基于本体集成的语义标注模型设计

语义Web的全面实现需借助于语义标注,标注网页信息会涉及到多个本体.据此,通过研究桥本体,提出一个在本体集成的基础上建立起来的多本体语义标注模型.该模型利用桥本体集成顶层本体和多个领域本体,同时借助基于本体的信息抽取技术对网页进行语义标注,并将标注信息存入标注库,使标注信息与网页分离,提高语义检索的效率.通过举例说明了本模型的合理性.     

一种有效的服务资源自动语义标注方法

在服务网格中,分布式网络计算的实现依赖于如何在OGSA下实现服务交互问题的有效解决.为此,服务接口必须采用机器可理解的方式描述,从而为网格服务的动态发现和组合提供底层支持.服务语义标注技术满足了上述需求,它提出使用共享域本体中机器可理解的元数据标注服务资源描述.提出了一种有效的服务资源自动语义标注方法,该方法将服务语义标注过程分解为域标注和概念标注两个阶段,重点针对域标注问题,提出了基于机器学习的域标注算法,对实际服务资源的标注实验验证了该算法的有效性.     

Coupling semantic and statistical techniques for dynamically enriching web ontologies

With the development of the Semantic Web technology, the use of ontologies to store and retrieve information covering several domains has increased. However, very few ontologies are able to cope with the ever-growing need of frequently updated semantic information or specific user requirements in specialized domains. As a result, a critical issue is related to the unavailability of relational information between concepts, also coined missing background knowledge. One solution to address this issue relies on the manual enrichment of ontologies by domain experts which is however a time consuming and costly process, hence the need for dynamic ontology enrichment. In this paper we present an automatic coupled statistical/semantic framework for dynamically enriching large-scale generic ontologies from the World Wide Web. Using the massive amount of information encoded in texts on the Web as a corpus, missing background knowledge can therefore be discovered through a combination of semantic relatedness measures and pattern acquisition techniques and subsequently exploited. The benefits of our approach are: (i) proposing the dynamic enrichment of large-scale generic ontologies with missing background knowledge, and thus, enabling the reuse of such knowledge, (ii) dealing with the issue of costly ontological manual enrichment by domain experts. Experimental results in a precision-based evaluation setting demonstrate the effectiveness of the proposed techniques.     

Requirements and languages for the semantic representation of manufacturing systems

In the last years, attention has been devoted to the development of ontologies, which are ICT conceptual models allowing a formal and shared representation of a particular domain of discourse, and to the use of these representations in a variety of contexts, among which also the industrial engineering can be counted. Within the industrial engineering field, the manufacturing domain has not yet seen a wide application of ontologies. This paper firstly shows the use of ontologies for the semantic annotation of a Web Service-based architecture for the control of manufacturing systems; and then contributes to the research field of manufacturing domain ontologies by proposing a thorough literature review and analysis of the available languages supporting such objective. The paper collects the main requirements that semantic languages must meet to be used in the manufacturing domain with the outlined purpose. In fact, the available semantic languages are several and characterized by different features: the paper identifies the most proper ones for the manufacturing domain representation thanks to their analysis against the main requirements. Lastly, the paper shows how the discussed topics are deployed in a real industrial example.     

Web服务自动语义标注的本体定位方法研究

面向服务架构中,分布式网络计算的实现依赖于服务交互问题的有效解决。为此,服务接口必须采用机器可理解的方式描述,从而为服务的动态发现和组合提供底层支持。服务语义标注技术满足了上述需求,它是指通过共享域本体中机器可理解的元数据表示服务元素。本文将服务语义标注过程分解为域标注和概念标注两个阶段,重点针对域标注注问题,并提出了一种基于机器学习的域标注算法,对实际服务的标注实验验证了该算法的有效性     

Measuring Concept Similarities in Multimedia Ontologies: Analysis and Evaluations

The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have been extensively studied, but the question of how to exploit the structure of a multimedia ontology and existing inter-concept relations has not received similar attention. In this paper, we present a clustering-based method for modeling semantic concepts on low-level feature spaces and study the evaluation of the quality of such models with entropy-based methods. We cover a variety of methods for assessing the similarity of different concepts in a multimedia ontology. We study three ontologies and apply the proposed techniques in experiments involving the visual and semantic similarities, manual annotation of video, and concept detection. The results show that modeling inter-concept relations can provide a promising resource for many different application areas in semantic multimedia processing.     

基于形式概念分析和语义关联规则的目标图像标注

基于目标的图像标注一直是图像处理和计算机视觉领域中一个重要的研究问题.图像目标的多尺度性、多形变性使得图像标注十分困难.目标分割和目标识别是目标图像标注任务中两大关键问题.本文提出一种基于形式概念分析(Formal concept analysis, FCA)和语义关联规则的目标图像标注方法, 针对目标建议算法生成图像块中存在的高度重叠问题, 借鉴形式概念分析中概念格的思想, 按照图像块的共性将其归成几个图像簇挖掘图像类别模式, 利用类别概率分布判决和平坦度判决分别去除目标噪声块和背景噪声块, 最终得到目标语义簇; 针对语义目标判别问题, 首先对有效图像簇进行特征融合形成共性特征描述, 通过分类器进行类别判决, 生成初始目标图像标注, 然后利用图像语义标注词挖掘语义关联规则, 进行图像标注的语义补充, 以避免挖掘类别模式时丢失较小的语义目标.实验表明, 本文提出的图像标注算法既能保证语义标注的准确性, 又能保证语义标注的完整性, 具有较好的图像标注性能.     

Injecting domain knowledge in multi-objective optimization problems: A semantic approach

In the field of complex problem optimization with metaheuristics, semantics has been used for modeling different aspects, such as: problem characterization, parameters, decision-maker’s preferences, or algorithms. However, there is a lack of approaches where ontologies are applied in a direct way into the optimization process, with the aim of enhancing it by allowing the systematic incorporation of additional domain knowledge. This is due to the high level of abstraction of ontologies, which makes them difficult to be mapped into the code implementing the problems and/or the specific operators of metaheuristics. In this paper, we present a strategy to inject domain knowledge (by reusing existing ontologies or creating a new one) into a problem implementation that will be optimized using a metaheuristic. Thus, this approach based on accepted ontologies enables building and exploiting complex computing systems in optimization problems. We describe a methodology to automatically induce user choices (taken from the ontology) into the problem implementations provided by the jMetal optimization framework. With the aim of illustrating our proposal, we focus on the urban domain. Concretely, we start from defining an ontology representing the domain semantics for a city (e.g., building, bridges, point of interest, routes, etc.) that allows defining a-priori preferences by a decision maker in a standard, reusable, and formal (logic-based) way. We validate our proposal with several instances of two use cases, consisting in bi-objective formulations of the Traveling Salesman Problem (TSP) and the Radio Network Design problem (RND), both in the context of an urban scenario. The results of the experiments conducted show how the semantic specification of domain constraints are effectively mapped into feasible solutions of the tackled TSP and RND scenarios. This proposal aims at representing a step forward towards the automatic modeling and adaptation of optimization problems guided by semantics, where the annotation of a human expert can be now considered during the optimization process.     

基于领域本体的数据清洗研究

对数据清洗过程中的语义问题进行了分类,基于领域本体提出了领域概念树和精确度水平节点集的概念。结合领域概念树和精确度水平节点集,给出了基于领域本体的数据清洗方法。该方法通过利用领域本体包含的语义信息,提高了数据清洗质量。与传统的数据清洗方法相比,由于该方法只与本体领域模型进行交互,不局限于特定领域,所以扩展性更强,数据清洗的质量也较高。     

Learning to Share Meaning in a Multi-Agent System

The development of the semantic Web will require agents to use common domain ontologies to facilitate communication of conceptual knowledge. However, the proliferation of domain ontologies may also result in conflicts between the meanings assigned to the various terms. That is, agents with diverse ontologies may use different terms to refer to the same meaning or the same term to refer to different meanings. Agents will need a method for learning and translating similar semantic concepts between diverse ontologies. Only until recently have researchers diverged from the last decade's common ontology paradigm to a paradigm involving agents that can share knowledge using diverse ontologies. This paper describes how we address this agent knowledge sharing problem of how agents deal with diverse ontologies by introducing a methodology and algorithms for multi-agent knowledge sharing and learning in a peer-to-peer setting. We demonstrate how this approach will enable multi-agent systems to assist groups of people in locating, translating, and sharing knowledge using our Distributed Ontology Gathering Group Integration Environment (DOGGIE) and describe our proof-of-concept experiments. DOGGIE synthesizes agent communication, machine learning, and reasoning for information sharing in the Web domain.     

基于玉米本体的语义检索系统

采用形式概念分析方法由词汇-文件关系表构造概念格并进行约简,建立玉米种植本体。提出基于领域本体的语义标注方法,改进现有的权值计算方法以获得特征词,经句法分析生成RDF三元组。实现基于领域本体的用户查询处理和查询推荐算法,研制面向玉米种植的语义检索系统,并选取100篇玉米种植文档作为实验文本集合进行对比实验,结果表明,该语义检索系统在查准率和查全率上均优于基于关键字的检索方法。     

Crowdsourcing techniques to create a fuzzy subset of SNOMED CT for semantic tagging of medical documents

Ontologies and other schemes are useful for allowing semantic tagging of documents for many applications on the semantic web. Representing uncertainty on the semantic web is becoming increasingly common, using ontologies and other techniques. Ontology and declarative tools allow documents using concepts contained in these ontologies to be reasoned about using computer systems. Very large ontologies and vocabularies have been created; however, users may find it difficult to select the correct concept or term when there are large numbers of items that on face value appear to represent the same idea. Creating subsets of ontologies is a popular approach to solve this problem but this may not fit well with the need to deal with complex domains. However, crowdsourcing techniques, which harness the power of large groups, may be more effective than document analysis or expert opinion. In crowdsourcing, large numbers of people collaborate by performing relatively simple tasks usually using applications distributed via the World Wide Web. This approach is being tested in the medical domain using a very large clinical vocabulary, SNOMED CT.     

基于本体的图像自动标注

提出一种新的图像本体标注的框架,结合领域本体中概念的关系,通过层次概率标注来获得图像高层语义概念的标注,实现待标注图像语义的自动标注。我们将图像的语义可以定义为属性概念和高层抽象概念,采用二次标注方法实现对于图像语义的自动标注。实验证明,本文的方法可以使图像获得丰富的高层抽象语义概念标注,从而缩小"语义鸿沟",有效提高了检索的效率和精确度。     

A RULE-BASED APPROACH FOR SEMANTIC ANNOTATION EVOLUTION

An approach for managing knowledge in an organization in the new infrastructure of Semantic Web consists of building a corporate semantic web (CSW). The main components of a CSW are (i) evolving resources distributed over an intranet and indexed using (ii) semantic annotations expressed with the vocabulary provided by (iii) a shared ontology. However, changes in the operating environment may lead to some inconsistencies in the system and they result in need of modifications of the CSW components. These changes need to be evolved and well managed. In this paper we present a rule-based approach allowing us to detect and correct semantic annotation inconsistencies. This rule-based approach is implemented in the CoSWEM system enabling to manage the evolution of such a CSW, especially to address the evolution of semantic annotations when its underlying ontologies change.     

基于形式概念分析的图像场景语义标注模型

为生成有效表示图像场景语义的视觉词典,提高场景语义标注性能,提出一种基于形式概念分析(FCA)的图像场景语义标注模型。该方法首先将训练图像集与其初始的视觉词典抽象为形式背景,采用信息熵标识了各视觉单词的权重,并分别构造了各场景类别概念格结构;然后再利用各视觉单词权重的均值刻画概念格内涵上各组合视觉单词标注图像的贡献,按照类别视觉词典生成阈值,从格结构上有效提取了标注各类场景图像语义的视觉词典;最后,利用K最近邻标注测试图像的场景语义。在Fei-Fei Scene 13类自然场景图像数据集上进行实验,并与Fei-Fei方法和Bai方法相比,结果表明该方法在β=0.05和γ=15时,标注分类精度更优。     

Axiom-based ontology matching

Abstract: Managing multiple ontologies is now a core question in most of the applications that require semantic interoperability. The semantic web is surely the most significant application of this report: the current challenge is not to design, develop and deploy domain ontologies but to define semantic correspondences among multiple ontologies covering overlapping domains. In this paper, we introduce a new approach of ontology matching named axiom-based ontology matching. As this approach is founded on the use of axioms, it is mainly dedicated to heavyweight ontologies, but it can also be applied to lightweight ontologies as a complementary approach to the current techniques based on the analysis of natural language expressions, instances and/or taxonomical structures of ontologies. This new matching paradigm is defined in the context of the conceptual graphs model, where the projection (i.e. the main operator for reasoning with conceptual graphs which corresponds to homomorphism of graphs) is used as a means to semantically match the concepts and the relations of two ontologies through the explicit representation of the axioms in terms of conceptual graphs. We also introduce an ontology of representation, called MetaOCGL, dedicated to the reasoning of heavyweight ontologies at the meta-level.     

Semantic context based refinement for news video annotation

Automatic video annotation is to bridge the semantic gap and facilitate concept based video retrieval by detecting high level concepts from video data. Recently, utilizing context information has emerged as an important direction in such domain. In this paper, we present a novel video annotation refinement approach by utilizing extrinsic semantic context extracted from video subtitles and intrinsic context among candidate annotation concepts. The extrinsic semantic context is formed by identifying a set of key terms from video subtitles. The semantic similarity between those key terms and the candidate annotation concepts is then exploited to refine initial annotation results, while most existing approaches utilize textual information heuristically. Similarity measurements including Google distance and WordNet distance have been investigated for such a refinement purpose, which is different with approaches deriving semantic relationship among concepts from given training datasets. Visualness is also utilized to discriminate individual terms for further refinement. In addition, Random Walk with Restarts (RWR) technique is employed to perform final refinement of the annotation results by exploring the inter-relationship among annotation concepts. Comprehensive experiments on TRECVID 2005 dataset have been conducted to demonstrate the effectiveness of the proposed annotation approach and to investigate the impact of various factors.     

Semi-automatic construction of domain ontology for agent reasoning

One of the key elements of the Semantic Web technologies is domain ontologies and those ontologies are important constructs for multi-agent system. The Semantic Web relies on domain ontologies that structure underlying data enabling comprehensive and transportable machine understanding. It takes so much time and efforts to construct domain ontologies because these ontologies can be manually made by domain experts and knowledge engineers. To solve these problems, there have been many researches to semi-automatically construct ontologies. Most of the researches focused on relation extraction part but manually selected terms for ontologies. These researches have some problems. In this paper, we propose a hybrid method to extract relations from domain documents which combines a named relation approach and an unnamed relation approach. Our named relation approach is based on the Hearst’s pattern and the Snowball system. We merge a generalized pattern scheme into their methods. In our unnamed relation approach, we extract unnamed relations using association rules and clustering method. Moreover, we recommend candidate relation names of unnamed relations. We evaluate our proposed method by using Ziff document set offered by TREC.