一种基于加权领域本体的语义检索方法

提出了新方法WOSR,以对已经被本体概念标注的领域信息资源进行语义检索.WOSR方法首先建立领域本体,再采用均等概率分布方法为概念赋权,然后通过概念的权重求出概念相似度,最后计算用户检索请求和信息资源之间的语义相似度,并根据相似度的大小排序输出检索结果.实验结果表明,WOSR方法比其他经典方法的检索效果更好.     

Learning and inferencing in user ontology for personalized Semantic Web search

User modeling is aimed at capturing the users’ interests in a working domain, which forms the basis of providing personalized information services. In this paper, we present an ontology based user model, called user ontology, for providing personalized information service in the Semantic Web. Different from the existing approaches that only use concepts and taxonomic relations for user modeling, the proposed user ontology model utilizes concepts, taxonomic relations, and non-taxonomic relations in a given domain ontology to capture the users’ interests. As a customized view of the domain ontology, a user ontology provides a richer and more precise representation of the user’s interests in the target domain. Specifically, we present a set of statistical methods to learn a user ontology from a given domain ontology and a spreading activation procedure for inferencing in the user ontology. The proposed user ontology model with the spreading activation based inferencing procedure has been incorporated into a semantic search engine, called OntoSearch, to provide personalized document retrieval services. The experimental results, based on the ACM digital library and the Google Directory, support the efficacy of the user ontology approach to providing personalized information services.     

基于本体的数字图书馆个性化用户模型表示

本文针对当前个性化服务中基于关键词的用户兴趣表示方法在语义上的不足,结合本体语义信息丰富的特点,提出了一种基于本体的用户模型表示方法。在数字图书馆领域内,介绍了本体形式化描述并构建了数字图书馆领域本体,给出了用户模型的表示方法。并以个性化信息检索为例,说明了利用用户兴趣本体表示中的同义,上下位等关系给用户提供服务的方法。实验表明基于本体的表示方法能够给用户提供更加个性化的信息。     

基于本体的智能检索及其在泌尿外科中的应用

以本体论作为指导理论,通过研究泌尿外科辅助诊断系统模型,在泌尿外科领域本体的基础上研究语义相似度、语义相关度的计算方法,并提出新的相关度计算方法。该方法可以定量地分析领域本体中的概念间相关度。并通过建立泌尿外科本体,实现基于泌尿外科本体的语义推理。     

基于领域本体的概念语义相似度计算研究

通过对领域本体参照下传统概念的3种语义相似度的计算模型研究,针对这3种计算模型的优缺点和领域本体所特有的性质,提出了一种改进的基于领域本体的概念语义相似度计算模型.实验结果表明,该计算模型通过定量的分析利用本体构词所描述的概念、特性之间的相似度,可以指导基于领域知识本体的语义查询中概念集扩充和查询结果排序,为概念之间的语义关系提供一种有效的量化.     

Image retrieval ++—web image retrieval with an enhanced multi-modality ontology

In this paper we present an enhanced multi-modality ontology-based approach for web image retrieval step by step. Several ontology-based approaches have been made in the field of multimedia retrieval. Our multi-modality approach is one of the earliest attempts to integrate information from different modalities and apply the model in a complex domain. In order to develop the model, we need to answer the following questions: (1) how to find the proper structure and construct an ontology which can integrate information from different modalities; (2) how to quantify the matching degree (concept similarity) and provide an independent ranking mechanism; (3) how to ensure the scalability of this approach when applied to large domains. The first question has been answered by our multi-modality ontology which has been discussed in Wang et al. (Does ontology help in image retrieval? In: Asia-Pacific workshop on visual information processing, 2006) and its extension (Wang et al., Does ontology help in image retrieval?—a comparison between keyword, text ontology and multi-modality ontology approaches, ACM Press, New York, NY, USA, pp 109–112, 2006). More details about this work is given later. The main focus of this paper is that we propose a new ranking mechanism using Spearman’s ranking correlation to measure the similarity of concepts in the ontology. We take the priorities of information from different modalities into consideration. This algorithm gives the answer of the second question. The semantic matchmaking result is quantized and the degree of similarity between concepts is calculated. For the third question, importing of ontology will resolve the scalability issue but computing concept similarity and identify relationships when integrating different ontologies will be beyond the scope of this paper. To convince readers that our multi-modality ontology and concept similarity ranking is the right step forward, we decided to work on the animal kingdom. We believe this domain is challenging as demonstrated by images depict animals in a wide range of aspects, pose, configurations and appearances. We experimented with a data sets of 4,000 web images. Based on ground truth, we analyze the image content and text information, build up the enhanced multi-modality ontology and compare the retrieval results. Results show that we can even classify close animal species which share similar appearances and we can infer their hidden relationships from the canine family graph. By assigning a ranking to the semantic relationships we show unequivocal evidence that our improved model achieves good accuracy and performs comparable result with the Google re-ranking result in our previous work.     

一种本体概念的语义相似度计算方法

概念语义相似度已广泛应用于 Web 服务发现、本体映射等领域, 但现有的概念语义相似度计算方法对概念间语义相似程度的区分不够细致. 本文从本体结构出发, 首先提出了自底向上的本体概念出现概率计算方法, 并在此基础上改进了基于节点信息量的概念语义相似性度量方法; 然后又设计了基于边计算的本体概念语义相似度计算方法; 最后对上述两种方法线性加权, 提出了一种加权的本体概念语义相似度计算方法. 实验结果表明该方法能进一步正确区分本体中父子概念及兄弟概念间的相似程度.     

改进的领域本体概念相似度计算方法

在领域本体中,概念间往往存在多条路径,现有的基于语义距离的方法只考虑最短距离的路径,不能完全体现出概念间的相似度。基于此,提出一种基于加权语义距离的概念相似度计算方法。该方法搜索出两概念间的所有路径,以所有路径的加权平均距离代替最短距离来计算相似度,并综合考虑节点深度、公共父节点对相似度的影响。实验表明,该方法计算出的概念相似度能够更准确地体现出概念间的相似程度。     

语义相似度计算在语义标注中的应用

提出了一种词汇和本体概念间的语义相似度计算方法。该方法利用编辑距离和维基百科从语法和语义两方面综合考虑词汇和概念间的语义相似度。在领域本体的指导下,将方法应用于语义标注过程,建立词汇与本体概念之间的映射。在标注过程中建立知识库,提高算法性能,实验结果说明该方法是行之有效的。     

Cross-language transfer of semantic annotation via targeted crowdsourcing: task design and evaluation

Modern data-driven spoken language systems (SLS) require manual semantic annotation for training spoken language understanding parsers. Multilingual porting of SLS demands significant manual effort and language resources, as this manual annotation has to be replicated. Crowdsourcing is an accessible and cost-effective alternative to traditional methods of collecting and annotating data. The application of crowdsourcing to simple tasks has been well investigated. However, complex tasks, like cross-language semantic annotation transfer, may generate low judgment agreement and/or poor performance. The most serious issue in cross-language porting is the absence of reference annotations in the target language; thus, crowd quality control and the evaluation of the collected annotations is difficult. In this paper we investigate targeted crowdsourcing for semantic annotation transfer that delegates to crowds a complex task such as segmenting and labeling of concepts taken from a domain ontology; and evaluation using source language annotation. To test the applicability and effectiveness of the crowdsourced annotation transfer we have considered the case of close and distant language pairs: Italian–Spanish and Italian–Greek. The corpora annotated via crowdsourcing are evaluated against source and target language expert annotations. We demonstrate that the two evaluation references (source and target) highly correlate with each other; thus, drastically reduce the need for the target language reference annotations.     

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.     

基于本体技术构建的协同式网络课件编辑系统

领域本体是解决异构系统语义互操作的关键技术。我们提出了基于协同式课件编辑环境的领域本体模型，该模型的特点在于建立了领域知识概念与学习资源和课件文档之间的关联。该模型采用TRIPLE语言实现，它能够方便地与RDF进行转换，并具有较强的推理能力。另外，本文还提出了基于领域本体的“语义冲突消除模型”，该模型是解决课件协同编辑过程中语义冲突的基础。     

Ontology-based concept similarity in Formal Concept Analysis

Both domain ontologies and Formal Concept Analysis (FCA) aim at modeling concepts, although with different purposes. In the literature, a promising research area concerns the role of FCA in ontology engineering, in particular, in supporting the critical task of reusing independently developed domain ontologies. With this regard, the possibility of evaluating concept similarity is acquiring an increasing relevance, since it allows the identification of different concepts that are semantically close. In this paper, an ontology-based method for assessing similarity between FCA concepts is proposed. Such a method is intended to support the ontology engineer in difficult activities that are becoming fundamental in the development of the Semantic Web, such us ontology merging and ontology mapping and, in particular, it can be used in parallel to existing semi-automatic tools relying on FCA.     

一种基于本体的概念相似度计算及其应用

概念的语义相似度研究,是知识表示以及信息检索领域中的一个重要内容。本文提出了基于语义相似度和相关度的综合概念相似度计算方法,考虑了语义距离和本体库特征,加入概念的信息重合度、概念的深度、概念的密度和不对称因子的辅助影响。通过实验和两种传统的语义相似度计算方法进行对比,本方法能更好地区分本体树中不同关系的概念对,验证了该方法的有效性。     

改进的本体概念相似度计算模型

本体映射能很好地解决语义网中的本体异构性问题,其核心在于计算本体概念的相似度。针对现有的概念相似度计算的精度和查准率不高,提出一种改进的概念相似度计算模型。首先利用本体特征之间的偏序关系建立形式背景和概念格,然后在结构层次求出概念间的交不可约元集,并通过对集合里各元素的语义关系进行量化计算出概念间的相似度。实例和分析结果表明,改进的概念相似度计算模型在F-Score上有明显提高。     

概念与文档的语义相似度计算

将本体作为背景知识引入到概念之间相似度和文档之间相似度的计算中。通过图模型表示本体中概念以及概念之间的语义关系,用来将一个概念和一个文档扩展为一个语义模糊集,并计算模糊集合之间的相似度。文档相似度的计算是在概念相似度计算的基础之上。在概念相似度的计算过程中引入了语义相似度矩阵以及基于共信息理论的模糊相似度方法。     

Genetic algorithm for text clustering using ontology and evaluating the validity of various semantic similarity measures

This paper proposes a self-organized genetic algorithm for text clustering based on ontology method. The common problem in the fields of text clustering is that the document is represented as a bag of words, while the conceptual similarity is ignored. We take advantage of thesaurus-based and corpus-based ontology to overcome this problem. However, the traditional corpus-based method is rather difficult to tackle. A transformed latent semantic indexing (LSI) model which can appropriately capture the associated semantic similarity is proposed and demonstrated as corpus-based ontology in this article. To investigate how ontology methods could be used effectively in text clustering, two hybrid strategies using various similarity measures are implemented. Experiments results show that our method of genetic algorithm in conjunction with the ontology strategy, the combination of the transformed LSI-based measure with the thesaurus-based measure, apparently outperforms that with traditional similarity measures. Our clustering algorithm also efficiently enhances the performance in comparison with standard GA and k-means in the same similarity environments.     

基于蒙古语新闻领域本体的分布式检索方法

目前蒙古语语义Web方面的研究成果都是基于单机环境的,当语义Web信息检索系统投入实际运行时,单机环境存在存储容量有限和多用户并发查询速度慢等问题.针对此问题,提出了基于蒙古语新闻领域本体的分布式语义Web检索方法.首先依据蒙古语新闻领域的特点,参照七步法和骨架法,构建蒙古语新闻领域本体,研究适合本体的混合语义相似度算法进行语义扩展.然后将本体数据与算法部署于Hadoop分布式平台,解决了大规模本体数据存储的逻辑描述、物理结构和并行处理问题,实现了基于蒙古语新闻领域本体的分布式检索系统.实验结果表明,该方法有效地减少了查询关键词的响应时间,提高了新闻检索的查全率和查准率.     

Measuring semantic similarity between land-cover classes for spatial analysis: an ontology hierarchy exploration approach

Resolving semantic heterogeneity is one of the major research challenges involved in many fields of study, such as, natural language processing, search engine development, document clustering, geospatial information retrieval, knowledge discovery, etc. When semantic heterogeneity is often considered as an obstacle for realizing full interoperability among diverse datasets, proper quantification of semantic similarity is another challenge to measure the extent of association between two qualitative concepts. The proposed work addresses this issue for any geospatial application where spatial land-cover distribution is crucial to model. Most of the these applications such as: prediction, change detection, land-cover classification, etc. often require to examine the land-cover distribution of the terrain. This paper presents an ontology-based approach to measure semantic similarity between spatial land-cover classes. As land-cover distribution is a qualitative information of a terrain, it is challenging to measure their extent of similarity among each other pragmatically. Here, an ontology is considered as the concept hierarchy of different land-cover classes which is built using domain experts’ knowledge. This work can be considered as the spatial extension of our earlier work presented in [1]. The similarity metric proposed in [1] is utilized here for spatial concepts. A case study with real land-cover ontology is presented to quantify the semantic similarity between every pair of land-covers with semantic hierarchy based similarity measurement (SHSM) scheme [1]. This work may facilitate quantification of semantic knowledge of the terrain for other spatial analyses as well.     

一种基于语义词典的本体对齐框架

提出一种基于语义词典的本体对齐框架.首先抽取出代表本体元素的字符串,这些字符串包括本体中的概念、实例、关系等,并利用现有的词典和语义资源将字符串变为词的集合;然后将本体对齐转换为单词集合间的映射,通过多相似度的匹配算法来进行相似度计算,从而实现本体对齐.实验结果表明,所提出的方法是有效的且较之以前的对齐方法有一定的提高.