基于推理和相似度计算的语义Web服务匹配策略

将本体的概念引入Web服务提升了服务的语义表达能力和发现能力,传统的语义Web服务匹配主要有基于推理和基于本体相似度计算两种方式.基于推理的服务匹配较容易实现,具有稳定的查准率和较高的运行效率,但同一级结果间不能进一步区分结果的匹配度.基于相似度计算的服务发现可以精确地描述结果与查询条件的匹配程度,便于用户比较,但实现较为复杂,并且在最坏情况下有糟糕的响应时间.在基于推理的服务匹配策略的基础上,提出了一种带有语义相似度计算的服务匹配策略,并用一个书籍领域本体的匹配实例进行了说明.     

一种基于人工免疫的本体匹配算法

针对目前本体匹配算法存在运行效率低和匹配准确度不高等问题,提出一种基于人工免疫的动态本体匹配算法,用来快速地从现有本体中筛选出用户所需的子本体。该算法根据用户行为信息构建抗原本体模型,利用情景匹配确定其领域上下文环境,然后通过结构匹配获得匹配度最高的本体,最后对本体执行语义匹配得到最终需要的子本体。实验表明,该算法提高了本体匹配的准确度和效率。     

流程可定制本体匹配框架:RiMOM2

本体作为语义Web中的语义表示形式,是语义Web体系结构中的核心元素,是实现知识共享、协同工作的关键。然而现实世界中本体自身与生俱来的分布性和异构性,又极大地限制了数据的共享与集成。为了实现知识的共享、数据的集成,近年来针对本体匹配方法的研究得到了广泛的重视。随着本体匹配研究的深入,许多有效的本体匹配方法被提出。RiMOM2正是一种集成了多种有效本体匹配方法的多策略本体匹配框架。它尽可能地向初级用户隐藏不必要的阂值设定和参数设置,而向高级用户提供匹配流程的可定制功能,以期针对不同用户实现一种既能适用于普遍本体匹配任务,操作简易,又能达到具有针对性匹配效果的本体匹配工具。同时该框架具有匹配方法组件的易扩展性。     

一种半自动化的复杂语义匹配系统

模式间的语义匹配是实现数据共享和交换的基础和关键。介绍了一种用于完成异构数据源复杂语义匹配的半自动化匹配系统iSeMatch。其多匹配器架构保证了匹配的效率,同时使系统具有高度可扩展性;而包括领域约束、匹配历史和领域本体模型在内的领域知识的利用,更进一步提高了匹配结果的准确率。该系统是半自动化生成语义匹配并尽可能减少人工干预的一种有效的解决方案。     

基于本体的任务和知识个体匹配模型研究

为实现在知识产品在线定制过程中任务和知识个体的有效匹配,鉴于传统的基于关键词的匹配技术忽略语义信息的缺点,本文提出了一个基于本体的任务和知识个体匹配模型。首先提出了一种综合改进的概念语义相似度计算方法用以提高概念间的语义相似度计算的准确性,然后将任务信息和知识个体的信息分别抽象为任务概念向量和能力概念向量,通过计算两个概念向量之间的语义相似度,得到任务和知识个体的匹配结果。实验结果表明,该模型具有较高的准确率和召回率,能够为知识产品在线定制提供有效的匹配服务。     

基于WSDL-S的轻量级语义Web服务匹配模型

针对现有大多数基于语义的Web服务发现方法实施难度大,实用性不强的问题,提出了一种基于WSDL-S描述的轻量级语义Web服务匹配模型.该模型最大的特点是只需用户输入简单的服务查询字符串就能自动实现Web服务的匹配和调用.服务匹配经过领域本体匹配和基于WordNet词典的同义词匹配两个步骤,特别是领域本体匹配过程中的学习模块能有效提高系统性能.实验结果表明,该系统性能提高在20%到82%之间.     

基于本体间语义相似度的服务组合算法

随着服务提供者不断发布新的服务,Web环境下的服务数量在不断增长,Web服务组合的时间也会更长.为满足一些对响应时间要求较高的用户,本文提出了一种基于本体间语义相似度的服务组合算法,该算法利用领域本体间的语义关系,计算参数间的语义相似度,将其与给定的一个语义相似度阈值比较来选择匹配的服务,然后通过回溯得到服务组合序列.实验表明,该算法可以在较短的时间内实现服务的组合,其服务组合时间与效率明显优于BF*和HWSCO算法.     

基于概念图匹配的语义相似性算法研究

本文针对传统搜索技术查全率和查准率不能满足用户日益增长的需求这一突出问题,提出一种基于概念图语义匹配的方法来计算两个本体中类之间的相似性,文中提到的本体是由实体类、这些类之间的语义关系和描述这些类的不同特征组成的.该模型首先将用户的查询信息转变为一个概念图,然后和已有的资源概念图进行匹配计算语义的相似性,实例表明该方法可以满足用户的需求,提高了检索效率.     

基于OWL-S服务匹配的信息查询模型

根据Web服务的特点,提出一种采用两级匹配、带有本体语义功能,提高Web服务匹配的效率,具有基于领域本体能表达清楚自己需要的信息、精确用户查询和基于模糊本体的逐步精确的信息查询模型,对动态电子商务系统的个性化及界面集成提供一个值得借鉴的解决方案。     

查询请求的语义扩展研究

语义扩展是使用语义相同或相近的词语对原始查询扩展,以减少查询结果与用户查询间的不匹配。针对现有查询扩展策略缺乏有效的语义处理机制,本文把基于描述逻辑的本体与自然语言处理技术综合起来,提出一种新的语义查询扩展算法。实验表明该算法具有良好的性能,能够有效提高查准率。     

A system for information retrieval in a medical digital library based on modular ontologies and query reformulation

Ontologies have proven to be useful in the area of Information Retrieval and the biomedical informatics community has acknowledged, in recent years, their utility. However, building and updating manually ontologies is a long and tedious task. This paper proposes a system that allows any search engine to develop its semantic layer by applying ontology learning techniques on Web snippets and applies it to a well-known medical digital library, PubMed. The new system (SemPubMed) automatically builds new ontology fragments related to the user’s query and then it reformulates queries using the new concepts in order to improve information retrieval. Our system has endured a twofold evaluations. On the one hand, we have evaluated the quality of the modular ontologies built by the system. On the other hand, we have studied how the semantic reformulation of the queries has led to an improvement of the quality of the results given by PubMed, both in terms of precision and recall. Obtained results show that adding semantic layer to PubMed enables an improvement of query reformulation and predicted ranking score.     

An experimental evaluation of ontology-based user profiles

In recent years, a number of research works have been carried out to improve the information retrieval process by exploiting external knowledge, e.g. by employing ontologies. Even though ontologies seem to be a promising technique to improve the retrieval process, hardly any study has been performed to evaluate the use of ontologies over a longer time period to model user interests. In this work we introduce an ontology based video recommender system that exploits implicit relevance feedback to capture users’ evolving information needs. The system exploits a generic ontology to organise users’ interests. We evaluate the recommendations by performing a user-centred multiple time-series study where participants were asked to include the system into their daily news gathering routine. The results of this study suggest that the system can be successfully employed to improve personal information seeking tasks in news domain.     

On-the-fly repair of structural and lexical ontology mismatches in multi-agent systems

In a Semantic-Web-like multi-agent environment, ontology mismatch is inevitable: we can’t realistically expect agents created at different times and places by different people to commit to one unchanging universal ontology. Ontology matching seems to be the only solution to such a problem. However, standard techniques for aligning heterogeneous ontologies are based on time-consuming, off-line and often semi-automated processes and pre-suppose full access to the interacting agents’ ontologies. This is far from ideal in situations where agents meet for the first time, interact quickly and have restricted access to other agents’ private information. In this paper we present the Ontology Repair System (ORS), which attempts to match fully-fledged first-order ontologies automatically using incomplete information. Particular emphasis is laid on its semantic matching module, the Semantic Matcher, which provides a solution for lexical mismatches, which are the most common and the most challenging to address. ORS and the Semantic Matcher have been implemented and evaluated, with very promising results.     

Multimedia ontology matching by using visual and textual modalities

Ontologies have been intensively applied for improving multimedia search and retrieval by providing explicit meaning to visual content. Several multimedia ontologies have been recently proposed as knowledge models suitable for narrowing the well known semantic gap and for enabling the semantic interpretation of images. Since these ontologies have been created in different application contexts, establishing links between them, a task known as ontology matching, promises to fully unlock their potential in support of multimedia search and retrieval. This paper proposes and compares empirically two extensional ontology matching techniques applied to an important semantic image retrieval issue: automatically associating common-sense knowledge to multimedia concepts. First, we extend a previously introduced textual concept matching approach to use both textual and visual representation of images. In addition, a novel matching technique based on a multi-modal graph is proposed. We argue that the textual and visual modalities have to be seen as complementary rather than as exclusive sources of extensional information in order to improve the efficiency of the application of an ontology matching approach in the multimedia domain. An experimental evaluation is included in the paper.     

Ontology-based user profile learning

Personal agents gather information about users in a user profile. In this work, we propose a novel ontology-based user profile learning. Particularly, we aim to learn context-enriched user profiles using data mining techniques and ontologies. We are interested in knowing to what extent data mining techniques can be used for user profile generation, and how to utilize ontologies for user profile improvement. The objective is to semantically enrich a user profile with contextual information by using association rules, Bayesian networks and ontologies in order to improve agent performance. At runtime, we learn which the relevant contexts to the user are based on the user’s behavior observation. Then, we represent the relevant contexts learnt as ontology segments. The encouraging experimental results show the usefulness of including semantics into a user profile as well as the advantages of integrating agents and data mining using ontologies.     

DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO)

Increased availability of mobile computing, such as personal digital assistants (PDAs), creates the potential for constant and intelligent access to up-to-date, integrated and detailed information from the Web, regardless of one's actual geographical position. Intelligent question-answering requires the representation of knowledge from various domains, such as the navigational and discourse context of the user, potential user questions, the information provided by Web services and so on, for example in the form of ontologies. Within the context of the SmartWeb project, we have developed a number of domain-specific ontologies that are relevant for mobile and intelligent user interfaces to open-domain question-answering and information services on the Web. To integrate the various domain-specific ontologies, we have developed a foundational ontology, the SmartSUMO ontology, on the basis of the DOLCE and SUMO ontologies. This allows us to combine all the developed ontologies into a single SmartWeb Integrated Ontology (SWIntO) having a common modeling basis with conceptual clarity and the provision of ontology design patterns for modeling consistency. In this paper, we present SWIntO, describe the design choices we made in its construction, illustrate the use of the ontology through a number of applications, and discuss some of the lessons learned from our experiences.     

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.     

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.     

基于用户行为的启发式本体搜索机制

为了能够以较高的准确率搜索到用户所需要的领域本体,在分析本体搜索需求和研究用户搜索行为的基础上,提出了一种基于用户行为的启发式本体搜索机制,利用不同用户由于领域认知不同,输入的具有领域共性的搜索关键词不同,实现用户搜索关键词的启发式扩展和搜索匹配度的提高。实验表明,使用该方法执行本体搜索具有较高的准确率和召回率。