基于上下文的综合战场环境本体建模

为满足综合战场环境建模的需求,提出一种基于上下文的综合战场环境本体建模方法。针对模型各种应用领域,分别抽取不同处理过程中共有的以及关键处理过程中特有的视角,形成上下文。对概念进行上下文限定,并在各自上下文中确定概念间的关系,构建上下文限定本体模型。应用结果表明,该模型检索效率较高,能够表述复杂关系。     

本体模块化的研究与实现

将软件工程中模块化的思想引入本体知识库的构建过程中,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,更有利于本体知识库的共享、重用和维护.用模块化的方法构建了汽车驾驶培训领域本体,建立方法库,在本体模块间用查询方式实现模块间的通信.这样的开发经验可以推广到其他领域.     

本体模块化的研究

将软件工程中模块化的思想引入本体知识库的构建过程中,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,更有利于本体知识库的共享、重用和维护.文中用模块化的方法构建了汽车驾驶培训领域本体,建立方法库,在本体模块间用查询方式实现模块间的通信.此模块化具有普适性,可以推广到其他领域.     

一种基于RDF图的本体匹配方法

本体匹配是建立两个本体之间映射关系的过程,一个高效、严格的相似度计算方法是本体匹配的前提条件,为此提出了一种基于RDF图匹配的方法。该方法用RDF图表示本体,使本体间的匹配问题转化为RDF图的匹配问题,并利用匹配树表示匹配的状态,通过匹配树计算出两个本体中各实体之间的相似度,进而得到两个本体之间的映射关系。实验结果表明,该方法在查全率和查准率方面都有很好的表现。     

基于本体匹配模型的研究

由于本体越来越广泛地应用于语义间的信息交换,并在大量分布领域中成为支持语言共享的关键要素,如同因特网一样的共享需求也相应增加。其中匹配本体的机制是获得最终目标的方式之一。本体匹配的过程中两个本体在概念级的语义相关以及根据这些语义相关性源本体实例被转换为目标本体实例。文章对此进行了研究。     

一种解决视频检索中语义不一致性方法

近年来,领域本体作为实现知识共享的基础已应用于视频语义检索。然而,同一或不同应用或领域知识可能存在多个本体,这些本体之间缺乏对术语使用的约定。这种现状所产生的语义不一致性导致难以实现检索过程中的知识推理和互操作过程的自动化,因而严重影响视频检索的效率和结果。提出一个两层本体结构,把同一或不同领域共享或共用的概念与知识映射到一个上层本体,从而解决多本体间语义上的不一致性,实现本体间知识的共享。并通过应用于视频场景的检索实验,说明该方法是有效和可行的。     

基于微波法的储油罐液位测量

分析了基于微波法雷达液位计的测量原理,影响因素及测量部分的系统设计和处理过程,导出了基于计算公式,较详细地介绍了实际液位的计算和校正过程。     

基于Change Tree的本体变更日志存储研究*

本体变更的目的是为了适应环境的变化,而变更的前提是要保证本体和使用它的应用或数据间的兼容性,本体概念或属性的重命名、合并、分解都可能造成本体与应用间的不兼容。提出了一种基于ChangeTree的本体变更日志存储方法。该方法采用有向树来组织本体的变更过程,用变更树节点来存储本体变更的信息,用节点间与或关系来关联本体的变更类型;同时该方法能采用树搜索来遍历本体变更的每一个历史阶段,从而为重现本体变化的历史提供了一种灵活高效的途径。     

基于Change-Tree的本体变更日志存储研究

本体变更的目的是为了适应环境的变化,而变更的前提是要保证本体和使用它的应用或数据间的兼容性,本体概念或属性的重命名、合并、分解都可能造成本体与应用间的不兼容.提出了一种基于Change-Tree的本体变更日志存储方法.该方法采用有向树来组织本体的变更过程,用变更树节点来存储本体变更的信息,用节点间与或关系来关联本体的变更类型;同时该方法能采用树搜索来遍历本体变更的每一个历史阶段,从而为重现本体变化的历史提供了一种灵活高效的途径.     

基于DRG的遥感影像几何校正

几何校正是遥感影像处理的重要环节,地面控制点(GCP)的采集是影响遥感影像几何校正精度和效率的重要因素。结合SPOT5影像处理过程,比较了实际作业中几种GCP采集模式的优缺点,总结探讨了在ERDAS IMAGINE中利用DRG进行地面控制点采集的几何校正方法。     

Ontology Evolution: Not the Same as Schema Evolution

As ontology development becomes a more ubiquitous and collaborative process, ontology versioning and evolution becomes an important area of ontology research. The many similarities between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution. However, there are also important differences between database schemas and ontologies. The differences stem from different usage paradigms, the presence of explicit semantics and different knowledge models. A lot of problems that existed only in theory in database research come to the forefront as practical problems in ontology evolution. These differences have important implications for the development of ontology-evolution frameworks: The traditional distinction between versioning and evolution is not applicable to ontologies. There are several dimensions along which compatibility between versions must be considered. The set of change operations for ontologies is different. We must develop automatic techniques for finding similarities and differences between versions.     

一种基于有向无环图的本体内聚度度量方法

目前,随着本体的广泛使用和快速发展,本体在结构与语义上变得越来越复杂。如何对本体的质量进行评估成为本体构建和重用的主要问题。在本体构建过程中,对本体进行评估有利于对本体进行重构和优化,以构建高质量的本体。在本体重用过程中,可以帮助用户在候选本体集中选择最优结构的本体。提出一种基于有向无环图(DAG)的本体内聚度度量方法,首先依据有向无环图的结构提出一组本体内聚度度量指标;然后根据已有的度量验证框架对其进行验证,说明度量指标在理论上有效;最后使用经典本体数据集进行实验,说明所提出的本体内聚度度量方法的合理性和有效性,有利于本体的构建和重用。     

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.     

基于范畴论的本体描述方法

针对各种本体语言在描述本体过程中容易产生理解不一致的问题,利用范畴论的抽象性及图形化表示的优势,提出一种基于范畴论的本体描述方法,并给出了本体、本体映射和本体实例化的范畴论定义,其中本体被描述成范畴中的对象,本体间的映射被描述成范畴中对象间的同态射,本体实例化被描述成范畴间的函子。在此基础上,结合共极限和推出给出本体合并的描述及解释,并证明了本体合并的一些典型性质。     

A statistically-based ontology matching tool

Ontologies have become a popular means of knowledge sharing and reuse. This has motivated development of large independent ontologies within the same or different domains with some overlapping information among them. In order to match such large ontologies, automatic matchers become an inevitable solution. This work explores the use of a predictive statistical model to establish an alignment between two input ontologies. We demonstrate how to integrate ontology partitioning and parallelism in the ontology matching process in order to make the statistical predictive model scalable to large ontology matching tasks. Unlike most ontology matching tools which establish 1:1 cardinality mappings, our statistical model generates one-to-many cardinality mappings.     

语义Web中的本体匹配研究

随着本体应用的快速发展,本体数量大幅增长,这些本体描述的内容存在重复和关联,但在本体模式上却表现各异。本体匹配旨在识别异构本体中存在语义关联的实体,并建立它们之间匹配关系。它对于消除本体异构、实现本体集成和数据融合等具有重要作用。形式化定义了语义Web中的本体匹配问题,并从本体匹配方法、本体匹配挑战和本体匹配原型系统3个方面调研了最新研究进展,旨在为进一步研究指明方向。     

Determining semantic similarity among entity classes from different ontologies

Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent entity classes.     

Overcoming the pitfalls of ontology authoring: Strategies and implications for tool design

The process of authoring ontologies appears to be fragmented across several tools and workarounds, and there exists no well accepted framework for common authoring tasks such as exploring ontologies, comparing versions, debugging, and testing. This lack of an adequate and seamless tool chain potentially hinders the broad uptake of ontologies, especially OWL, as a knowledge representation formalism. We start to address this situation by presenting insights from an interview-based study with 15 ontology experts. We uncover the tensions that may emerge between ontology authors including antagonistic ontology building styles (definition-driven vs. manually crafted hierarchies). We identify the problems reported by the ontology authors and the strategies they employ to solve them. These data are mapped to a set of key design recommendations, which should inform and guide future efforts for improving ontology authoring tool support, thus opening up ontology authoring to a new generation of users. We discuss future research avenues in light of these results.     

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.     

A K-way spectral partitioning of an ontology for ontology matching

Ontology matching, the process of resolving heterogeneity between two ontologies consumes a lot of computing memory and time. This problem is exacerbated in large ontology matching tasks. To address the problem of time and space complexity in the matching process, ontology partitioning has been adopted as one of the methods, however, most ontology partitioning algorithms either produce incomplete partitions or are slow in the partitioning process hence eroding the benefits of the partitioning. In this paper, we demonstrate that spectral partitioning of an ontology can generate high quality partitions geared towards ontology matching.