A genetic fuzzy agent using ontology model for meeting scheduling system

A Genetic Fuzzy Agent (GFA) using the ontology model for Meeting Scheduling System (MSS) is presented in this paper. The ontology model includes the Fuzzy Meeting Scheduling Ontology (FMSO) and the Fuzzy Personal Ontology (FPO) that can support to construct the knowledge base of the GFA. The FMSO is utilized to record and describe the meeting scheduling domain knowledge for the GFA. In addition, we implement a FMSO editor for generating the Web Ontology Language, OWL, that will be utilized by the GFA. Furthermore, the GFA will infer the suitable meeting time slots based on the ontology model. Moreover, it also adjusts the FMSO and FPO based on the results of the genetic learning mechanism for the next meeting. The experimental results show that our approach can effectively work for MSS.     

面向文本的本体学习研究概述

对本体（ontology）的研究在计算机领域变得越来越广泛,但手工构造本体是一项繁琐而辛苦的任务,还会导致知识获取瓶颈。本体学习技术是利用本体工程技术和机器学习技术等众多学科技术来实现本体的（半）自动构建。本体的学习可以面向文本、知识库、结构化数据、半结构化数据和无结构数据。本文主要介绍了面向文本的本体学习,并对其中的学习内容、学习方法、学习工具、学习过程和系统评价等关键技术进行了说明,特别介绍了学习方法中的基于统计的方法、词汇句法模式法和形式概念分析法并对其优缺点做了简单的分析。     

Automated ontology construction for unstructured text documents

Ontology is playing an increasingly important role in knowledge management and the Semantic Web. This study presents a novel episode-based ontology construction mechanism to extract domain ontology from unstructured text documents. Additionally, fuzzy numbers for conceptual similarity computing are presented for concept clustering and taxonomic relation definitions. Moreover, concept attributes and operations can be extracted from episodes to construct a domain ontology, while non-taxonomic relations can be generated from episodes. The fuzzy inference mechanism is also applied to obtain new instances for ontology learning. Experimental results show that the proposed approach can effectively construct a Chinese domain ontology from unstructured text documents.     

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.     

Ontology构建中概念与关系的获取与处理

有效构建一个Ontology,需要对领域中的概念及其关系有清楚的理解。如果不能很好地抽取领域概念,往往不能开发出结构组织良好的Ontology。论文从Ontology的基本理论出发,基于基本概念、角色和关系,探讨了Ontology开发中的基本概念、角色概念、整体概念与部分概念等基本要素,并说明这些要素的处理,并为Ontology的开发提供了范例。     

Ontology Integration: Approaches and Challenging Issues

In recent years, the decentralized development of ontologies has led to the generation of multiple ontologies of overlapping knowledge. This heterogeneity problem can be tackled by integrating existing ontologies to build a single coherent one. Ontology integration has been investigated during the last two decades, but it is still a challenging task. In this article, we provide a comprehensive survey of all ontology integration aspects. We discuss related notions and scrutinize existing techniques and literature approaches. We also detail the role of ontology matching in the ontology integration process. Indeed, the ontology community has adopted the splitting of the ontology integration problem into matching, merging and repairing sub-tasks, where matching is a necessary preceding step for merging, and repairing can be included in the matching process or performed separately. Ontology matching and merging systems have become quite proficient, however the trickiest part lies in the repairing step. We also focus on the case of a holistic integration of multiple heterogeneous ontologies, which needs further exploration. Finally, we investigate challenges, open issues, and future directions of the ontology integration and matching areas.     

一种本体知识文摘方法的研究和实现

本体作为领域知识的表示方法，已经成为语义Web的基础。本体通常由领域专家建立，用于表示领域中概念以及概念与概念之间的关系。但这也使得普通用户难以理解本体中描述的信息。普通用户往往希望本体中的信息能够以自然语言的形式描述。这正是本文讨论的主要问题。本文采用分治策略，利用基于嵌套复杂模板的解决方案，设计并实现了本体知识文摘的算法。我们开发了一个原型系统SWARMS，并将该文摘算法进行了运用。初步的实验表明，本文提出的方法取得较好的结果。     

一种改进的领域本体分类算法

本体学习技术的研究目前还处于探索阶段。研究了知识生产领域中本体学习技术的应用,提出了本体学习中领域本体的分类算法。本算法同时考虑了概念之间的语义相似度与结构相关度,并以"分类量化值"作为领域本体的分类标准。实验证明,本算法较之其它相关算法更为有效。     

基于本体描述构件库中的本体演化研究

在构件的描述与检索中引入本体，可以更好地表示构件间的语义信息。本体是对知识的表达，可以对知识进行有效的组织和管理，实现知识共享和重利用，从而充分有效地利用知识资源。但是知识不是一成不变的，基于本体描述的构件库系统中的本体库需要更新和维护。该文提出了一种本体演化理论框架，探讨了其研究方向和重点。     

基于领域学习的本体映射方法

在语义数据集成中本体映射是关键,手工设置映射关系不仅消耗时间而且不准确,需要使用本体映射工具自动发现这种映射关系.在现有本体映射方法的基础上提出了基于领域学习的映射发现策略,可以从领域知识中发现复杂映射的规则,增加映射时的实例数据,从而提高映射发现的查全率和准确率;同时利用本体中丰富的语义知识去筛选候选数据,从而降低映射方法的算法复杂度.实验证明该方法是有效的.     

本体进化及维护的研究总结

本体是共享概念的规范、精确的描述。本体本身并不是静态的模型,所以它必须能够捕获意思和关系的变化。这样,本体的映射和进化就成为本体学习和发展的一个基本任务。本体进化就是获得新的信息和知识时适当地维护和扩充已有本体。     

Ontology design with a granular approach

Ontology design for complex applications is quite a challenge. The quality of an ontology is highly dependent upon the capabilities of designers, and the collaborative design process is hampered by the difficulty of balancing the viewpoints of different designers. In this paper, we present a granular view of ontology: ontologies are granular, ontologies are granular approximations of conceptualizations and conceptual-relation granules of an ontology are ordered tuples. We then propose a corresponding granular ontology design approach. In our granular ontology design approach, the unified granular cognition level and hierarchies of sub-concepts are initialized before ontological terms are designed in detail, which reduces the subjective effects of the capabilities of designers. Our approach also introduces the idea of optimization to choose an optimal subset, which can best approximate the real concept domain, from the knowledge rule set presented by different domain experts. The optimal subset is chosen on the basis of the principle of granular ontology knowledge structure.     

从文本中获取植物知识方法的研究

知识获取一直是人工智能中的一个关键问题.当前,知识的文本挖掘（KAT）已经成为计算机领域的一个重要的研究课题.本文中,给出了基于植物本体的从海量网页文本库中自动获取植物领域知识的方法.该方法包括两个部分,一是植物本体（Botanical Ontology）,它是顾芳博士等建立的生物本体的扩展.第二部分是以植物本体为基础,在网络文本库中进行文本挖掘（Text Mining）,自动获取植物知识.实验证明,基于本体的文本挖掘是一种有效的知识获取方法.     

Knowledge and software modeling using UML

Ontology can be considered as a comprehensive knowledge model which enables the developer to practice knowledge, instead of code, reuse. In the development of knowledge-based systems, different modeling languages are employed at different stages of the development process. By using a common modeling language for the knowledge and software models, knowledge instead of software reuse can be achieved. We illustrate the process by first presenting an ontology developed for an industrial domain and then investigate Unified Modeling Language (UML) as an ontology modeling tool. Since any model expressed in UML can be translated into a software model, the transition from the knowledge model to system implementation is better supported with the proposed approach. The industrial domain of selecting a remediation technique for petroleum contaminated sites is adopted for the illustration case study.     

企业会计核算领域中的信息本体研究

一个共享的会计信息本体可以清晰会计领域的知识结构,促进企业会计数据共享和互操作,并为智能会计信息系统和领域语义网络构建打下基础。该文在分析了计算机领域中本体的概念、作用和开发方法的基础上,对企业会计核算领域中的信息本体进行了研究,最后给出了基于本体的会计信息查询的实现框架。     

CFinder: An intelligent key concept finder from text for ontology development

Key concept extraction is a major step for ontology learning that aims to build an ontology by identifying relevant domain concepts and their semantic relationships from a text corpus. The success of ontology development using key concept extraction strongly relies on the degree of relevance of the key concepts identified. If the identified key concepts are not closely relevant to the domain, the constructed ontology will not be able to correctly and fully represent the domain knowledge. In this paper, we propose a novel method, named CFinder, for key concept extraction. Given a text corpus in the target domain, CFinder first extracts noun phrases using their linguistic patterns based on Part-Of-Speech (POS) tags as candidates for key concepts. To calculate the weights (or importance) of these candidates within the domain, CFinder combines their statistical knowledge and domain-specific knowledge indicating their relative importance within the domain. The calculated weights are further enhanced by considering an inner structural pattern of the candidates. The effectiveness of CFinder is evaluated with a recently developed ontology for the domain of ‘emergency management for mass gatherings’ against the state-of-the-art methods for key concept extraction including—Text2Onto, KP-Miner and Moki. The comparative evaluation results show that CFinder statistically significantly outperforms all the three methods in terms of F-measure and average precision.     

一种改进的面向文本的领域概念筛选算法

在语义技术及其应用中,本体学习是一个研究热点,而领域概念筛选则是本体学习的基础。对于领域概念筛选问题,领域一致度与领域相关度相结合的方法效果较好,却也存在信息描述不全的缺点,因此提出了一种针对此问题的改进的领域概念筛选算法。通过计算候选概念之间的语义相似度,识别出低频的具有同义关系和整体一部分关系的词语集,过滤掉部分冗余概念,然后采用改进的领域相关度和领域一致度相结合的公式进行筛选。实验表明,该方法提高了领域概念筛选的有效性。     

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.     

基于维基的协作式本体注记平台

本体技术使得信息系统具有处理知识的能力,增强信息检索与共享,但是目前本体工具与本体语言对普通的用户具有较高的门槛,加上本体建立方式依赖于本体构造者所注重的知识领域的议题,这反而阻碍了本体的演化。本文提出了一种以本体知识库为核心,基于维基的协作式本体注记平台,利用维基方便,简单及共享的特性推动网络化的本体建设。     

军事领域本体构建研究

在仿真系统的研究过程中,面临军事领域知识获取及不同应用之间的互操作等问题.这些难题的背后实际是技术人员和军事人员之间理解一致性和机器之间理解一致性的问题.本体是关于事物本质的模型,已被广泛应用于知识分类、表达、共享及重用等方面.首先,讨论了本体的概念并给出了领域本体构建的一套通用原则和方法.然后详细分析了军事领域本体所包含的各方面知识元素,包括作战单元类、单元之间的关系、交战行为、交战规则、武器装备、作战计划等,并分别给出了它们的简单表示结构.最后概要讨论了多个本体合并时的本体检验问题.标准化的军事知识表示结构可以为技术人员和军事人员之间的交流提供统一的认识和参照,为机器之间的互操作提供一种"语言",从而消除理解上的冲突和混乱,为作战仿真系统的开发扫清了道路.