活方法","authors":"S. Fathalla, Yaman Kannot","doi":"10.4018/IJCCP.2017010104","DOIUrl":null,"url":null,"abstract":"The successful application of semantic web in medical informatics and the fast expanding of biomedical knowledge have prompted to the requirement for a standardized representation of knowledge and an efficient algorithm for querying this extensive information. Spreading activation algorithm is suitable to work on incomplete and large datasets. This article presents a method called SAOO (Spreading Activation over Ontology) which identifies the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique over large disease ontology. The proposed methodology is divided into two phases: Semantic matching and Disease relatedness detection. In Semantic Matching, semantically identify diseases in user's query in the ontology. In the Disease Relatedness Detection, URIs of the diseases are passed to the relatedness detector which returns the set of diseases that may connect them. The proposed method improves the non-semantic medical systems by considering semantic domain knowledge to infer diseases relatedness.","PeriodicalId":177246,"journal":{"name":"Data Analytics in Medicine","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Bidirectional Spreading Activation Method for Finding Human Diseases Relatedness Using Well-Formed Disease Ontology\",\"authors\":\"S. Fathalla, Yaman Kannot\",\"doi\":\"10.4018/IJCCP.2017010104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The successful application of semantic web in medical informatics and the fast expanding of biomedical knowledge have prompted to the requirement for a standardized representation of knowledge and an efficient algorithm for querying this extensive information. Spreading activation algorithm is suitable to work on incomplete and large datasets. This article presents a method called SAOO (Spreading Activation over Ontology) which identifies the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique over large disease ontology. The proposed methodology is divided into two phases: Semantic matching and Disease relatedness detection. In Semantic Matching, semantically identify diseases in user's query in the ontology. In the Disease Relatedness Detection, URIs of the diseases are passed to the relatedness detector which returns the set of diseases that may connect them. The proposed method improves the non-semantic medical systems by considering semantic domain knowledge to infer diseases relatedness.\",\"PeriodicalId\":177246,\"journal\":{\"name\":\"Data Analytics in Medicine\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Analytics in Medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/IJCCP.2017010104\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Analytics in Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJCCP.2017010104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
语义网在医学信息学中的成功应用和生物医学知识的快速增长,促使人们对知识的标准化表示和查询这些广泛信息的有效算法提出了要求。扩展激活算法适用于不完整的大数据集。本文提出了一种基于双向搜索技术的传播激活算法在大型疾病本体上识别两种人类疾病之间相关性的方法SAOO (spread Activation over Ontology)。该方法分为语义匹配和疾病相关性检测两个阶段。在语义匹配中,在本体中对用户查询中的疾病进行语义识别。在疾病相关性检测中,疾病的uri被传递给相关性检测器,后者返回可能与疾病相关的一组疾病。该方法通过考虑语义领域知识对非语义医疗系统进行疾病相关性推断,从而改进了非语义医疗系统。
Bidirectional Spreading Activation Method for Finding Human Diseases Relatedness Using Well-Formed Disease Ontology
The successful application of semantic web in medical informatics and the fast expanding of biomedical knowledge have prompted to the requirement for a standardized representation of knowledge and an efficient algorithm for querying this extensive information. Spreading activation algorithm is suitable to work on incomplete and large datasets. This article presents a method called SAOO (Spreading Activation over Ontology) which identifies the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique over large disease ontology. The proposed methodology is divided into two phases: Semantic matching and Disease relatedness detection. In Semantic Matching, semantically identify diseases in user's query in the ontology. In the Disease Relatedness Detection, URIs of the diseases are passed to the relatedness detector which returns the set of diseases that may connect them. The proposed method improves the non-semantic medical systems by considering semantic domain knowledge to infer diseases relatedness.