开源软件中结构复杂度的度量方法

针对大型开源软件的复杂性,提出一种基于随机图和结构熵的开源软件结构复杂度的度量方法。将开源软件中的软件包抽象成点,将软件包之间的依赖关系抽象成有向边,建立随机图,并引入结构熵的概念。结合随机图的特性和结构熵度量开源软件的耦合度和内聚度。利用该方法进行实例分析,结果表明,随着开源软件按版本发展,软件耦合度和内聚度不断增长。     

面向对象软件内聚度度量数据分布的实证研究

度量数据的分布信息对于理解和使用面向对象软件度量有重要意义.人们对面向对象软件规模度量、耦合度度量乃至继承维度的度量数据的分布都有研究,但对除内聚度缺乏度LCOM之外的内聚度度量数据的分布却缺乏研究.已有的实证研究表明,LCOM并不是好的内聚度度量,因此探讨其他内聚度度量数据分布很有必要.对包括内聚度缺乏度、基于连通性的内聚度度量和基于相似性的内聚度度量总共17个度量指标在112个Java开源软件项目的分布情况进行实证研究,对每个度量指标的每个项目数据使用幂律分布和对数正态分布进行拟合,并使用荟萃分析方法对拟合结果进行了分析.实证研究结果表明,非规范化的内聚度量可使用对数正态分布和幂律分布拟合,但规范化的基于相似性的内聚度量（包括CC、LSCC、SCOM和SCC）需要排除方法数小于等于1或字段数为0的特殊类才能使用对数正态分布拟合,而基于连通性的内聚度度量（包括TCC、LCC、DCD和DCI）则只有对应的非规范化版本的数据才符合对数正态分布或幂律分布.实证研究可帮助人们更好地理解和使用内聚度度量,特别是可以帮助人们如何利用已有的方法确定内聚度度量的阈值.     

QoS多层本体的双向度量模型

针对传统质量评估模式中指标权重赋值依据单一的问题,首先将服务描述本体分为共享本体和专属本体两个抽象层次,构建具有“抽象-应用-度量”多层结构的QoS本体,用于QoS度量的对象描述和数据采集;然后建立基于深度信任网络和回归模型的双向度量模型DM-QSM,将服务描述信息和类似服务历史数据作为训练样本数据集对DM-QSM进行正向训练,再结合用户反馈对DM-QSM进行逆向调优,以实现QoS度量指标权重及其偏好度的自适应调节。最后选用可编程建模环境NetLogo为实验平台、公共服务数据集QWS为训练样本集、电子商务应用服务为测试样本集,验证了DM-QSM的可行性和有效性。     

改进的大规模本体分块与映射方法

针对当前大规模本体映射分块结果不精确的问题,提出一种改进的大规模本体分块与映射方法。对本体进行预处理,把本体表示为有向无环图,将本体分块问题转换为图分割问题。采用基于遗传算法的GPO算法对有向无环图进行分块,利用基于本体块结构和参考点相结合的策略找到正确的块映射。实验结果表明,改进映射方法的分块与映射质量较优。     

大规模本体分块与映射研究

针对当前在大规模本体映射方面存在的不足,提出一种新的基于遗传算法的大规模本体分块与映射方法.首先,对本体进行预处理,把本体表示成有向无环图,将本体分块问题转换成图分割问题.然后采用基于遗传算法的GPO( Genetic-Partition-Ontology)算法对有向无环图进行分块,该算法不需要输入分块个数;最后利用基于本体块结构和基于参考点相结合方法找到正确的块映射.理论分析和实验结果表明,本文提出的映射方法能取得很好的结果.     

基于客户程序度量包内聚性

为了一致而高效地计算包内聚性,许多研究者提出了大量的包内聚性度量方法.然而,这些方法主要依赖于包内部的数据流关系,常导致度量结果与实际开发经验相悖.为了解决这一问题,首先以包的职责为基础将包划分为4类.然后,提出了共同重用内聚CRC,并根据包的分类框架讨论了CRC的适用性.CRC的核心思想是若多个类总被共同重用,则它们之间存在紧密耦合.最后,提出了度量CRC的海明内聚度HC.与现有方法相比,HC同时考虑了包内和包间的数据依赖.因而,该方法能够有效地反映包内部类间的语义关系.此外,HC利用包的使用模式提高了度量结果的可区分性.实验研究表明HC能够有效评估包的内聚程度.充分说明了作为HC基础的CRC具有较高的合理性.     

基于ε-Connections的模块化本体建模研究

模块化本体将模块化的思想引入本体研究领域,将本体组织成多个本体模块的集成形式,这样不仅方便了本体的构建,也有利于本体的维护、共享和重用。在提出了一个模块化本体的三层体系结构的基础上,将模块化本体建模过程划分为本体模块划分、本体模块构建、本体模块集成和本体模块一致性检验四个步骤。在分别阐述各个步骤的主要工作之后,使用ε-Connections构建了一个减速器本体的模块化实例,验证了上述模块化本体建模步骤的可行性。     

基于本体的教学领域知识库构建及应用

教学领域知识库的构建有利于知识的检索和共享。将本体引入教学领域知识库构建过程中,通过对计算机图形学课程功能及组织结构进行设计和分析,构建教学知识库本体,提供语义空间,这样不仅有利于知识概念的共享和重用,也有利于实现基于本体的知识检索。研究基于本体的教学知识库系统,具有很强的理论和实际意义。     

状态依赖的类内聚度量

类是面向对象软件中的模块,包含属性和方法。类内聚度量是对类属性和方法相关程度的度量。通过对类方法和属性的分类及其与类状态之间的关系分析,提出了类核的概念和状态依赖的类内聚度量方法。该方法有利于克服类内聚度量复杂性和判断模糊性问题,并可以在设计阶段对类的内聚进行度量。     

本体评估方法研究综述

本体是知识共享的重要工具,也是语义Web的支柱.本体的使用是以保证其质量为前提.本体评估为本体构建的质量及本体的选择重用提供了技术支持.分析了本体评估的必要性;对本体评估的现状进行综述,主要包括归纳总结了本体评估的方法及本体评估的基本思路,介绍了本体评估的指标体系,比较分析了本体评估工具;最后提出本体评估存在的问题和进一步研究方向.     

Measuring design complexity of semantic web ontologies

Ontology languages such as OWL are being widely used as the Semantic Web movement gains momentum. With the proliferation of the Semantic Web, more and more large-scale ontologies are being developed in real-world applications to represent and integrate knowledge and data. There is an increasing need for measuring the complexity of these ontologies in order for people to better understand, maintain, reuse and integrate them. In this paper, inspired by the concept of software metrics, we propose a suite of ontology metrics, at both the ontology-level and class-level, to measure the design complexity of ontologies. The proposed metrics are analytically evaluated against Weyuker’s criteria. We have also performed empirical analysis on public domain ontologies to show the characteristics and usefulness of the metrics. We point out possible applications of the proposed metrics to ontology quality control. We believe that the proposed metric suite is useful for managing ontology development projects.     

A semantic metrics suite for evaluating modular ontologies

Ontologies, which are formal representations of knowledge within a domain, can be used for designing and sharing conceptual models of enterprises information for the purpose of enhancing understanding, communication and interoperability. For representing a body of knowledge, different ontologies may be designed. Recently, designing ontologies in a modular manner has emerged for achieving better reasoning performance, more efficient ontology management and change handling. One of the important challenges in the employment of ontologies and modular ontologies in modeling information within enterprises is the evaluation of the suitability of an ontology for a domain and the performance of inference operations over it. In this paper, we present a set of semantic metrics for evaluating ontologies and modular ontologies. These metrics measure cohesion and coupling of ontologies, which are two important notions in the process of assessing ontologies for enterprise modeling. The proposed metrics are based on semantic-based definitions of relativeness, and dependencies between local symbols, and also between local and external symbols of ontologies. Based on these semantic definitions, not only the explicitly asserted knowledge in ontologies but also the implied knowledge, which is derived through inference, is considered for the sake of ontology assessment. We present several empirical case studies for investigating the correlation between the proposed metrics and reasoning performance, which is an important issue in applicability of employing ontologies in real-world information systems.     

Empirical findings on ontology metrics

Ontologies are becoming the preferred way of representing, dealing and reasoning with large volumes of information in several domains. In consequence, the creation, evaluation and maintenance of ontologies has become an engineering process that needs to be managed and measured using sound and reliable methods. As part of any ontology engineering or revision process, metrics can play a role helping in identifying possible problems or incorrect use of ontology elements, along with providing a kind of quality assessment that complements reviews requiring expert inspection. However, in spite of the fact that there are a number of ontology metric proposals described in the literature, there is a lack of empirical studies that provide a basis for their interpretation. This paper reports a systematic exploration of existing ontology metrics from a large set of ontologies extracted from the Swoogle search engine. The OntoRank value used inside the Swoogle to rank search results is used as a contrast for some existing metrics. The results show that the existing proposed metrics evaluated in general do not seem to be indicators of that ranking, but they can be helpful to identify particular kinds of ontologies.     

Evaluation of the OQuaRE framework for ontology quality

The increasing importance of ontologies has resulted in the development of a large number of ontologies in both coordinated and non-coordinated efforts. The number and complexity of such ontologies make hard to ontology and tool developers to select which ontologies to use and reuse. So far, there are no mechanism for making such decisions in an informed manner. Consequently, methods for evaluating ontology quality are required. OQuaRE is a method for ontology quality evaluation which adapts the SQuaRE standard for software product quality to ontologies. OQuaRE has been applied to identify the strengths and weaknesses of different ontologies but, so far, this framework has not been evaluated itself. Therefore, in this paper we present the evaluation of OQuaRE, performed by an international panel of experts in ontology engineering. The results include the positive and negative aspects of the current version of OQuaRE, the completeness and utility of the quality metrics included in OQuaRE and the comparison between the results of the manual evaluations done by the experts and the ones obtained by a software implementation of OQuaRE.     

Semantic oriented ontology cohesion metrics for ontology-based systems

Ontologies play a core role to provide shared knowledge models to semantic-driven applications targeted by Semantic Web. Ontology metrics become an important area because they can help ontology engineers to assess ontology and better control project management and development of ontology based systems, and therefore reduce the risk of project failures. In this paper, we propose a set of ontology cohesion metrics which focuses on measuring (possibly inconsistent) ontologies in the context of dynamic and changing Web. They are: Number of Ontology Partitions (NOP), Number of Minimally Inconsistent Subsets (NMIS) and Average Value of Axiom Inconsistencies (AVAI). These ontology metrics are used to measure ontological semantics rather than ontological structure. They are theoretically validated for ensuring their theoretical soundness, and further empirically validated by a standard test set of debugging ontologies. The related algorithms to compute these ontology metrics also are discussed. These metrics proposed in this paper can be used as a very useful complementarity of existing ontology cohesion metrics.     

Ontologies: How can They be Built?

Ontologies are an important component in many areas, such as knowledge management and organization, electronic commerce and information retrieval and extraction. Several methodologies for ontology building have been proposed. In this article, we provide an overview of ontology building. We start by characterizing the ontology building process and its life cycle. We present the most representative methodologies for building ontologies from scratch, and the proposed techniques, guidelines and methods to help in the construction task. We analyze and compare these methodologies. We describe current research issues in ontology reuse. Finally, we discuss the current trends in ontology building and its future challenges, namely, the new issues for building ontologies for the Semantic Web.     

How ontologies are made: Studying the hidden social dynamics behind collaborative ontology engineering projects

Traditionally, evaluation methods in the field of semantic technologies have focused on the end result of ontology engineering efforts, mainly, on evaluating ontologies and their corresponding qualities and characteristics. This focus has led to the development of a whole arsenal of ontology-evaluation techniques that investigate the quality of ontologies as a product. In this paper, we aim to shed light on the process of ontology engineering construction by introducing and applying a set of measures to analyze hidden social dynamics. We argue that especially for ontologies which are constructed collaboratively, understanding the social processes that have led to their construction is critical not only in understanding but consequently also in evaluating the ontologies. With the work presented in this paper, we aim to expose the texture of collaborative ontology engineering processes that is otherwise left invisible. Using historical change-log data, we unveil qualitative differences and commonalities between different collaborative ontology engineering projects. Explaining and understanding these differences will help us to better comprehend the role and importance of social factors in collaborative ontology engineering projects. We hope that our analysis will spur a new line of evaluation techniques that view ontologies not as the static result of deliberations among domain experts, but as a dynamic, collaborative and iterative process that needs to be understood, evaluated and managed in itself. We believe that advances in this direction would help our community to expand the existing arsenal of ontology evaluation techniques towards more holistic approaches.     

Cohesion and coupling metrics for ontology modules

In recent years, an increasing number of ontologies and semantic web applications have been developed and used. A conscious effort has been made to develop methods to modularize ontologies. These methods contribute to building a new ontology. However, few studies have focused on the evaluative methods for ontology modules. In this study, we propose novel metrics to measure ontology modularity. To evaluate the ontology modules, we introduce cohesion and coupling based on the theory of software metrics. A cohesion metric and two coupling metrics were used to measure cohesion and coupling for ontology modules. The proposed metrics provide more detailed support in considering the different types of relationships between classes in ontology modules. In addition, the new coupling metrics contribute to checking the consistency between the ontology modules and their original ontology. The proposed metrics were validated using well-known verification frameworks and empirical experiments to complement the previous investigations. The results of this study offer ontology engineers valuable criteria with which to evaluate ontology modules and help ontology users select qualifying ontology modules.     

面向模块重用的描述逻辑SHJF本地性规则研究

可重用本体模块的抽取是本体重用的一个关键环节。与传统工程应用中使用的基于本体层次的结构化方法抽取本体模块相比,使用逻辑的方法能充分利用本体提供的语义信息,抽取的本体模块更具完整性和正确性。在研究保守扩展的本体模块理论基础上,根据Grau B C提出的 SHOJQ 本地性规则,提出并证明了描述逻辑SHJF对应的语义本地性规则和句法本地性规则,为基于该规则抽取可重用本体模块提供了理论基础。     

Where to publish and find ontologies? A survey of ontology libraries

One of the key promises of the Semantic Web is its potential to enable and facilitate data interoperability. The ability of data providers and application developers to share and reuse ontologies is a critical component of this data interoperability: if different applications and data sources use the same set of well defined terms for describing their domain and data, it will be much easier for them to “talk” to one another. Ontology libraries are the systems that collect ontologies from different sources and facilitate the tasks of finding, exploring, and using these ontologies. Thus ontology libraries can serve as a link in enabling diverse users and applications to discover, evaluate, use, and publish ontologies. In this paper, we provide a survey of the growing—and surprisingly diverse—landscape of ontology libraries. We highlight how the varying scope and intended use of the libraries affects their features, content, and potential exploitation in applications. From reviewing 11 ontology libraries, we identify a core set of questions that ontology practitioners and users should consider in choosing an ontology library for finding ontologies or publishing their own. We also discuss the research challenges that emerge from this survey, for the developers of ontology libraries to address.