As the tsunami of data has emerged,search engines have become the most powerful tool for obtaining scattered information on the internet.The traditional search engines return the organized results by using ranking alg...As the tsunami of data has emerged,search engines have become the most powerful tool for obtaining scattered information on the internet.The traditional search engines return the organized results by using ranking algorithm such as term frequency,link analysis(PageRank algorithm and HITS algorithm)etc.However,these algorithms must combine the keyword frequency to determine the relevance between user’s query and the data in the computer system or internet.Moreover,we expect the search engines could understand users’searching by content meanings rather than literal strings.Semantic Web is an intelligent network and it could understand human’s language more semantically and make the communication easier between human and computers.But,the current technology for the semantic search is hard to apply.Because some meta data should be annotated to each web pages,then the search engine will have the ability to understand the users intend.However,annotate every web page is very time-consuming and leads to inefficiency.So,this study designed an ontology-based approach to improve the current traditional keyword-based search and emulate the effects of semantic search.And let the search engine can understand users more semantically when it gets the knowledge.展开更多
A new mapping approach for automated ontology mapping using web search engines （such as Google） is presented. Based on lexico-syntactic patterns, the hyponymy relationships between ontology concepts can be obtained ...A new mapping approach for automated ontology mapping using web search engines （such as Google） is presented. Based on lexico-syntactic patterns, the hyponymy relationships between ontology concepts can be obtained from the web by search engines and an initial candidate mapping set consisting of ontology concept pairs is generated. According to the concept hierarchies of ontologies, a set of production rules is proposed to delete the concept pairs inconsistent with the ontology semantics from the initial candidate mapping set and add the concept pairs consistent with the ontology semantics to it. Finally, ontology mappings are chosen from the candidate mapping set automatically with a mapping select rule which is based on mutual information. Experimental results show that the F-measure can reach 75% to 100% and it can effectively accomplish the mapping between ontologies.展开更多
To integrate reasoning and text retrieval, the architecture of a semantic search engine which includes several kinds of queries is proposed, and the semantic search engine Smartch is designed and implemented. Based on...To integrate reasoning and text retrieval, the architecture of a semantic search engine which includes several kinds of queries is proposed, and the semantic search engine Smartch is designed and implemented. Based on a logical reasoning process and a graphic user-defined process, Smartch provides four kinds of search services. They are basic search, concept search, graphic user-defined query and association relationship search. The experimental results show that compared with the traditional search engine, the recall and precision of Smartch are improved. Graphic user-defined queries can accurately locate the information of user needs. Association relationship search can find complicated relationships between concepts. Smartch can perform some intelligent functions based on ontology inference.展开更多
目的通过梳理中医专家问诊思维导图,采用本体和语义网技术探索构建中医专家问诊信息模型的方法。方法中医专家问诊信息模型是由中医专家问诊数据采集信息模型和中医专家问诊知识库本体两部分组成,基于对中医专家问诊思维导图的梳理,结...目的通过梳理中医专家问诊思维导图,采用本体和语义网技术探索构建中医专家问诊信息模型的方法。方法中医专家问诊信息模型是由中医专家问诊数据采集信息模型和中医专家问诊知识库本体两部分组成,基于对中医专家问诊思维导图的梳理,结合已发表的论文和专著中所载各类中医病-证-症量表、已发布的各专病标准数据集、中医和中西医指南/专家共识、高校教材、中医问诊相关国家标准和行业标准等,对研究素材中的中医专家问诊数据采集相关信息框架提炼,完成中医专家问诊数据采集信息模型的构建;参考复用中医药语言系统(Traditional Chinese Medicine language systems,TCMLS)、中医临床术语系统(Traditional Chinese Medicine clinical terminological systems,TCMCTS)、国标、行标中的术语进行筛选、合并、分类,确立领域概念,结合中医临床问诊实际情况和呼吸科专病中医问诊思维导图,采用人工知识抽取方法,构建中医专家问诊知识库相关语义关系,采用七步法及protégé本体工具构建中医专家问诊知识库本体,实现中医专家问诊信息的知识推理表示。结果成功绘制了中医专家问诊思维导图,初步完成了中医专家问诊信息模型的构建,实现了中医专家问诊知识库的结构化表达。结论整合了中医专家问诊相关知识,构建了中医专家问诊信息模型,规范了中医专家问诊知识库信息化表达,为中医临床专科问诊信息化研究的创新发展提供了借鉴和参考。展开更多
Radiology doctors perform text-based image retrieval when they want to retrieve medical images.However,the accuracy and efficiency of such retrieval cannot keep up with the requirements.An innovative algorithm is bein...Radiology doctors perform text-based image retrieval when they want to retrieve medical images.However,the accuracy and efficiency of such retrieval cannot keep up with the requirements.An innovative algorithm is being proposed to retrieve similar medical images.First,we extract the professional terms from the ontology structure and use them to annotate the CT images.Second,the semantic similarity matrix of ontology terms is calculated according to the structure of the ontology.Lastly,the corresponding semantic distance is calculated according to the marked vector,which contains different annotations.We use 120 real liver CT images(divided into six categories)of a top three-hospital to run the algorithm of the program.Result shows that the retrieval index"Precision"is 80.81%,and the classification index"AUC(Area Under Curve)"under the"ROC curve"(Receiver Operating Characteristic)is 0.945.展开更多
以经济学领域本体为例,首先研究SemSORD基本原理和方法,然后提出基于关系数据库关键词检索(Keyword Search over Relational Databases,KSORD)技术实现的关系数据库语义检索模型,并实现相应的原型系统Si-SEEKER,最后提出该领域的研究挑...以经济学领域本体为例,首先研究SemSORD基本原理和方法,然后提出基于关系数据库关键词检索(Keyword Search over Relational Databases,KSORD)技术实现的关系数据库语义检索模型,并实现相应的原型系统Si-SEEKER,最后提出该领域的研究挑战和技术发展趋势.展开更多
文摘As the tsunami of data has emerged,search engines have become the most powerful tool for obtaining scattered information on the internet.The traditional search engines return the organized results by using ranking algorithm such as term frequency,link analysis(PageRank algorithm and HITS algorithm)etc.However,these algorithms must combine the keyword frequency to determine the relevance between user’s query and the data in the computer system or internet.Moreover,we expect the search engines could understand users’searching by content meanings rather than literal strings.Semantic Web is an intelligent network and it could understand human’s language more semantically and make the communication easier between human and computers.But,the current technology for the semantic search is hard to apply.Because some meta data should be annotated to each web pages,then the search engine will have the ability to understand the users intend.However,annotate every web page is very time-consuming and leads to inefficiency.So,this study designed an ontology-based approach to improve the current traditional keyword-based search and emulate the effects of semantic search.And let the search engine can understand users more semantically when it gets the knowledge.
基金The National Natural Science Foundation of China(No60425206,90412003)the Foundation of Excellent Doctoral Dis-sertation of Southeast University (NoYBJJ0502)
文摘A new mapping approach for automated ontology mapping using web search engines （such as Google） is presented. Based on lexico-syntactic patterns, the hyponymy relationships between ontology concepts can be obtained from the web by search engines and an initial candidate mapping set consisting of ontology concept pairs is generated. According to the concept hierarchies of ontologies, a set of production rules is proposed to delete the concept pairs inconsistent with the ontology semantics from the initial candidate mapping set and add the concept pairs consistent with the ontology semantics to it. Finally, ontology mappings are chosen from the candidate mapping set automatically with a mapping select rule which is based on mutual information. Experimental results show that the F-measure can reach 75% to 100% and it can effectively accomplish the mapping between ontologies.
基金The National Natural Science Foundation of China(No60403027)
文摘To integrate reasoning and text retrieval, the architecture of a semantic search engine which includes several kinds of queries is proposed, and the semantic search engine Smartch is designed and implemented. Based on a logical reasoning process and a graphic user-defined process, Smartch provides four kinds of search services. They are basic search, concept search, graphic user-defined query and association relationship search. The experimental results show that compared with the traditional search engine, the recall and precision of Smartch are improved. Graphic user-defined queries can accurately locate the information of user needs. Association relationship search can find complicated relationships between concepts. Smartch can perform some intelligent functions based on ontology inference.
文摘目的通过梳理中医专家问诊思维导图,采用本体和语义网技术探索构建中医专家问诊信息模型的方法。方法中医专家问诊信息模型是由中医专家问诊数据采集信息模型和中医专家问诊知识库本体两部分组成,基于对中医专家问诊思维导图的梳理,结合已发表的论文和专著中所载各类中医病-证-症量表、已发布的各专病标准数据集、中医和中西医指南/专家共识、高校教材、中医问诊相关国家标准和行业标准等,对研究素材中的中医专家问诊数据采集相关信息框架提炼,完成中医专家问诊数据采集信息模型的构建;参考复用中医药语言系统(Traditional Chinese Medicine language systems,TCMLS)、中医临床术语系统(Traditional Chinese Medicine clinical terminological systems,TCMCTS)、国标、行标中的术语进行筛选、合并、分类,确立领域概念,结合中医临床问诊实际情况和呼吸科专病中医问诊思维导图,采用人工知识抽取方法,构建中医专家问诊知识库相关语义关系,采用七步法及protégé本体工具构建中医专家问诊知识库本体,实现中医专家问诊信息的知识推理表示。结果成功绘制了中医专家问诊思维导图,初步完成了中医专家问诊信息模型的构建,实现了中医专家问诊知识库的结构化表达。结论整合了中医专家问诊相关知识,构建了中医专家问诊信息模型,规范了中医专家问诊知识库信息化表达,为中医临床专科问诊信息化研究的创新发展提供了借鉴和参考。
文摘Radiology doctors perform text-based image retrieval when they want to retrieve medical images.However,the accuracy and efficiency of such retrieval cannot keep up with the requirements.An innovative algorithm is being proposed to retrieve similar medical images.First,we extract the professional terms from the ontology structure and use them to annotate the CT images.Second,the semantic similarity matrix of ontology terms is calculated according to the structure of the ontology.Lastly,the corresponding semantic distance is calculated according to the marked vector,which contains different annotations.We use 120 real liver CT images(divided into six categories)of a top three-hospital to run the algorithm of the program.Result shows that the retrieval index"Precision"is 80.81%,and the classification index"AUC(Area Under Curve)"under the"ROC curve"(Receiver Operating Characteristic)is 0.945.
文摘以经济学领域本体为例,首先研究SemSORD基本原理和方法,然后提出基于关系数据库关键词检索(Keyword Search over Relational Databases,KSORD)技术实现的关系数据库语义检索模型,并实现相应的原型系统Si-SEEKER,最后提出该领域的研究挑战和技术发展趋势.
