面向本体学习的粒计算方法研究
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摘要
随着Web发展,面向主题的领域数据源(本文称为领域信息系统)不仅越来越多,而且其包含的信息非常丰富。这类数据源可以归属于半结构化数据源,具有内部结构不完整、内容不完备或不精确或不确定、数据量大、动态和分布式存储等特点。如今,从这类数据源中用于实现知识抽取和知识表示的方法正变得日益重要。
然而,现有的本体学习方法基本上是基于非结构化和结构化数据源下提出的,对半结构化数据源进行本体学习时一般按基于非结构化方法进行相应处理,而没有充分考虑半结构化数据源本身具有的隐含结构特征。从现有的文献资料看很少专门介绍基于半结构化数据源的本体学习方法与技术的阐述。因此,从领域信息系统中实现知识抽取以构建近似本体必然给本体学习算法带来很大的挑战。
另一方面,虽然在粒计算数据挖掘方面有不少学者进行了广泛深入的研究,但从领域信息系统这类数据源中进行基于粒计算的数据挖掘方法研究还不多见。由于粒计算在不完备、不确定信息处理和对复杂问题近似求解方面具有特殊的优势,所以针对领域信息系统,通过研究基于粒计算的数据挖掘方法不仅扩展数据挖掘、知识发现的理论与方法,而且将为在复杂数据源下的本体学习提供一条有效的途径。
本文以粒计算理论为指导,针对领域信息系统就面向本体学习的粒计算方法进行了一些探索性的研究工作。
本文的主要贡献表现在以下几个方面:
(1)通过对领域信息系统中信息函数的扩展,给出了领域信息系统的形式化描述和相应的粗糙集,提出了面向本体学习的领域概念粒度空间模型
首先,由于领域信息系统可以归属于一种半结构化数据,具有数据不完备又有数据值是多值等特点,需要对信息函数进行扩展。通过扩展的信息函数可以定义领域信息系统中对象之间的各种关系,包括等价关系,由此给出了相应的粗糙集模型,从而拓展了粗糙集应用环境。
其次,从面向本体概念学习和分类学习的需要出发,针对领域信息系统提出了一种逐层粒化方法生成具有不同抽象程度的信息粒(本文称为对象粒),并从粒计算的近似概念观点出发定义了对象粒的特征描述和相应的特征支持集,构建了领域对象粒度空间。将对象粒及其特征描述结合为一个整体定义为概念粒,以此来体现领域(近似)概念。并通过层次粒化所生成的领域对象粒度空间诱导出相应的领域概念粒度空间。讨论了概念粒度空间中概念粒之间的关系、概念粒之间的运算、有关性质和领域不确定信息的粒近似表示,分析了概念粒度空间模型的特点。并将基于领域信息系统的本体概念学习和分类关系归结为概念粒度空间生成的过程。
(2)针对领域本体概念和概念之间关系获取的需要,提出领域多层次概念获取的粒计算方法
基于所提出的领域概念粒度空间模型,针对领域信息系统中特征值具有不确定或不精确的情形定义了具体的粒化准则,提出领域多层次概念获取算法(CGS),从而为领域信息系统的本体概念学习和分类关系学习提供了一种有效的解决方法和实现技术。并在该算法的基础上针对领域信息系统具有动态特征和分布性特征提出了领域多层次概念获取的增量方法(CGS2),从而能有效地适应动态数据源的需要。通过算法测试比较、原型演示测试,以及本体学习原型演示系统的仞步应用表明所提出的方法是有效的。
(3)针对领域本体关系概念获取的需要,提出领域多维多层次之间关联关系挖掘的粒计算方法
通过关联关系的挖掘来实现领域本体关系概念及非分类关系的学习是一种经常使用的方法。先前的方法是针对非结构化数据源在已经具有概念集的基础上发现概念之间的关联关系。本文应用特征值域概念层次结构和关联关系挖掘的优化策略,提出了基于粒计算的领域多维多层次关联关系挖掘算法(G—Approach)。对所提出的算法进行详细的实例说明,并选择不同类型数据集和其他典型挖掘方法进行不同角度的测试与比较。测试结果表明了所提出的方法是有效的。另外,本文所提出的领域多维多层次之间关联关系挖掘的粒计算方法是直接运行于领域信息系统上,用于发现具有不同概念层次之间的关系概念。因此,所提出的方法不仅扩展了对复杂数据源数据挖掘方法,而且为领域本体关系概念学习提供了一种有效的解决方法与实现技术。
同时基于G-Approach方法提出领域多维多层次间关联关系的粒计算增量挖掘方法(G-Approach2),从而能适应动态的或分布式的领域信息系统的需要。实验测试与比较结果表明对非稠密型数据源G-Approach2方法比G-Approach方法具有更好的性能,但对完全属于稠密型数据集的测试结果却不如直接采用CGS方法好。因此,这需要我们进一步深入分析研究,以图改进增量方法使之能对稠密型数据集有更好的性能。
(4)针对本体非分类关系学习的需要,提出基于不同概念粒度空间的概念粒间交叉关系获取方法
针对同一领域信息系统从不同的角度或侧面构建不同领域概念粒度空间,通过分析粒间上下文,提出了基于不同领域概念粒度空间的概念粒间交叉关系获确椒āＳ纱宋恿煊蛐畔⑾低辰辛煊虮咎宓姆欠掷嘌疤峁┝艘恢钟行У慕饩龇椒ㄓ胧迪旨际酢?
(5)提出基于粒计算的本体学习框架
结合形式概念格提出基于粒计算的简单本体生成算法;提出基于粒计算的本体学习框架。该框架主要包括三个部分,一是领域概念获取,二是领域概念间关系的获取,三是将所获取的概念和关系映射为本体类和关系,包括类映射、关系映射和实例映射等;基于该框架和面向本体学习的粒计算方法设计了相应的原型演示系统,通过具体的数据集的测试运行验证了所提出的框架和算法是有效的。
With the development of web, there are an increasing number of subject-oriented domain data sources (the domain information system in this dissertation) with substantial information. This kind of data source is semi-structured, which has characteristics of incomplete internal structure, incomplete, imprecise or uncertain contents, a mass of data, dynamic and distributed storage and so on. Approaches to implementing knowledge extraction and knowledge representation from this kind of data source have become much more important now.
However, the existing ontology learning methods are typically proposed based on unstructured and structured data sources, whereas corresponding treatment of ontology learning for semi-structured data sources usually bases on unstructured methods, which fail to fully consider the hidden structural features of data sources. The existing literature seldom covers ontology learning methods and technologies based on semi-structured data sources. Therefore, with this kind of semi-structured data of the domain information system, big challenges await ontology learning algorithms.
On the other hand, although granular computing (GrC) methods in data mining have been explored widely and deeply by many scholars, there are few based on data source in the domain information system in data mining. It is important to research approaches to ontology learning oriented granular computing, which has theoretical significance and application prospect in that it will not only extend theories and methods of data mining and knowledge discovery, but also provide an effective way for ontology learning in complicated data sources.
This dissertation is set in the domain information system with the guideline of the GrC theory, focusing on GrC data mining and oriented toward ontology learning, aiming to carry on some explorative work in this field.
What has been explored in this dissertation are as follows:
(1) Through the expansion of information functions in the domain information system, formal description and corresponding rough sets of the system are given, and then the domain concept granular space model oriented ontology learning is proposed
Firstly, the domain information system should expand information functions, due to the fact that the system is semi-structured data with the features of incomplete and multi-valued data and so on. Through the expansion of information functions, various relations among objects in the domain information system, including equivalent relation, can be defined, so corresponding rough set model is proposed, and application environment of rough set is extended.
Secondly, to meet the need of concept and taxonomic relation learning in ontology learning, one method of hierarchy granulating with respect to the domain information system is proposed. The method can generate information granules of different abstract degrees (referred to as object granules in this dissertation). Feature descriptions of object granules and corresponding feature support sets are defined from the approximate concept of GrC, and the domain object granular space is constructed. Concept granule is defined by combining object granule with its corresponding feature description, which represents the domain (approximate) concept. Then the domain object granular space, which is generated by hierarchy granulating, induces the corresponding domain concept granular space. The relationship and operation between object granules, corresponding properties and the representation of granule approximation in the domain uncertain information are discussed, and some features of concept granular space model are analyzed. And, ontology concept learning and taxonomic relation learning are formulated as the generating process of the domain concept granular space based on the domain information system.
(2) In response to the need of acquiring concepts and their taxonomic relation in ontology learning, a multi-hierarchy concept acquisition approach based on granular computing is proposed
Given the domain concept granular space model and the situation of uncertain or imprecise characteristic value in the domain information system, the granulating criterion is defined and the domain multi-hierarchy concept acquisition algorithm (CGS) is proposed. Therefore it provides one feasible method and technology for ontology concept learning and taxonomic relation learning of the domain information system. And based on the algorithm, the increment method of the domain multi-hierarchy concept acquisition (CGS2) is proposed according to the dynamic and distributed characteristics on the domain information system, thus it can be adapted to the needs of dynamic data sources effectively. Through the comparison of algorithm testing, prototype demonstration testing and preliminary application of prototype demonstration system of ontology learning, the proposed methods are proven to be effective.
(3) In response to the need of relation concept acquisition in ontology learning, a granular computing method for mining association relation with domain multi-dimension and multi-level is proposed
It is often done to attain relation concept and non-taxonomic relation in ontology learning through the learning of association relation. But this method finds association relation between concepts based on concept sets. This dissertation puts forward an approach (G-Approach) to mining domain multi-dimension and multi-level of association relation based on granular computing by employing characteristics range concept hierarchy and optimization strategy of association relation mining. Some real examples are provided to dwell on the proposed algorithm, choosing different types of data sets and other typical mining methods to test and compare from different aspects, and the test results testify to the feasibility of the approach. Besides, the proposed approach is running on the domain information system directly, and association relation between different concept hierarchies can be found. Therefore, the approach not only extends the data mining method to complicated data sources, but also provides one workable solution and technology of elation concept learning in ontology learning.
Meanwhile, an increment approach (G-Approach2) to mining association relation with domain multi-dimensional and multi-level based on G-Approach is proposed, which can cater to the need of dynamic or distributed domain information system. The results of comparison and testing show that capability of G- Approach2 is better than that of G-Approach in terms of non dense data source, while the test results on completed dense data sets show the CGS method is much better. Therefore, we should explore further to improve the increment method targeted at dense data sets.
(4) In response to the need of ontology non-taxonomic relation learning, the acquisition method of cross relationship between concept granules based on different concept granular spaces is proposed
Given different aspects or sides constructing different domain concept granular spaces from the same domain information system, an approach to acquiring cross relationship between concept granules is proposed based on the context analysis between concept granules. Therefore, it provides one feasible method and technology of ontology non-taxonomic relation learning based on the domain information system.
(5) Ontology learning framework based on granular computing is proposed
Simple ontology generating algorithm based on granular computing combined with formal concept lattice is proposed, and a framework for ontology learning based on granular computing is also established. The framework mainly includes three parts- domain concept acquisition, domain concept relation acquisition, and mapping of the concepts and relations to ontology class and relationship, including class mapping, relationship mapping and instance mapping and so on. Then according to the approaches proposed to ontology learning oriented granular computing and the framework, corresponding prototype demonstration system is designed, the proposed framework and approaches are effective proved by operation testing on the given data set.
然而,现有的本体学习方法基本上是基于非结构化和结构化数据源下提出的,对半结构化数据源进行本体学习时一般按基于非结构化方法进行相应处理,而没有充分考虑半结构化数据源本身具有的隐含结构特征。从现有的文献资料看很少专门介绍基于半结构化数据源的本体学习方法与技术的阐述。因此,从领域信息系统中实现知识抽取以构建近似本体必然给本体学习算法带来很大的挑战。
另一方面,虽然在粒计算数据挖掘方面有不少学者进行了广泛深入的研究,但从领域信息系统这类数据源中进行基于粒计算的数据挖掘方法研究还不多见。由于粒计算在不完备、不确定信息处理和对复杂问题近似求解方面具有特殊的优势,所以针对领域信息系统,通过研究基于粒计算的数据挖掘方法不仅扩展数据挖掘、知识发现的理论与方法,而且将为在复杂数据源下的本体学习提供一条有效的途径。
本文以粒计算理论为指导,针对领域信息系统就面向本体学习的粒计算方法进行了一些探索性的研究工作。
本文的主要贡献表现在以下几个方面:
(1)通过对领域信息系统中信息函数的扩展,给出了领域信息系统的形式化描述和相应的粗糙集,提出了面向本体学习的领域概念粒度空间模型
首先,由于领域信息系统可以归属于一种半结构化数据,具有数据不完备又有数据值是多值等特点,需要对信息函数进行扩展。通过扩展的信息函数可以定义领域信息系统中对象之间的各种关系,包括等价关系,由此给出了相应的粗糙集模型,从而拓展了粗糙集应用环境。
其次,从面向本体概念学习和分类学习的需要出发,针对领域信息系统提出了一种逐层粒化方法生成具有不同抽象程度的信息粒(本文称为对象粒),并从粒计算的近似概念观点出发定义了对象粒的特征描述和相应的特征支持集,构建了领域对象粒度空间。将对象粒及其特征描述结合为一个整体定义为概念粒,以此来体现领域(近似)概念。并通过层次粒化所生成的领域对象粒度空间诱导出相应的领域概念粒度空间。讨论了概念粒度空间中概念粒之间的关系、概念粒之间的运算、有关性质和领域不确定信息的粒近似表示,分析了概念粒度空间模型的特点。并将基于领域信息系统的本体概念学习和分类关系归结为概念粒度空间生成的过程。
(2)针对领域本体概念和概念之间关系获取的需要,提出领域多层次概念获取的粒计算方法
基于所提出的领域概念粒度空间模型,针对领域信息系统中特征值具有不确定或不精确的情形定义了具体的粒化准则,提出领域多层次概念获取算法(CGS),从而为领域信息系统的本体概念学习和分类关系学习提供了一种有效的解决方法和实现技术。并在该算法的基础上针对领域信息系统具有动态特征和分布性特征提出了领域多层次概念获取的增量方法(CGS2),从而能有效地适应动态数据源的需要。通过算法测试比较、原型演示测试,以及本体学习原型演示系统的仞步应用表明所提出的方法是有效的。
(3)针对领域本体关系概念获取的需要,提出领域多维多层次之间关联关系挖掘的粒计算方法
通过关联关系的挖掘来实现领域本体关系概念及非分类关系的学习是一种经常使用的方法。先前的方法是针对非结构化数据源在已经具有概念集的基础上发现概念之间的关联关系。本文应用特征值域概念层次结构和关联关系挖掘的优化策略,提出了基于粒计算的领域多维多层次关联关系挖掘算法(G—Approach)。对所提出的算法进行详细的实例说明,并选择不同类型数据集和其他典型挖掘方法进行不同角度的测试与比较。测试结果表明了所提出的方法是有效的。另外,本文所提出的领域多维多层次之间关联关系挖掘的粒计算方法是直接运行于领域信息系统上,用于发现具有不同概念层次之间的关系概念。因此,所提出的方法不仅扩展了对复杂数据源数据挖掘方法,而且为领域本体关系概念学习提供了一种有效的解决方法与实现技术。
同时基于G-Approach方法提出领域多维多层次间关联关系的粒计算增量挖掘方法(G-Approach2),从而能适应动态的或分布式的领域信息系统的需要。实验测试与比较结果表明对非稠密型数据源G-Approach2方法比G-Approach方法具有更好的性能,但对完全属于稠密型数据集的测试结果却不如直接采用CGS方法好。因此,这需要我们进一步深入分析研究,以图改进增量方法使之能对稠密型数据集有更好的性能。
(4)针对本体非分类关系学习的需要,提出基于不同概念粒度空间的概念粒间交叉关系获取方法
针对同一领域信息系统从不同的角度或侧面构建不同领域概念粒度空间,通过分析粒间上下文,提出了基于不同领域概念粒度空间的概念粒间交叉关系获确椒āＳ纱宋恿煊蛐畔⑾低辰辛煊虮咎宓姆欠掷嘌疤峁┝艘恢钟行У慕饩龇椒ㄓ胧迪旨际酢?
(5)提出基于粒计算的本体学习框架
结合形式概念格提出基于粒计算的简单本体生成算法;提出基于粒计算的本体学习框架。该框架主要包括三个部分,一是领域概念获取,二是领域概念间关系的获取,三是将所获取的概念和关系映射为本体类和关系,包括类映射、关系映射和实例映射等;基于该框架和面向本体学习的粒计算方法设计了相应的原型演示系统,通过具体的数据集的测试运行验证了所提出的框架和算法是有效的。
With the development of web, there are an increasing number of subject-oriented domain data sources (the domain information system in this dissertation) with substantial information. This kind of data source is semi-structured, which has characteristics of incomplete internal structure, incomplete, imprecise or uncertain contents, a mass of data, dynamic and distributed storage and so on. Approaches to implementing knowledge extraction and knowledge representation from this kind of data source have become much more important now.
However, the existing ontology learning methods are typically proposed based on unstructured and structured data sources, whereas corresponding treatment of ontology learning for semi-structured data sources usually bases on unstructured methods, which fail to fully consider the hidden structural features of data sources. The existing literature seldom covers ontology learning methods and technologies based on semi-structured data sources. Therefore, with this kind of semi-structured data of the domain information system, big challenges await ontology learning algorithms.
On the other hand, although granular computing (GrC) methods in data mining have been explored widely and deeply by many scholars, there are few based on data source in the domain information system in data mining. It is important to research approaches to ontology learning oriented granular computing, which has theoretical significance and application prospect in that it will not only extend theories and methods of data mining and knowledge discovery, but also provide an effective way for ontology learning in complicated data sources.
This dissertation is set in the domain information system with the guideline of the GrC theory, focusing on GrC data mining and oriented toward ontology learning, aiming to carry on some explorative work in this field.
What has been explored in this dissertation are as follows:
(1) Through the expansion of information functions in the domain information system, formal description and corresponding rough sets of the system are given, and then the domain concept granular space model oriented ontology learning is proposed
Firstly, the domain information system should expand information functions, due to the fact that the system is semi-structured data with the features of incomplete and multi-valued data and so on. Through the expansion of information functions, various relations among objects in the domain information system, including equivalent relation, can be defined, so corresponding rough set model is proposed, and application environment of rough set is extended.
Secondly, to meet the need of concept and taxonomic relation learning in ontology learning, one method of hierarchy granulating with respect to the domain information system is proposed. The method can generate information granules of different abstract degrees (referred to as object granules in this dissertation). Feature descriptions of object granules and corresponding feature support sets are defined from the approximate concept of GrC, and the domain object granular space is constructed. Concept granule is defined by combining object granule with its corresponding feature description, which represents the domain (approximate) concept. Then the domain object granular space, which is generated by hierarchy granulating, induces the corresponding domain concept granular space. The relationship and operation between object granules, corresponding properties and the representation of granule approximation in the domain uncertain information are discussed, and some features of concept granular space model are analyzed. And, ontology concept learning and taxonomic relation learning are formulated as the generating process of the domain concept granular space based on the domain information system.
(2) In response to the need of acquiring concepts and their taxonomic relation in ontology learning, a multi-hierarchy concept acquisition approach based on granular computing is proposed
Given the domain concept granular space model and the situation of uncertain or imprecise characteristic value in the domain information system, the granulating criterion is defined and the domain multi-hierarchy concept acquisition algorithm (CGS) is proposed. Therefore it provides one feasible method and technology for ontology concept learning and taxonomic relation learning of the domain information system. And based on the algorithm, the increment method of the domain multi-hierarchy concept acquisition (CGS2) is proposed according to the dynamic and distributed characteristics on the domain information system, thus it can be adapted to the needs of dynamic data sources effectively. Through the comparison of algorithm testing, prototype demonstration testing and preliminary application of prototype demonstration system of ontology learning, the proposed methods are proven to be effective.
(3) In response to the need of relation concept acquisition in ontology learning, a granular computing method for mining association relation with domain multi-dimension and multi-level is proposed
It is often done to attain relation concept and non-taxonomic relation in ontology learning through the learning of association relation. But this method finds association relation between concepts based on concept sets. This dissertation puts forward an approach (G-Approach) to mining domain multi-dimension and multi-level of association relation based on granular computing by employing characteristics range concept hierarchy and optimization strategy of association relation mining. Some real examples are provided to dwell on the proposed algorithm, choosing different types of data sets and other typical mining methods to test and compare from different aspects, and the test results testify to the feasibility of the approach. Besides, the proposed approach is running on the domain information system directly, and association relation between different concept hierarchies can be found. Therefore, the approach not only extends the data mining method to complicated data sources, but also provides one workable solution and technology of elation concept learning in ontology learning.
Meanwhile, an increment approach (G-Approach2) to mining association relation with domain multi-dimensional and multi-level based on G-Approach is proposed, which can cater to the need of dynamic or distributed domain information system. The results of comparison and testing show that capability of G- Approach2 is better than that of G-Approach in terms of non dense data source, while the test results on completed dense data sets show the CGS method is much better. Therefore, we should explore further to improve the increment method targeted at dense data sets.
(4) In response to the need of ontology non-taxonomic relation learning, the acquisition method of cross relationship between concept granules based on different concept granular spaces is proposed
Given different aspects or sides constructing different domain concept granular spaces from the same domain information system, an approach to acquiring cross relationship between concept granules is proposed based on the context analysis between concept granules. Therefore, it provides one feasible method and technology of ontology non-taxonomic relation learning based on the domain information system.
(5) Ontology learning framework based on granular computing is proposed
Simple ontology generating algorithm based on granular computing combined with formal concept lattice is proposed, and a framework for ontology learning based on granular computing is also established. The framework mainly includes three parts- domain concept acquisition, domain concept relation acquisition, and mapping of the concepts and relations to ontology class and relationship, including class mapping, relationship mapping and instance mapping and so on. Then according to the approaches proposed to ontology learning oriented granular computing and the framework, corresponding prototype demonstration system is designed, the proposed framework and approaches are effective proved by operation testing on the given data set.
引文
[1]邓志鸿,唐世渭,张铭,扬冬青,陈捷.Ontology研究综述[J].北京大学学报(自然科学版),2002,38(5):730-738
[2]刘红阁,郧丽萍,张少方.本体论的研究和应用现状[J].信息技术快报,2005,3(1):1-12
[3]李善平.本体论研究综述[J].计算机研究与发展,2004,41(7):1041-1052
[4]Studer R,Benjamins V R,Fensel D.Knowledge engineering,principles and methods[J].Data and Knowledge Engineering.1998,25(22):161-197
[5]杜小勇,李曼,王珊.本体学习研究综述[J].软件学报,2006,17(9):1837-1847
[6]刘柏嵩,基于Web的通用本体学习研究[Ph.D],浙江大学,杭州,2007
[7]孔敬,本体学习:原理、方法与相关进展[J].情报学报,2006,25(6):657-665
[8]Nierenburg S,Beale S,Mahesh K,et al.Lexicons in the mikrokosmos project[C].In:Proceedings of the AISB Workshop on Multilinguality in the Lexicon.Brighton:Ulrich Heid,1996.26-33
[9]Maedche A,staab S.Ontology learning for the semantic Web[J].IEEE Intelligent Systems,2001,16(2):72-79
[10]Gomez-Perez A,Manzano-Macho D.A survey of ontology learning methods and techniques[EB],http://Ontoweb.org/Members/ruben/Deliverable%201.5,2005
[11]刘清.Rough集及Rough推理[M].北京:科学出版社,2005,第3版
[12]苗夺谦,王国胤,刘清,林早阳,姚一豫.粒计算“过去、现在与展望”[M].北京:科学出版社,2007
[13]王国胤,张清华,胡军.粒计算研究综述[J].智能系统学报,2007,2(6):6-26
[14]李道国,苗夺谦,张东星,张红云.粒度计算研究综述[J].计算机科学,2005,32(9):1-12
[15]邓蔚,王国胤,吴渝.粒计算综述[J].计算机科学,2004,31(10):178-181
[16]Bargiela A,Pedrycz W.Granular computing:an introduction[M].Kluwer Academic Publishers,Boston,2002.
[17]Doherty P,Grabowski M,Lukaszewicz W,Szalas A.Towards a framework for approximate ontologies[J].Fundamenta Informaticae,2003,57(2):147-165
[18]Stojanovic L,Stojanovic N,Volz R.Migrating data-intensive web sites into the semantic Web[C].In:Proceedings of the 17th ACM Symp.on Applied Computing.New York:ACM Press,2002.1100-1107
[19]Astrova I.Reverse engineering of relational database to ontologies[C].In:Proceedings of the ESWC 2004.Heidelberg:Springer-Verlag,2004.327-341.
[20]Missikoff M.Text mining techniques to automatically enrich a domain ontology[J],Applied Intelligence,2003,18:323-340
[21]Shamsfard M,Barforoush A A.Learning ontologies from natural language texts[J].International Journal Human-Computer Studies,2004,60(1):17-63
[22]Cimiano P,Volker J.Text2Onto—a framework for ontology learning and data-driven change discovery[C].In:NLDB 2005,Alicante,Spain,2005.Berlin,Herdelberg:Springer-Verlag,2005.227-238
[23]Missikoff M,Navigli R and Velardi P.Integrated approach for web ontology learning and engineering[J].IEEE Computer,2002,35(11):60-63
[24]Navigli R,Velardi P,Gangemi A.Ontology learning and its application to automated terminology translation[J].IEEE Intelligent Systems,2003,18(1):22-31
[25]Bisson G,Nedellec C,Canamero L.Designing clustering methods for ontology building—the mo'k workbench[C].In:Proceedings of the ECAI Ontology Learning Workshop,2000.13-19
[26]杜波,田怀风,王立,陆汝占.基于多策略的专业领域术语抽取器的设计[J].计算机工程,2005,31(14):159-160
[27]郑家恒,卢娇丽.关键词抽取方法的研究[J].计算机工程,2005,31(18):194-196
[28]陈文亮,朱靖波,姚天顺.基于Bootstrapping的领域词汇自动获取[C].第7届全国计算语言学联合学术会议论文集(JSCL 2003).北京:清华大学出版社,2003.67-72
[29]Faure D,Nedellec C.A corpus-based conceptual clustering method for verb frames and ontology acquisition[C].In:Proceedings of the LREC,Granada,1998.5-12
[30]Maedche A,Staab S.Discovering conceptual relation from text[C].In:Proceedings of the ECAI 2000.Amsterdam:IOS Press,2000.321-325
[31]Nakaya N,Kurematsu M,Yamaguchi T.A domain ontology development environment using a MRD and text corpus[C].In:Proceedings of the Joint Conference on Knowledge based Software Engineering,2002
[32]Velardi P,Fabriani P,Missikoff M.Using text processing techniques to automatically enrich a domain ontology[C].In:Proceedings of the International Conference on Formal Ontology in Information Systems,Ogunquit,2001.270-284
[33]Cimiano P,Vaolker J.Towards large-scale,unsupervised and ontology-based named entity recognition[R].Technical Report,AIFB,University of Karlsruhe,2004
[34]Charniak E,Berland M.Finding parts in very large corpus[C].In:Proceedings of the 37~(th)Annual Meeting of the Association for Computational Linguistics on Computational Linguistics,Maryland,1999.Morristown:Association for Computational Linguistics,1999.57-64
[35]Kavalec M,Sv(?)tek V.A study on automated relation labelling in ontology learning[EB].http://nb.vse.cz/～svatek/olp05.pdf
[36]Papatheodorou C,Vassiliou A,Simon B.Discovery of ontologies for learning resources using word-based clustering[C].In:Proceedings of the World Conference on Educational Multimedia,Hypermedia and Telecommunications.Chesapeake:AACE,2002.1523-1528.
[37]Yao Y Y.Granular computing for data mining[C].In:Proceedings of SPIE Conference on Data Mining,Intrusion Detection,Information Assurance,and Data Networks Security,2006.1-12
[38]郑征.相容粒度空间模型及其应用研究[Ph.D].中国科学院,北京,2006
[39]蒙祖强.基于分类模型的知识发现机理和方法研究[Ph.D].中南大学,长沙,2004
[40]范明,孟小峰等译.数据挖掘—概念与技术[M].北京:机械工业出版社,2001
[41]Margaret H.Dunham.Data mining—introductory and advanced topics[M].北京:清华大学出版社,2003
[42]张铃,张钹.模糊商空间理论(模糊粒度计算方法)[J].软件学报,2003,14(4):770-776
[43]陈万里.基于商空间理论和粗糙集理论的粒计算模型研究[Ph.D].安徽大学,合肥,2005
[44]Lin T Y,Louie E.Modeling the real world for data mining:granular computing approach[C].In:Proceeding of Joint 9~(th) IFSA World Congress and 20~(th) NAFIPS International Conference,Vancouver,Canada,2001.3044-3049
[45]Lin T Y,Hu X H,Louie E.A fast association rule algorithm based on bitmap and granular computing[C].In:Proceedings of the IEEE International Conference Fuzzy Systems,StLouis,Missouri,2003.678-683
[46]Lin T Y,Yin P.Finding the reduct by granular computing using bit maps:decision rules mining[C].In:Data Mining and Knowledge Discovery:Theory,Tools,and Technology,Orlando,FI:USA,2003.82-93.
[47]Lin T Y,Eric L.Finding association rules by granular computing:fast algorithms for finding association rules[C].In:Data Mining,Rough Sets and Granular Computing,Physica-Verlag,2002.23-42
[48]Wang G Y,Hu F,Huan G H,et al.A granular computing model based on tolerance relation[J].The Journal of China Universities of Post s and Telecommunications,2005,12(3):86-90
[49]胡峰,黄海,王国胤等.不完备信息系统的粒计算方法[J].小型微型计算机系统,2005,26(8):1335-1339
[50]安久江,甘全,王国胤等.一种基于粒计算的规则知识获取算法[J].计算科学,2005,32(8):137-140
[51]邓蔚,王国胤,吴渝.基于近似分割的粒计算规则获取算法[C].中国人工智能学会第11届学术年会论文集,武汉,2005
[52]An J J,Wang G Y,Wu Y,et al.A rule generation algorithm based on granular computing[C].2005 IEEE International Conference on Granular Computing,Beijing,2005.102-107
[53]Yao Y Y,Yao J T.Granular computing as a basis for consistent classification problems[C].In:PAKDD 2002 Workshop:Towards Foundation of Data Mining,Communications of Institute of Information and Computing Machinery,2003.101-106.
[54]Chen Y H,Yao Y Y.Multiview intelligent data analysis based on granular computing[C].In:Proceedings of 2006 IEEE International Conference on Granular Computing,2006.281-286
[55]Yao Y Y.Stratified rough sets and granular computing[C].In:Proceedings of the 18th International Conference of the North American Fuzzy Information Processing Society[C].New York,1999.800-804
[56]Yao Y Y.Information granulation and rough set approximation[J].International Journal of Intelligent Systems,2001,16(1):87-104
[57]Pawlak Z.Rough sets[J].International Journal of Computer and Information Sciences,1982,11:341-356
[58]Pawlak Z.Rough sets,Theoretical Aspects of Reasoning about Data[M].Kluwer Academic Publishers,Dordrecht,1991
[59]王国胤.Rough集理论与知识获取[M].西安:西安交通大学出版社,2001
[60]张文修,吴伟志,梁吉业等.粗糙集理论与方法[M].北京:科学出版社,2001.
[61]史忠植.知识发现[M].北京:科学出版社,2002
[62]张丽娟,李舟军,陈火旺.粒度计算及其在数据挖掘中的应用[J].计算机科学,2005,32(12):178-180
[63]Wang G Y,Zhao J.An J J,et al.A comparative study of algebra viewpoint and information viewpoint in attribute reduction[J].Fundamenta Informaticae,2005,68(3):289-301
[64]王国胤.决策表核属性的计算方法[J].计算机学报,2003,26(5):611-615
[65]刘少辉.知识发现中粗糙集理论的研究[Ph.D].中国科学院研究生院,北京,2003
[66]王庆东.基于粗糙集的数据挖掘方法研究[Ph.D].浙江大学,杭州,2005
[67]Yao Y Y.Concept formation and learning:a cognitive informatics perspective[C].In:Proceedings of the 3rd IEEE International Conference on Cognitive Informatics,2004.42-51
[68]Yao Y Y.Modeling data mining with granular computing[C].In:Proceedings of the 25th Annual International Computer Software and Applications Conference,2001.638-643
[69]Zhao Y,Yao Y Y,Yao,J T.Level construction of decision trees for classification[J].International Journal of Software Engineering and Knowledge Engineering,2006,16(1):103-123
[70]Yao J T,Yao Y Y.Induction of classification rules by granular computing[C].In:Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing (RSCTC 2002),Lecture Notes in Artificial Intelligence 2475,2002.331-338
[71]Zhao Y.Coarsening classification rules on basis of granular computing[C].In:Lecture Notes in Artificial Intelligence 3060,Berlin:Springer,2004.578-579
[72]刘勇.基于粒度计算的知识发现研究及其应用[Ph.D].浙江大学,杭州,2006
[73]耿志强.流程工业粒度数据挖掘技术研究与应用[Ph.D].北京化工大学,北京,2005
[74]卜东波,白硕,李国杰.聚类/分类中的粒度原理[J].计算机学报,2002,25(8):810-816
[75]刘君强.海量数据挖掘技术研究[Ph.D].浙江大学,杭州,2003
[76]Manish M,Rakesh A,Jorma R.SLIQ:a fast scalable classifier for data mining[C].In:Proceedings of the 5~(th) International Conference on Extending Database Technology,Avignon,France,1996.18-32
[77]John C,Sharer S,et al.SPRINT:a scalable parallel classifier for data mining[R].Research Report,IBM Almaden Research Center,1996
[78]Qin Z R,Wang G Y,Wu Y,et al.A scalable rough set knowledge reduction algorithm[C].In:Lecture Notes In Artificial Intelligence 3066.Berlin:Springer,2004.445-454
[79]Doningos P.Knowledge acquisition from examples via multiple models[C].In:Proceedings of the 14th International Conference on Machine Learning,Morgan Kaufman,USA,1997.98-106
[80]Prodromidis A L,Chan P K,Stolfo S J.Meta-learning in distributed data mining system:Issues and approaches[C].In:Advances in distributed and parallel Knowledge Discovery.Menlo Park:Association for the Advancement of Artificial Intelligence Press,2000
[81]Wu X D,Zhang S C.Synthesizing high-frequency rules form different data sources[J].IEEE Transaction on Knowledge and Data Engineering,2003,15(2):353-367
[82]刘宗田.属性最小约简的增量式算法[J].电子学报,1999,27(11):96-98
[83]Hu F,Wang G Y,Huang H,et al.Incremental attribute reduction based on elementary sets[C].In:Lecture Notes in Artificial Intelligence 3641.Berlin:Springer,2005.185-193
[84]Wang G Y.Domain-oriented data-driven data mining based on rough set[J].Journal of Nanchang Institute of Technology,2006,25(2):46
[85]Yao Y Y,Zhong N.Granular computing using information tables[C].In:Data Mining,Rough Sets and Granular Computing,Physica-Verlag,Heidelberg,2002.102-124
[86]Yao Y.Y..Granular Computing[J].Computer Science(in chinese),2004,31:1-5
[87]Yao Y Y.A partition model of granular computing[J].In:LNCS Transactions on Rough Sets,2004.232-253
[88]Yao Y Y.Perspectives of granular computing[C].In:Proceedings of 2005 IEEE International Conference on granular computing,2005.85-90
[89]Yao Y Y.The art of granular computing[C].In:Proceeding of the International Conference on Rough Sets and Emerging Intelligent Systems Paradigms,LNAI 4585,2007.101-112
[90]Yao Y Y.The rise of granular computing[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2008,20(3):299-308
[91]张钹,张铃.问题求解的理论及应用[M].北京:清华大学出版社,1990
[92]刘清,孙辉,王洪发.粒计算研究现状及基于Rough逻辑语义的粒计算研究[J].计算机学报,2008,31(4):543-555
[93]Zadeh L A.Fuzzy sets and information granularity[C].In:Advances in Fuzzy Set Theory and Applications.Amsterdam:North-Holland,1979.3-18
[94]Hobbs J R.Granularity[C].In:Proceedings of the Ninth International Joint Conference on Artificial Intelligence,1985.432-435
[95]Zadeh L A.Fuzzy logic=computing with words[J].IEEE Transactions on Fuzzy Systems,1996,4:103-111
[96]Zadeh L A.Some reflections on soft computing,granular computing and their roles in the conception,design and utilization of information/intelligent systems[C].In:Soft Computing.Berlin:Springer-Verlag,1998.23-25
[97]T.Y.Lin.From rough sets and neighborhood systems to information granulation and computing in words[C].In:Proceedings of European Congress on Intelligent Techniques and Soft Computing,1997.1602-1607
[98]Lin T Y.Granular computing on binary relations Ⅰ:data mining and neighborhood systems[C].In:Rough Sets In Knowledge Discovery,Physica-Verlag,1998.107-121
[99]Lin T Y.Granular computing on binary relations Ⅱ:rough set representations and belief functions[C].In:Rough Sets In Knowledge Discovery,Physica-Verlag,1998.121-140
[100]Lin T Y.Granulation and nearest neighborhood:rough set approach[C].In:Granular Computing:an Emerging Paradigm,Physica-Verlag,Heidelberg,2001.125-142
[101]Lin T Y.Rough sets,fuzzy sets,data mining and granular computing[C].In:Proceedings of the 9~(th) International Conference(RSFDGrC 2003),LNCS 2639,2003.16-24
[102]Lin T Y.Granular computing:examples,intuition,and modeling[C].In:Proceedings of International Conference on Granular Computing,Beijing:China,2005.40-44
[103]Lin T Y.Granular computing Ⅱ:infrastructures for AI-engineering[C].In:Proceedings of International Conference on Granular Computing,Atlanta:USA,2006.2-7
[104]Skowron A,Stepaniuk J.Information granules and rough neurcomputing[C].In:Rough Neurocomputing:Techniques for Computing with Words,Cognitive Technologies.Berlin:Springer-Verlag,2003.43-84
[105]Skowron A.Toward intelligent systems:calculi of information granules[J].Bulletin of International Rough Set Society,2001,5(122):9-30
[106]Skowron A,Stepaniuk J,Peters,James F.Extracting patterns using information granules[J].Bulletin of International Rough Set Society,2001,5(122):135-142
[107]Polkwski L.Towards rough set foundations,metrological approach[C].In:Lecture in Artificial Intelligence 3066.Berlin:Springer-Verlag,2004.8-25
[108]Polkwski L.A calculus on granules from rough inclusions in information systems[J].Journal of Nanchang Institute of Technology,2006,25(2):22-27
[109]Liu Q.Granules and reasoning based on granular computing[C].In:Lecture Notes in Artificial Intelligence 2718.Berlin:Springer-Verlag,2003.516-526
[110]Liu Q,Jiang F,Deng D Y.Design and implement for the diagnosis software of bood viscosity syndrome based on morphology on GrC[C].In:Lecture Notes in AI 2639.Berlin:Springer-Verlag,2003.413-420
[111]刘清.粒及其分解和合并的研究[C].中国人工智能进展 中国人工智能学会第10届全国学术年会,2003.1411-1416
[112]刘清,孙辉.从Rough集的发展前景看粒计算的研究趋势[J].南昌工程学院学报,2006,25(5):1-10
[113]刘清,刘群.粒及粒计算在逻辑推理中的应用[J].计算机研究与发展,2004,41(4):546-551
[114]Liu Q.,Wang Q Y.Granular logic with closeness relation "λ" and its reasoning[C].In:Proceedings of Rough Sets,Fuzzy Sets,Data Mining and Granular Computing.Springer,Berlin,2005.709-717.
[115]刘消.G-逻辑及其归结推理[J].计算机学报,2004,27(7):865-874
[116]张维明,邓苏,罗雪山等.信息系统建模技术与应用[M].北京:电子工业出版社,1997
[117]Zadeh L A.Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J].Fuzzy Sets and Systems,1997,19:111-127
[118]Zhang L,Zhang B.The quotient space theory of problem solving[C].In:Lecture Notes in Artificial Intelligence 2639,Springer-Verlag,Berlin,2003.11-15
[119]Zhu W,Wang F Y.Reduction and axiomization of coveting generalized rough sets[J]Information Sciences,2003,152:217-230
[120]祝峰,何华灿.粗集的公理化[J].计算机学报,2000,23(3):330-333
[121]仇国芳,陈劲.概念知识系统与概念信息粒格[J].工程数学学报,2005,22(6):963-969
[122]仇国芳,荆彦玲.粗糙集与概念格[M].西安:西安交通大学出版社,2006
[123]Kryszkiewicz M.Rough set approach to incomplete information systems[J].Information Sciences,1998,112:39-49
[124]Wolterink,T.What is an Ontology?[EB].http://referaat.cs.utwente.nl/new/papers.php?Confid=1
[125]Ziarko W.Variable precision rough sets model[J].Journal of Computer and System Sciences,1993,46(1):39-59
[126]Dubois D,Prade H.Rough fuzzy sets and fuzzy rough sets[J].International Journal of General Systems,1990,17:191-208
[127]Slezak D,Ziarko W.Attribute reduction in the bayesian version of variable precision rough set model[J].Electronic Notes in Theoretical Computer Science,2003,82(4):1-11
[128]王国胤.Rough集理论在不完备信息系统中的扩充[J].计算机研究与发展,2002,39(10):1238-1243
[129]陈炯,曹付元.Rough集理论在多值信息系统中的扩充[J].山西大学学报,2006,26(12):158-160
[130]刘斓,刘清.基于粒的二进制运算的关联规则提取方法[J].南昌大学学报(理科版),2003,27(1):98-101
[131]苗夺谦,王珏.粗糙集理论中知识粗糙性与信息熵关系的讨论[J].模式识别与人工智能,1998,11(3):34-240.
[132]Pons-Porrata A,Ruiz-Shulcloper J,Martinez-Trinidad J F.RGC:a new conceptual clustering algorithm for mixed incomplete data sets[J].Mathematical and Computer Modelling,2002,36:1375-1385
[133]汪培庄.因素空间与概念描述[J].软件学报,1992,1:30.-0
[134]陈世权,程里春.模糊概念格[J].模糊系统与数学,2002,16(4):12-18
[135]强宁,刘宗田,林炜.模糊概念格的一种构造算法[J].电子学报,2005,33(2):350-353
[136]强宁.模糊概念格模型及其应用研究[Ph.D].上海大学,上海,2005
[137]胡可云,陆玉昌,石纯一.概念格及其应用进展,清华大学学报(自然科学版)[J].2000,40(9):77-81
[138]胡可云,陆玉昌,石纯一.基于概念格的分类和关联规则的集成挖掘方法[J].软件学报,2000,11(11):1478-1484
[139]刘宗田.容差近似空间的广义概念格模型研究[J].计算机学报,2000,23(1):66-70
[140]Ai-Omary A Y,Jamil M.S.A new approach of clustering based machine-learning algorithm[J].Knowledge-Based System,2006,19:248-258
[141]程勇.基于本体的不确定性知识管理的研究[Ph.D].中国科学院,北京,2005
[142]张建辉.K-means聚类算法研究及应用[Ms.D].武汉理工大学,武汉,2007
[143]UCI Machine Learning Data Repository,Http://www.ics.uci.edu/mleaen/MLReoditory.html.
[144]Agrawal R,Srikant R.Fast algorithm for mining association rules[C].In:Proceedings of the 20th International Conference on Very Large Databases.Burlington:Morgan Kaufmann Publishers,1994.487-499.
[145]Han J,Pei J,Yin Y.Mining frequent patterns without candidate generation[C].In:Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data.New York:ACM Press,2000.1-12
[146]Zaki M J,Hsiao C J.CHARM:an efficient algorithm for closed association rule mining[R].Technical Report,TR99-10,Department of Computer Science,Rensselaer Polytechnic Institute,1999
[147]Pan F,Cong G,Tung A K H,et al.CARPENTER:Finding closed patterns in long biological datasets[C].In:Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM Press,2003.637-642
[148]Agrawal R,Shafer J C.Parallel mining of association rules[J].IEEE Transactions on knowledge and date engineering,1996,8(6):962-969
[149]Cheung D W,Lee S D,Kao B.A general incremental technique for maintaining discovered association rules[C].In:Proceedings of the 5th International Conference on Database Systems for Advanced Applications.Melbourne,Australia,1997.185-194
[150]李闯,杨胜.基于粗糙集理论的ORD关联规则挖掘算法[J].计算机工程与设计,2008,29(14):3666-3668
[151]王丽丽.不完备系统中混合数据关联规则挖掘的研究[Ms.D].长沙理工大学,长沙,2008
[152]何军,刘红岩,杜小勇.挖掘多关系关联规则[J].软件学报,2007,18(11):2752-2765
[153]冯玉才,冯剑琳.关联规则的增量式更新算法[J].软件学报,1998,9(4):301-306
[154]高峰,谢剑英.发现关联规则的增量式更新算法[J].计算机工程,2000,26(12):49-50
[155]Lee S D,Cheung D W.Maintenance of discovered association rules:when to update[C].In:Proceedings of SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery(DMKD-97),Tucson,Arizona,U.S.A,1997
[156]Stepaniuk J,Skowron A.Ontological framework for approximation[C].In:RSFDGrC 2005,LNAI 3641,2005.718-727
[157]Slowinski R,Vanderpooten D.A generalized definition of rough approximations based on similarity[J].IEEE Transaction.Knowledge.Data Engineering.2000,12(2):331-336
[158]王俊红,梁吉业.概念格与粗糙集[J]山西大学学报(自然科学版),2003,26(4):307-310
[159]周文,刘宗田,陈慧琼.FCA与本体结合研究的综述[J].计算机科学,2006,33(2):8-12
[160]黄美丽,刘宗田.基于形式概念分析的领域本体构建方法研究[J].计算机科学,2006,33(1):210-212,239
[2]刘红阁,郧丽萍,张少方.本体论的研究和应用现状[J].信息技术快报,2005,3(1):1-12
[3]李善平.本体论研究综述[J].计算机研究与发展,2004,41(7):1041-1052
[4]Studer R,Benjamins V R,Fensel D.Knowledge engineering,principles and methods[J].Data and Knowledge Engineering.1998,25(22):161-197
[5]杜小勇,李曼,王珊.本体学习研究综述[J].软件学报,2006,17(9):1837-1847
[6]刘柏嵩,基于Web的通用本体学习研究[Ph.D],浙江大学,杭州,2007
[7]孔敬,本体学习:原理、方法与相关进展[J].情报学报,2006,25(6):657-665
[8]Nierenburg S,Beale S,Mahesh K,et al.Lexicons in the mikrokosmos project[C].In:Proceedings of the AISB Workshop on Multilinguality in the Lexicon.Brighton:Ulrich Heid,1996.26-33
[9]Maedche A,staab S.Ontology learning for the semantic Web[J].IEEE Intelligent Systems,2001,16(2):72-79
[10]Gomez-Perez A,Manzano-Macho D.A survey of ontology learning methods and techniques[EB],http://Ontoweb.org/Members/ruben/Deliverable%201.5,2005
[11]刘清.Rough集及Rough推理[M].北京:科学出版社,2005,第3版
[12]苗夺谦,王国胤,刘清,林早阳,姚一豫.粒计算“过去、现在与展望”[M].北京:科学出版社,2007
[13]王国胤,张清华,胡军.粒计算研究综述[J].智能系统学报,2007,2(6):6-26
[14]李道国,苗夺谦,张东星,张红云.粒度计算研究综述[J].计算机科学,2005,32(9):1-12
[15]邓蔚,王国胤,吴渝.粒计算综述[J].计算机科学,2004,31(10):178-181
[16]Bargiela A,Pedrycz W.Granular computing:an introduction[M].Kluwer Academic Publishers,Boston,2002.
[17]Doherty P,Grabowski M,Lukaszewicz W,Szalas A.Towards a framework for approximate ontologies[J].Fundamenta Informaticae,2003,57(2):147-165
[18]Stojanovic L,Stojanovic N,Volz R.Migrating data-intensive web sites into the semantic Web[C].In:Proceedings of the 17th ACM Symp.on Applied Computing.New York:ACM Press,2002.1100-1107
[19]Astrova I.Reverse engineering of relational database to ontologies[C].In:Proceedings of the ESWC 2004.Heidelberg:Springer-Verlag,2004.327-341.
[20]Missikoff M.Text mining techniques to automatically enrich a domain ontology[J],Applied Intelligence,2003,18:323-340
[21]Shamsfard M,Barforoush A A.Learning ontologies from natural language texts[J].International Journal Human-Computer Studies,2004,60(1):17-63
[22]Cimiano P,Volker J.Text2Onto—a framework for ontology learning and data-driven change discovery[C].In:NLDB 2005,Alicante,Spain,2005.Berlin,Herdelberg:Springer-Verlag,2005.227-238
[23]Missikoff M,Navigli R and Velardi P.Integrated approach for web ontology learning and engineering[J].IEEE Computer,2002,35(11):60-63
[24]Navigli R,Velardi P,Gangemi A.Ontology learning and its application to automated terminology translation[J].IEEE Intelligent Systems,2003,18(1):22-31
[25]Bisson G,Nedellec C,Canamero L.Designing clustering methods for ontology building—the mo'k workbench[C].In:Proceedings of the ECAI Ontology Learning Workshop,2000.13-19
[26]杜波,田怀风,王立,陆汝占.基于多策略的专业领域术语抽取器的设计[J].计算机工程,2005,31(14):159-160
[27]郑家恒,卢娇丽.关键词抽取方法的研究[J].计算机工程,2005,31(18):194-196
[28]陈文亮,朱靖波,姚天顺.基于Bootstrapping的领域词汇自动获取[C].第7届全国计算语言学联合学术会议论文集(JSCL 2003).北京:清华大学出版社,2003.67-72
[29]Faure D,Nedellec C.A corpus-based conceptual clustering method for verb frames and ontology acquisition[C].In:Proceedings of the LREC,Granada,1998.5-12
[30]Maedche A,Staab S.Discovering conceptual relation from text[C].In:Proceedings of the ECAI 2000.Amsterdam:IOS Press,2000.321-325
[31]Nakaya N,Kurematsu M,Yamaguchi T.A domain ontology development environment using a MRD and text corpus[C].In:Proceedings of the Joint Conference on Knowledge based Software Engineering,2002
[32]Velardi P,Fabriani P,Missikoff M.Using text processing techniques to automatically enrich a domain ontology[C].In:Proceedings of the International Conference on Formal Ontology in Information Systems,Ogunquit,2001.270-284
[33]Cimiano P,Vaolker J.Towards large-scale,unsupervised and ontology-based named entity recognition[R].Technical Report,AIFB,University of Karlsruhe,2004
[34]Charniak E,Berland M.Finding parts in very large corpus[C].In:Proceedings of the 37~(th)Annual Meeting of the Association for Computational Linguistics on Computational Linguistics,Maryland,1999.Morristown:Association for Computational Linguistics,1999.57-64
[35]Kavalec M,Sv(?)tek V.A study on automated relation labelling in ontology learning[EB].http://nb.vse.cz/～svatek/olp05.pdf
[36]Papatheodorou C,Vassiliou A,Simon B.Discovery of ontologies for learning resources using word-based clustering[C].In:Proceedings of the World Conference on Educational Multimedia,Hypermedia and Telecommunications.Chesapeake:AACE,2002.1523-1528.
[37]Yao Y Y.Granular computing for data mining[C].In:Proceedings of SPIE Conference on Data Mining,Intrusion Detection,Information Assurance,and Data Networks Security,2006.1-12
[38]郑征.相容粒度空间模型及其应用研究[Ph.D].中国科学院,北京,2006
[39]蒙祖强.基于分类模型的知识发现机理和方法研究[Ph.D].中南大学,长沙,2004
[40]范明,孟小峰等译.数据挖掘—概念与技术[M].北京:机械工业出版社,2001
[41]Margaret H.Dunham.Data mining—introductory and advanced topics[M].北京:清华大学出版社,2003
[42]张铃,张钹.模糊商空间理论(模糊粒度计算方法)[J].软件学报,2003,14(4):770-776
[43]陈万里.基于商空间理论和粗糙集理论的粒计算模型研究[Ph.D].安徽大学,合肥,2005
[44]Lin T Y,Louie E.Modeling the real world for data mining:granular computing approach[C].In:Proceeding of Joint 9~(th) IFSA World Congress and 20~(th) NAFIPS International Conference,Vancouver,Canada,2001.3044-3049
[45]Lin T Y,Hu X H,Louie E.A fast association rule algorithm based on bitmap and granular computing[C].In:Proceedings of the IEEE International Conference Fuzzy Systems,StLouis,Missouri,2003.678-683
[46]Lin T Y,Yin P.Finding the reduct by granular computing using bit maps:decision rules mining[C].In:Data Mining and Knowledge Discovery:Theory,Tools,and Technology,Orlando,FI:USA,2003.82-93.
[47]Lin T Y,Eric L.Finding association rules by granular computing:fast algorithms for finding association rules[C].In:Data Mining,Rough Sets and Granular Computing,Physica-Verlag,2002.23-42
[48]Wang G Y,Hu F,Huan G H,et al.A granular computing model based on tolerance relation[J].The Journal of China Universities of Post s and Telecommunications,2005,12(3):86-90
[49]胡峰,黄海,王国胤等.不完备信息系统的粒计算方法[J].小型微型计算机系统,2005,26(8):1335-1339
[50]安久江,甘全,王国胤等.一种基于粒计算的规则知识获取算法[J].计算科学,2005,32(8):137-140
[51]邓蔚,王国胤,吴渝.基于近似分割的粒计算规则获取算法[C].中国人工智能学会第11届学术年会论文集,武汉,2005
[52]An J J,Wang G Y,Wu Y,et al.A rule generation algorithm based on granular computing[C].2005 IEEE International Conference on Granular Computing,Beijing,2005.102-107
[53]Yao Y Y,Yao J T.Granular computing as a basis for consistent classification problems[C].In:PAKDD 2002 Workshop:Towards Foundation of Data Mining,Communications of Institute of Information and Computing Machinery,2003.101-106.
[54]Chen Y H,Yao Y Y.Multiview intelligent data analysis based on granular computing[C].In:Proceedings of 2006 IEEE International Conference on Granular Computing,2006.281-286
[55]Yao Y Y.Stratified rough sets and granular computing[C].In:Proceedings of the 18th International Conference of the North American Fuzzy Information Processing Society[C].New York,1999.800-804
[56]Yao Y Y.Information granulation and rough set approximation[J].International Journal of Intelligent Systems,2001,16(1):87-104
[57]Pawlak Z.Rough sets[J].International Journal of Computer and Information Sciences,1982,11:341-356
[58]Pawlak Z.Rough sets,Theoretical Aspects of Reasoning about Data[M].Kluwer Academic Publishers,Dordrecht,1991
[59]王国胤.Rough集理论与知识获取[M].西安:西安交通大学出版社,2001
[60]张文修,吴伟志,梁吉业等.粗糙集理论与方法[M].北京:科学出版社,2001.
[61]史忠植.知识发现[M].北京:科学出版社,2002
[62]张丽娟,李舟军,陈火旺.粒度计算及其在数据挖掘中的应用[J].计算机科学,2005,32(12):178-180
[63]Wang G Y,Zhao J.An J J,et al.A comparative study of algebra viewpoint and information viewpoint in attribute reduction[J].Fundamenta Informaticae,2005,68(3):289-301
[64]王国胤.决策表核属性的计算方法[J].计算机学报,2003,26(5):611-615
[65]刘少辉.知识发现中粗糙集理论的研究[Ph.D].中国科学院研究生院,北京,2003
[66]王庆东.基于粗糙集的数据挖掘方法研究[Ph.D].浙江大学,杭州,2005
[67]Yao Y Y.Concept formation and learning:a cognitive informatics perspective[C].In:Proceedings of the 3rd IEEE International Conference on Cognitive Informatics,2004.42-51
[68]Yao Y Y.Modeling data mining with granular computing[C].In:Proceedings of the 25th Annual International Computer Software and Applications Conference,2001.638-643
[69]Zhao Y,Yao Y Y,Yao,J T.Level construction of decision trees for classification[J].International Journal of Software Engineering and Knowledge Engineering,2006,16(1):103-123
[70]Yao J T,Yao Y Y.Induction of classification rules by granular computing[C].In:Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing (RSCTC 2002),Lecture Notes in Artificial Intelligence 2475,2002.331-338
[71]Zhao Y.Coarsening classification rules on basis of granular computing[C].In:Lecture Notes in Artificial Intelligence 3060,Berlin:Springer,2004.578-579
[72]刘勇.基于粒度计算的知识发现研究及其应用[Ph.D].浙江大学,杭州,2006
[73]耿志强.流程工业粒度数据挖掘技术研究与应用[Ph.D].北京化工大学,北京,2005
[74]卜东波,白硕,李国杰.聚类/分类中的粒度原理[J].计算机学报,2002,25(8):810-816
[75]刘君强.海量数据挖掘技术研究[Ph.D].浙江大学,杭州,2003
[76]Manish M,Rakesh A,Jorma R.SLIQ:a fast scalable classifier for data mining[C].In:Proceedings of the 5~(th) International Conference on Extending Database Technology,Avignon,France,1996.18-32
[77]John C,Sharer S,et al.SPRINT:a scalable parallel classifier for data mining[R].Research Report,IBM Almaden Research Center,1996
[78]Qin Z R,Wang G Y,Wu Y,et al.A scalable rough set knowledge reduction algorithm[C].In:Lecture Notes In Artificial Intelligence 3066.Berlin:Springer,2004.445-454
[79]Doningos P.Knowledge acquisition from examples via multiple models[C].In:Proceedings of the 14th International Conference on Machine Learning,Morgan Kaufman,USA,1997.98-106
[80]Prodromidis A L,Chan P K,Stolfo S J.Meta-learning in distributed data mining system:Issues and approaches[C].In:Advances in distributed and parallel Knowledge Discovery.Menlo Park:Association for the Advancement of Artificial Intelligence Press,2000
[81]Wu X D,Zhang S C.Synthesizing high-frequency rules form different data sources[J].IEEE Transaction on Knowledge and Data Engineering,2003,15(2):353-367
[82]刘宗田.属性最小约简的增量式算法[J].电子学报,1999,27(11):96-98
[83]Hu F,Wang G Y,Huang H,et al.Incremental attribute reduction based on elementary sets[C].In:Lecture Notes in Artificial Intelligence 3641.Berlin:Springer,2005.185-193
[84]Wang G Y.Domain-oriented data-driven data mining based on rough set[J].Journal of Nanchang Institute of Technology,2006,25(2):46
[85]Yao Y Y,Zhong N.Granular computing using information tables[C].In:Data Mining,Rough Sets and Granular Computing,Physica-Verlag,Heidelberg,2002.102-124
[86]Yao Y.Y..Granular Computing[J].Computer Science(in chinese),2004,31:1-5
[87]Yao Y Y.A partition model of granular computing[J].In:LNCS Transactions on Rough Sets,2004.232-253
[88]Yao Y Y.Perspectives of granular computing[C].In:Proceedings of 2005 IEEE International Conference on granular computing,2005.85-90
[89]Yao Y Y.The art of granular computing[C].In:Proceeding of the International Conference on Rough Sets and Emerging Intelligent Systems Paradigms,LNAI 4585,2007.101-112
[90]Yao Y Y.The rise of granular computing[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2008,20(3):299-308
[91]张钹,张铃.问题求解的理论及应用[M].北京:清华大学出版社,1990
[92]刘清,孙辉,王洪发.粒计算研究现状及基于Rough逻辑语义的粒计算研究[J].计算机学报,2008,31(4):543-555
[93]Zadeh L A.Fuzzy sets and information granularity[C].In:Advances in Fuzzy Set Theory and Applications.Amsterdam:North-Holland,1979.3-18
[94]Hobbs J R.Granularity[C].In:Proceedings of the Ninth International Joint Conference on Artificial Intelligence,1985.432-435
[95]Zadeh L A.Fuzzy logic=computing with words[J].IEEE Transactions on Fuzzy Systems,1996,4:103-111
[96]Zadeh L A.Some reflections on soft computing,granular computing and their roles in the conception,design and utilization of information/intelligent systems[C].In:Soft Computing.Berlin:Springer-Verlag,1998.23-25
[97]T.Y.Lin.From rough sets and neighborhood systems to information granulation and computing in words[C].In:Proceedings of European Congress on Intelligent Techniques and Soft Computing,1997.1602-1607
[98]Lin T Y.Granular computing on binary relations Ⅰ:data mining and neighborhood systems[C].In:Rough Sets In Knowledge Discovery,Physica-Verlag,1998.107-121
[99]Lin T Y.Granular computing on binary relations Ⅱ:rough set representations and belief functions[C].In:Rough Sets In Knowledge Discovery,Physica-Verlag,1998.121-140
[100]Lin T Y.Granulation and nearest neighborhood:rough set approach[C].In:Granular Computing:an Emerging Paradigm,Physica-Verlag,Heidelberg,2001.125-142
[101]Lin T Y.Rough sets,fuzzy sets,data mining and granular computing[C].In:Proceedings of the 9~(th) International Conference(RSFDGrC 2003),LNCS 2639,2003.16-24
[102]Lin T Y.Granular computing:examples,intuition,and modeling[C].In:Proceedings of International Conference on Granular Computing,Beijing:China,2005.40-44
[103]Lin T Y.Granular computing Ⅱ:infrastructures for AI-engineering[C].In:Proceedings of International Conference on Granular Computing,Atlanta:USA,2006.2-7
[104]Skowron A,Stepaniuk J.Information granules and rough neurcomputing[C].In:Rough Neurocomputing:Techniques for Computing with Words,Cognitive Technologies.Berlin:Springer-Verlag,2003.43-84
[105]Skowron A.Toward intelligent systems:calculi of information granules[J].Bulletin of International Rough Set Society,2001,5(122):9-30
[106]Skowron A,Stepaniuk J,Peters,James F.Extracting patterns using information granules[J].Bulletin of International Rough Set Society,2001,5(122):135-142
[107]Polkwski L.Towards rough set foundations,metrological approach[C].In:Lecture in Artificial Intelligence 3066.Berlin:Springer-Verlag,2004.8-25
[108]Polkwski L.A calculus on granules from rough inclusions in information systems[J].Journal of Nanchang Institute of Technology,2006,25(2):22-27
[109]Liu Q.Granules and reasoning based on granular computing[C].In:Lecture Notes in Artificial Intelligence 2718.Berlin:Springer-Verlag,2003.516-526
[110]Liu Q,Jiang F,Deng D Y.Design and implement for the diagnosis software of bood viscosity syndrome based on morphology on GrC[C].In:Lecture Notes in AI 2639.Berlin:Springer-Verlag,2003.413-420
[111]刘清.粒及其分解和合并的研究[C].中国人工智能进展 中国人工智能学会第10届全国学术年会,2003.1411-1416
[112]刘清,孙辉.从Rough集的发展前景看粒计算的研究趋势[J].南昌工程学院学报,2006,25(5):1-10
[113]刘清,刘群.粒及粒计算在逻辑推理中的应用[J].计算机研究与发展,2004,41(4):546-551
[114]Liu Q.,Wang Q Y.Granular logic with closeness relation "λ" and its reasoning[C].In:Proceedings of Rough Sets,Fuzzy Sets,Data Mining and Granular Computing.Springer,Berlin,2005.709-717.
[115]刘消.G-逻辑及其归结推理[J].计算机学报,2004,27(7):865-874
[116]张维明,邓苏,罗雪山等.信息系统建模技术与应用[M].北京:电子工业出版社,1997
[117]Zadeh L A.Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J].Fuzzy Sets and Systems,1997,19:111-127
[118]Zhang L,Zhang B.The quotient space theory of problem solving[C].In:Lecture Notes in Artificial Intelligence 2639,Springer-Verlag,Berlin,2003.11-15
[119]Zhu W,Wang F Y.Reduction and axiomization of coveting generalized rough sets[J]Information Sciences,2003,152:217-230
[120]祝峰,何华灿.粗集的公理化[J].计算机学报,2000,23(3):330-333
[121]仇国芳,陈劲.概念知识系统与概念信息粒格[J].工程数学学报,2005,22(6):963-969
[122]仇国芳,荆彦玲.粗糙集与概念格[M].西安:西安交通大学出版社,2006
[123]Kryszkiewicz M.Rough set approach to incomplete information systems[J].Information Sciences,1998,112:39-49
[124]Wolterink,T.What is an Ontology?[EB].http://referaat.cs.utwente.nl/new/papers.php?Confid=1
[125]Ziarko W.Variable precision rough sets model[J].Journal of Computer and System Sciences,1993,46(1):39-59
[126]Dubois D,Prade H.Rough fuzzy sets and fuzzy rough sets[J].International Journal of General Systems,1990,17:191-208
[127]Slezak D,Ziarko W.Attribute reduction in the bayesian version of variable precision rough set model[J].Electronic Notes in Theoretical Computer Science,2003,82(4):1-11
[128]王国胤.Rough集理论在不完备信息系统中的扩充[J].计算机研究与发展,2002,39(10):1238-1243
[129]陈炯,曹付元.Rough集理论在多值信息系统中的扩充[J].山西大学学报,2006,26(12):158-160
[130]刘斓,刘清.基于粒的二进制运算的关联规则提取方法[J].南昌大学学报(理科版),2003,27(1):98-101
[131]苗夺谦,王珏.粗糙集理论中知识粗糙性与信息熵关系的讨论[J].模式识别与人工智能,1998,11(3):34-240.
[132]Pons-Porrata A,Ruiz-Shulcloper J,Martinez-Trinidad J F.RGC:a new conceptual clustering algorithm for mixed incomplete data sets[J].Mathematical and Computer Modelling,2002,36:1375-1385
[133]汪培庄.因素空间与概念描述[J].软件学报,1992,1:30.-0
[134]陈世权,程里春.模糊概念格[J].模糊系统与数学,2002,16(4):12-18
[135]强宁,刘宗田,林炜.模糊概念格的一种构造算法[J].电子学报,2005,33(2):350-353
[136]强宁.模糊概念格模型及其应用研究[Ph.D].上海大学,上海,2005
[137]胡可云,陆玉昌,石纯一.概念格及其应用进展,清华大学学报(自然科学版)[J].2000,40(9):77-81
[138]胡可云,陆玉昌,石纯一.基于概念格的分类和关联规则的集成挖掘方法[J].软件学报,2000,11(11):1478-1484
[139]刘宗田.容差近似空间的广义概念格模型研究[J].计算机学报,2000,23(1):66-70
[140]Ai-Omary A Y,Jamil M.S.A new approach of clustering based machine-learning algorithm[J].Knowledge-Based System,2006,19:248-258
[141]程勇.基于本体的不确定性知识管理的研究[Ph.D].中国科学院,北京,2005
[142]张建辉.K-means聚类算法研究及应用[Ms.D].武汉理工大学,武汉,2007
[143]UCI Machine Learning Data Repository,Http://www.ics.uci.edu/mleaen/MLReoditory.html.
[144]Agrawal R,Srikant R.Fast algorithm for mining association rules[C].In:Proceedings of the 20th International Conference on Very Large Databases.Burlington:Morgan Kaufmann Publishers,1994.487-499.
[145]Han J,Pei J,Yin Y.Mining frequent patterns without candidate generation[C].In:Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data.New York:ACM Press,2000.1-12
[146]Zaki M J,Hsiao C J.CHARM:an efficient algorithm for closed association rule mining[R].Technical Report,TR99-10,Department of Computer Science,Rensselaer Polytechnic Institute,1999
[147]Pan F,Cong G,Tung A K H,et al.CARPENTER:Finding closed patterns in long biological datasets[C].In:Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM Press,2003.637-642
[148]Agrawal R,Shafer J C.Parallel mining of association rules[J].IEEE Transactions on knowledge and date engineering,1996,8(6):962-969
[149]Cheung D W,Lee S D,Kao B.A general incremental technique for maintaining discovered association rules[C].In:Proceedings of the 5th International Conference on Database Systems for Advanced Applications.Melbourne,Australia,1997.185-194
[150]李闯,杨胜.基于粗糙集理论的ORD关联规则挖掘算法[J].计算机工程与设计,2008,29(14):3666-3668
[151]王丽丽.不完备系统中混合数据关联规则挖掘的研究[Ms.D].长沙理工大学,长沙,2008
[152]何军,刘红岩,杜小勇.挖掘多关系关联规则[J].软件学报,2007,18(11):2752-2765
[153]冯玉才,冯剑琳.关联规则的增量式更新算法[J].软件学报,1998,9(4):301-306
[154]高峰,谢剑英.发现关联规则的增量式更新算法[J].计算机工程,2000,26(12):49-50
[155]Lee S D,Cheung D W.Maintenance of discovered association rules:when to update[C].In:Proceedings of SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery(DMKD-97),Tucson,Arizona,U.S.A,1997
[156]Stepaniuk J,Skowron A.Ontological framework for approximation[C].In:RSFDGrC 2005,LNAI 3641,2005.718-727
[157]Slowinski R,Vanderpooten D.A generalized definition of rough approximations based on similarity[J].IEEE Transaction.Knowledge.Data Engineering.2000,12(2):331-336
[158]王俊红,梁吉业.概念格与粗糙集[J]山西大学学报(自然科学版),2003,26(4):307-310
[159]周文,刘宗田,陈慧琼.FCA与本体结合研究的综述[J].计算机科学,2006,33(2):8-12
[160]黄美丽,刘宗田.基于形式概念分析的领域本体构建方法研究[J].计算机科学,2006,33(1):210-212,239