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复杂结构归纳学习的需求近年来快速增长。复杂结构归纳学习方法按照知识表示方式不同分为基于逻辑的方法与基于数学图的方法。阐述了复杂结构归纳学习研究的历史沿革,介绍、分析和对比了不同知识表示方式下的学习方法,给出了复杂结构归纳学习将来发展面临的挑战和需重点解决的问题。 相似文献
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伴随语义网的发展,语义网本体数量激增.然而万维网上绝大多数的数据仍存储在关系数据库中.建立关系数据库模式与语义网本体间的映射是一种实现两者之间互操作性的有效途径.因此,提出了一种基于语义的关系数据库模式与OWL本体间的映射方法SMap,包含简单映射发现和复杂映射学习两个阶段.在简单映射发现阶段,首先通过逆向工程规则将关系数据库模式和本体中的元素对应地分为不同类别,再为每个元素构建虚拟文档并计算它们之间的相似度,其中针对不同类别的元素设计了不同的虚拟文档抽取方案.在复杂映射学习阶段,基于已发现的简单映射以及重叠的数据库记录和本体实例,自动化地生成训练事实数据,然后运用归纳逻辑编程算法学习出多种类型的基于Horn规则的复杂映射.真实数据集上的实验结果表明,SMap在简单映射发现和复杂映射学习上均明显优于现有的关系数据库模式与本体间映射方法. 相似文献
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半监督聚类近年来成为了机器学习和数据挖掘领域的研究热点.目前存在的半监督聚类方法都采用属性-值的知识表示方式.但属性-值语言在表示复杂结构数据时存在很多弊端,而基于高阶逻辑的知识表示语言Escher能较好地表示复杂结构数据.在Fscher的知识表示方式下,首先当先验知识是实例之间的约束信息时,提出了搜索K-Means算法的K个初始质心的方法;其次,时先验知识不完全、能够发现的初始质心的个数,r小于K的情况,提出了搜索其余的K-r个初始质心的算法MSS-KMeans和SMSS-KMeans;最后在复杂结构数据集上,验证了所提算法的可行性.最终的实验结果表明,基于高阶逻辑知识表示方式的丰监督聚类方法要优于基于属性-值语言的半监督聚类方法. 相似文献
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基于动态描述逻辑DDL的动作理论 总被引:1,自引:1,他引:0
基于一阶谓词逻辑或高阶逻辑的动作理论与采用命题语言的动作理论之间存在一个关于描述和推理能力的鸿沟;作为描述逻辑的动态扩展,动态描述逻辑DDL为基于描述逻辑的动作刻画和推理提供了一种途径.系统地研究了基于DDL的动作表示和推理问题.首先,在应用描述逻辑对静态领域知识进行刻画的基础上,引入带参数的原子动作定义式和带参数的复... 相似文献
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复杂系统需求描述语言(MEISRDL)是一种基于业务特征的信息系统需求描述语言。由于该语言是一种半形式化语言,无法进行基于精确语义的模型检验,模型中容易存在语义上的矛盾或冲突。为了解决该问题,文章提出一种MEISRDL静态图模型的一致性检查方法。该方法采用描述逻辑SHOIN(D)描述MEISRDL静态图图元,实现半形式化的MEISRDL模型的形式化转换,通过模型映射算法可以有效推理判断模型语义矛盾。实例证明:该方法解决了MEISRDL静态图模型无法进行精确语义模型检验的问题,为复杂系统需求模型的语义一致性检查工作,提供了可靠的技术支持。 相似文献
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描述逻辑是语义Web的逻辑基础,是形式化表达领域知识的工具.但是描述逻辑只能表达单调推理,不能处理不完全知识.认知描述逻辑因其非单调特性和良好的时间复杂度等特点在处理不完全知识方面有较好的优势.本文在认知描述逻辑ALCK的基础上提出了新的认知描述逻辑语言ALCKR ,保留了描述逻辑原有的优点,加入传递角色属性,增强了表达能力,并通过认知查询拥有了非单调推理的能力.设计了ALCKR 的语法、语义以及表算法. 相似文献
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本体是共享概念模型的形式化规范说明,是一种能在语义和知识层次上描述信息系统概念模型的建模工具,为解决多域环境中的安全互操作提供了一种新的方法.使用本体及其描述语言,对基于角色的访问控制策略进行了描述,形成一个概念和属性的公理集合(TBox),并采用ALCN(含有个数限制和补算子的描述逻辑语言)对TBox进行形式化的描述.利用域间角色映射方法来解决多域访问控制策略的集成.使用基于规则的推理技术,定义多域访问控制中的一系列推理规则,实现访问控制领域的推理.基于前述方法实现了OntoAC系统,实验结果表明该方法是有效的. 相似文献
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在统一建模语言(UML)规范中顺序图的语义是以自然语言的形式描述的,是一种半形式化的语言,不能对系统的交互行为进行形式化分析及论证.针对UML顺序图缺乏精确的形式化描述问题,根据顺序图的时序特征,提出了增加交互操作符的UML顺序图的六元组形式化方法.对描述逻辑进行时序扩展,得到可表示动态和时序语义的形式化规范——时序描述逻辑.应用时序描述逻辑的时态算子得到时序描述逻辑语义形式的UML顺序图.用UML顺序图描述完整的C语言执行过程,将其形式化描述,实验结果表明,这种方法是可行的. 相似文献
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This paper introduces a novel logical framework for concept-learning called brave induction. Brave induction uses brave inference for induction and is useful for learning from incomplete information. Brave induction
is weaker than explanatory induction which is normally used in inductive logic programming, and is stronger than learning from satisfiability, a general setting of concept-learning in clausal logic. We first investigate formal properties of brave induction, then
develop an algorithm for computing hypotheses in full clausal theories. Next we extend the framework to induction in nonmonotonic logic programs. We analyze computational complexity of decision problems for induction on propositional theories. Further, we provide examples
of problem solving by brave induction in systems biology, requirement engineering, and multiagent negotiation. 相似文献
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Mathieu Serrurier Henri Prade 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2007,11(5):459-466
Introducing fuzzy predicates in inductive logic programming may serve two different purposes: allowing for more adaptability
when learning classical rules or getting more expressivity by learning fuzzy rules. This latter concern is the topic of this
paper. Indeed, introducing fuzzy predicates in the antecedent and in the consequent of rules may convey different non-classical
meanings. The paper focuses on the learning of gradual and certainty rules, which have an increased expressive power and have
no simple crisp counterpart. The benefit and the application domain of each kind of rules are discussed. Appropriate confidence
degrees for each type of rules are introduced. These confidence degrees play a major role in the adaptation of the classical
FOIL inductive logic programming algorithm to the induction of fuzzy rules for guiding the learning process. The method is
illustrated on a benchmark example and a case-study database. 相似文献
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采用遗传算法(GA)作为归纳逻辑程序设计(ILP)的搜索策略,可以提高ILP方法的鲁棒性和适应性,文章简要叙述了对作者提出的遗传归纳逻辑程序设计(GILP)算法作的改进,测试了选择策略对GILP算法收敛性能的影响,采用不同的选择策略不会影响算法的最终收敛结果,但会产生不同的选择压力,导致算法具有不同的收敛速率。 相似文献
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Costin Bădică Amelia Bădică Elvira Popescu Ajith Abraham 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2007,11(8):753-772
In this paper, we propose a novel class of wrappers (logic wrappers) inspired by the logic prog- ramming paradigm. The developed Logic wrappers (L-wrapper) have declarative semantics, and therefore:
(i) their specification is decoupled from their implementation and (ii) they can be generated using inductive logic programming.
We also define a convenient way for mapping L-wrappers to XSLT for efficient processing using available XSLT processing engines. 相似文献
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Separate-and-Conquer Rule Learning 总被引:9,自引:0,他引:9
Johannes Fürnkranz 《Artificial Intelligence Review》1999,13(1):3-54
This paper is a survey of inductive rule learning algorithms that use a separate-and-conquer strategy. This strategy can be traced back to the AQ learning system and still enjoys popularity as can be seen from its frequent use in inductive logic programming systems. We will put this wide variety of algorithms into a single framework and analyze them along three different dimensions, namely their search, language and overfitting avoidance biases. 相似文献
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基于参照的对词结构操作语义的归纳学习 总被引:1,自引:0,他引:1
心理语言学的研究和认知发展过程证明在语言获得的早期经历了一个自主的归纳学习过程,本文的出发点是语言发展的规律,并将词结构形式语义的获得过程和表示基础放在一个具有统一的语言理解和语言产生机制的语言信息加工模型中来考虑。本文讨论了一个基于实例的机器学习系统,为了获得词结构的形式语义,采用了操作语义的定义,并设计了一个基于参照的发现学习算法,其目的是使语义能伴随例句样本的丰富而精密化。 相似文献
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The least general generalization (LGG) of strings may cause an over-generalization in the generalization process of the clauses
of predicates with string arguments. We propose a specific generalization (SG) for strings to reduce over-generalization.
SGs of strings are used in the generalization of a set of strings representing the arguments of a set of positive examples
of a predicate with string arguments. In order to create a SG of two strings, first, a unique match sequence between these
strings is found. A unique match sequence of two strings consists of similarities and differences to represent similar parts
and differing parts between those strings. The differences in the unique match sequence are replaced to create a SG of those
strings. In the generalization process, a coverage algorithm based on SGs of strings or learning heuristics based on match
sequences are used.
Ilyas Cicekli received a Ph.D. in computer science from Syracuse University in 1991. He is currently a professor of the Department of Computer
Engineering at Bilkent University. From 2001 till 2003, he was a visiting faculty at University of Central Florida. His current
research interests include example-based machine translation, machine learning, natural language processing, and inductive
logic programming.
Nihan Kesim Cicekli is an Associate Professor of the Department of Computer Engineering at the Middle East Technical University (METU). She graduated
in computer engineering at the Middle East Technical University in 1986. She received the MS degree in computer engineering
at Bilkent University in 1988; and the PhD degree in computer science at Imperial College in 1993. She was a visiting faculty
at University of Central Florida from 2001 till 2003. Her current research interests include multimedia databases, semantic
web, web services, data mining, and machine learning. 相似文献