Induction of logic programs: FOIL and related systems

FOIL is a first-order learning system that uses information in a collection of relations to construct theories expressed in a dialect of Prolog. This paper provides an overview of the principal ideas and methods used in the current version of the system, including two recent additions. We present examples of tasks tackled by FOIL and of systems that adapt and extend its approach.     

Web知识规则提取的FOIL算法改进

将一阶学习的FOIL算法应用到Web知识规则的提取是当前学习Web知识所普遍采用的方法．本文在FOIL算法的基础上进行了改进，提出了基于网页间联系的新的路径学习算法，使得原算法的稳定性和精确度都明显提高。     

一个不受常量序限制的归纳逻辑程序设计算法

文章分析了FOIL（first－orderinductivelearner）递归谓词学习算法理论上的不足以及由此导致的应用范围的局限，并通过两个例子给予详细说明．为了克服这一缺陷，文章引入了反映递归规则集R与实例空间E本质关系的实例图H（R．E）和实例序的概念，奠定了算法的理论基础．在此基础上，给出了基于实例图的FOILPlus算法．算法通过对悬例、悬弧的操作把握住实例序，自然而然地防止了病态递归规则的产生，从而保证了FOILPlus可以不受常量序限制地完成学习任务；同时，算法的时空复杂度较之FOIL算法没有增加．FOILPlus算法已经编程实现，并用它尝试了两个FOIL学习失败的递归任务，都获得了成功．     

一个不受常量序限制的ILP学习算法

文章分析了FOIL(first-order inductive)递归谓词学习算法理论上的不足以及由此导致的应用范围的局限,并通过两个例子给予详细说明.为了克服这一缺陷,文章引入了反映递归规则集R与实例空间E本质关系的实例图H_(R,E)和实例序的概念,奠定了算法的理论基础.在此基础上,给出了基于实例图的FOILPlus算法.算法通过对悬例、悬弧的操作把握住实例序,自然而然的防止了病态递归规则的产生,从而保证FOILPlus可以不受常量序限制地完成学习任务;同时,算法的时空复杂度较之FOIL算法没有增加.FOILPlus算法已经编程实现,并用它尝试了两个FOIL学习失败的递归任务,都获得了成功.     

动态绑定实现自动服务编排

在面对服务架构中,业务逻辑由各种服务组件组合完成,不同服务组件完成不同业务流程的业务逻辑。面对不断变化和发展的商业环境,服务的组合性需要具有灵活性和可靠性。虽然目前已有多种方法能够优化和改善面对服务系统,但是针对服务编排自动化的应用和研究较少,这主要归因于复杂的业务逻辑和繁琐的服务编排工具。介绍一种采用一阶内涵逻辑语言(FOIL)为业务流程建立其业务逻辑的FOIL公式,通过计算FOIL公式,自动生成WS-BPEL结构以动态绑定已有的服务组件,最终完成自动服务编排。Tarski的真值理论证明这种方法具有实际应用价值。     

一种自顶向下和自底向上相结合的ILP算法

归纳逻辑程序设计(ILP)是机器学习的一个重要分支,给定一个样例集和相关背景知识,ILP研究如何构建与其相一致的逻辑程序,这些逻辑程序由有限一阶子句组成。文章描述了一种综合当前一些ILP方法多方面优势的算法ICCR,ICCR溶合了以FOIL为代表的自顶向下搜索策略和以GOLEM为代表的自底向上搜索策略,并能根据需要发明新谓词、学习递归逻辑程序,对比实验表明,对相同的样例及背景知识,ICCR比FOIL和GOLEM能学到精度更高的目标逻辑程序。     

An experimental evaluation of simplicity in rule learning

While recent research on rule learning has focused largely on finding highly accurate hypotheses, we evaluate the degree to which these hypotheses are also simple, that is small. To realize this, we compare well-known rule learners, such as CN2, RIPPER, PART, FOIL and C5.0 rules, with the benchmark system SL² that explicitly aims at computing small rule sets with few literals. The results show that it is possible to obtain a similar level of accuracy as state-of-the-art rule learners using much smaller rule sets.     

An effective hybrid learning system for telecommunication churn prediction

Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Moreover, high predictive accuracy and good interpretability of the results are two key measures of a classification model. More studies have shown that single model-based classification methods may not be good enough to achieve a satisfactory result. To obtain more accurate predictive results, we present a novel hybrid model-based learning system, which integrates the supervised and unsupervised techniques for predicting customer behaviour. The system combines a modified k-means clustering algorithm and a classic rule inductive technique (FOIL).Three sets of experiments were carried out on telecom datasets. One set of the experiments is for verifying that the weighted k-means clustering can lead to a better data partitioning results; the second set of experiments is for evaluating the classification results, and comparing it to other well-known modelling techniques; the last set of experiment compares the proposed hybrid-model system with several other recently proposed hybrid classification approaches. We also performed a comparative study on a set of benchmarks obtained from the UCI repository. All the results show that the hybrid model-based learning system is very promising and outperform the existing models.     

Improving Expressivity of Inductive Logic Programming by Learning Different Kinds of Fuzzy Rules

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.     

语义网格中基于多策略学习的Ontology匹配方法

近些年来，语义Web和网格计算这两个方向在各自的研究社区分别发展着，这两方面的交叉即语义网格(semantic grid)则是最近一段时间兴起的研究领域．通过给网格附加语义层，能够促进网格自组织的形成．现有的Gnd社区都是使用集中式的、一致性的、可扩充的Ontology库．超越集中式的语义存储是语义网格发展面临的最大挑战之一．针对网格社区间的Ontology异构性这个问题，提出了一种多策略的Ontology匹配学习方法．它使用多种分类方法来学习Ontology之间的匹配：使用一般的基于统计的分类方法来发现数据实例内部的分类特征；或者使用基于一阶逻辑的学习算法FOIL来发现数据实例之间的语义联系．在单个方法预测的基础上，匹配系统使用称之为最突出的冠军的匹配委员会方法来集成分类结果．实验表明在现实的知识领域中，系统能达到很高的匹配精度．