基于标签推荐的Mashup服务聚类

聚类Web服务能大大提高W c6服务搜索引擎检索相关服务的能力。ProgrammablcWeb. com是一个很流行 的在线社会Mashup网站。作为基于Web的应用程序,Mashup本质上是开发者提供的Web服务。结合Mashup服 务的描述文档和相应标签提出一种新颖的Mashup服务聚类的方法,此外还提出一种标签推荐的方法来改进服务聚 类的性能。实验结果表明,基于标签推荐的服务聚类方法的聚类精度比其他两种实验方法要高,说明提出的标签推荐 策略有效扩充了标签数较少的Mashup服务,从而带来更多相关标签信息,因而聚类效果更好。     

A language model approach for tag recommendation

Tags are user-generated keywords for entities. Recently tags have been used as a popular way to allow users to contribute metadata to large corpora on the web. However, tagging style websites lack the function of guaranteeing the quality of tags for other usages, like collaboration/community, clustering, and search, etc. Thus, as a remedy function, automatic tag recommendation which recommends a set of candidate tags for user to choice while tagging a certain document has recently drawn many attentions. In this paper, we introduce the statistical language model theory into tag recommendation problem named as language model for tag recommendation (LMTR), by converting the tag recommendation problem into a ranking problem and then modeling the correlation between tag and document with the language model framework. Furthermore, we leverage two different methods based on both keywords extraction and keywords expansion to collect candidate tag before ranking with LMTR to improve the performance of LMTR. Experiments on large-scale tagging datasets of both scientific and web documents indicate that our proposals are capable of making tag recommendation efficiently and effectively.     

基于简介和评论的标签推荐方法研究

Web 2.0时代,社会标签是信息资源组织的一种重要方式。标签推荐能够有效的帮助用户收集、定位、查找和共享在线资源。以往的标签推荐算法只是基于一种文本信息,比如基于电影的简介文本来进行标签推荐。但是实际上电影往往存在多种文本信息,比如同时存在摘要信息和评论信息,不同类型的信息能够反映电影的不同方面的属性,因此为了提高电影标签推荐的准确率和有效性,我们同时根据电影的简介和短评进行电影标签自动推荐,并使用多种方法融合基于不同类型文本的标签推荐的结果,实验证明,使用不同类型信息进行标签推荐能够比单一使用一种文本信息进行标签推荐有很大的提升。
     

融合主题与语言模型的个性化标签推荐方法研究

随着Web的推广和普及,产生了越来越多的网络数据。 广泛应用了 标签系统 ,以便人们使用搜索技术来组织和使用这些信息。这些数据允许用户使用关键字(标签)注释资源,为传统的基于文本的信息检索提供了方案。为了支持用户选择正确的关键字,标签推荐算法应运而生。提出了一种个性化标签推荐方法,该方法综合了用户的资源标签与标签概率模型。该模型利用了简单语言模型和隐含狄利克雷分配模型,并针对现实世界的大型数据集进行了大量实验。实验表明,该个性化方法改进了标签推荐算法,推荐结果优于传统方法。     

基于MapReduce的Web标签SOINN聚类算法

Web标签有助于用户根据自己特定的兴趣完成信息资源的分类、组织和检索。然而,正是由于协同标记系统特有的公开性、自由化的特点,采用其对信息资源进行描述、组织、分类和检索,存在着信息描述不精确、标签组织混乱和标签语意模糊等问题。在此背景下提出了3种基于特征向量表示法(FVR)的Web标签SOINN聚类算法:基于资源的特征向量表示法、基于其他共现标签的特征向量表示法和基于全集共现标签的特征向量表示法。同时应用MapReduce框架将SOINN算法进行并行化。实验表明,当类中心数量超过2000时,3种分布式聚类FVR算法的召回率和准确度优于原始算法,可获得很好的加速比。从而证明此分布式聚类算法具有很好的可扩展性,可以用于更为海量的Web日志聚类分析系统。     

Effective Tag Recommendation System Based on Topic Ontology Using Wikipedia and WordNet

In this paper, we proposed a novel approach based on topic ontology for tag recommendation. The proposed approach intelligently generates tag suggestions to blogs. In this approach, we construct topic ontology through enriching the set of categories in existing small ontology called as Open Directory Project. To construct topic ontology, a set of topics and their associated semantic relationships is identified automatically from the corpus‐based external knowledge resources such as Wikipedia and WordNet. The construction relies on two folds such as concept acquisition and semantic relation extraction. In the first fold, a topic‐mapping algorithm is developed to acquire the concepts from the semantic of Wikipedia. A semantic similarity‐clustering algorithm is used to compute the semantic similarity measure to group the set of similar concepts. The second is the semantic relation extraction algorithm, which derives associated semantic relations between the set of extracted topics from the lexical patterns between synsets in WordNet. A suitable software prototype is created to implement the topic ontology construction process. A Jena API framework is used to organize the set of extracted semantic concepts and their corresponding relationship in the form of knowledgeable representation of Web ontology language. Thus, Protégé tool provides the platform to visualize the automatically constructed topic ontology successfully. Using the constructed topic ontology, we can generate and suggest the most suitable tags for the new resource to users. The applicability of topic ontology with a spreading activation algorithm supports efficient recommendation in practice that can recommend the most popular tags for a specific resource. The spreading activation algorithm can assign the interest scores to the existing extracted blog content and tags. The weight of the tags is computed based on the activation score determined from the similarity between the topics in constructed topic ontology and content of the existing blogs. High‐quality tags that has the highest activation score is recommended to the users. Finally, we conducted experimental evaluation of our tag recommendation approach using a large set of real‐world data sets. Our experimental results explore and compare the capabilities of our proposed topic ontology with the spreading activation tag recommendation approach with respect to the existing AutoTag mechanism. And also discuss about the improvement in precision and recall of recommended tags on the data sets of Delicious and BibSonomy. The experiment shows that tag recommendation using topic ontology results in the folksonomy enrichment. Thus, we report the results of an experiment mean to improve the performance of the tag recommendation approach and its quality.     

Personalized topic-based tag recommendation

More and more content on the Web is generated by users. To organize this information and make it accessible via current search technology, tagging systems have gained tremendous popularity. Especially for multimedia content they allow to annotate resources with keywords (tags) which opens the door for classic text-based information retrieval. To support the user in choosing the right keywords, tag recommendation algorithms have emerged. In this setting, not only the content is decisive for recommending relevant tags but also the user's preferences.In this paper we introduce an approach to personalized tag recommendation that combines a probabilistic model of tags from the resource with tags from the user. As models we investigate simple language models as well as Latent Dirichlet Allocation. Extensive experiments on a real world dataset crawled from a big tagging system show that personalization improves tag recommendation, and our approach significantly outperforms state-of-the-art approaches.     

基于潜在标签挖掘和细粒度偏好的个性化标签推荐

为进一步提高个性化标签推荐性能,针对标签数据的稀疏性以及传统方法忽略隐藏在用户和项目上下文中潜在标签的缺陷,提出一种基于潜在标签挖掘和细粒度偏好的个性化标签推荐方法。首先,提出利用用户和项目的上下文信息从大量未观测标签中挖掘用户可能感兴趣的少量潜在标签,将标签重新划分为正类标签、潜在标签和负类标签三类,进而构建〈用户,项目〉对标签的细粒度偏好关系,在缓解标签稀疏性的同时,提高对标签偏好关系的表达能力;然后,基于贝叶斯个性化排序优化框架对细粒度偏好关系进行建模,并结合成对交互张量分解对偏好值进行预测,构建细粒度的个性化标签推荐模型并提出优化算法。对比实验表明,提出的方法在保证较快收敛速度的前提下,有效地提高了个性化标签的推荐准确性。     

Web service API recommendation for automated mashup creation using multi-objective evolutionary search

Modern software development builds on external Web services reuse as a promising way that allows developers delivering feature-rich software by composing existing Web service Application Programming Interfaces, known as APIs. With the overwhelming number of Web services that are available on the Internet, finding the appropriate Web services for automatic service composition, i.e., mashup creation, has become a time-consuming, difficult, and error-prone task for software designers and developers when done manually. To help developers, a number of approaches and techniques have been proposed to automatically recommend Web services. However, they mostly focus on recommending individual services. Nevertheless, in practice, service APIs are intended to be used together forming a social network between different APIs, thus should be recommended collectively. In this paper, we introduce a novel automated approach, called SerFinder, to recommend service sets for automatic mashup creation. We formulate the service set recommendation as a multi-objective combinatorial problem and use the non-dominated sorting genetic algorithm (NSGA-II) as a search method to extract an optimal set of services to create a given mashup. We aim at guiding the search process towards generating the adequate compromise among three objectives to be optimized (i) maximize services historical co-usage, (ii) maximize services functional matching with the mashup requirements, and (iii) maximize services functional diversity. We perform a large-scale empirical experiment to evaluate SerFinder on a benchmark of real-world mashups and services. The obtained results demonstrate the effectiveness of SerFinder in comparison with recent existing approaches for mashup creation and services recommendation. The statistical analysis results provide an empirical evidence that SerFinder, significantly outperforms four state-of-the-art widely-used multi-objective search-based algorithms as well as random search.     

Augmented context-based recommendation service framework using knowledge over the Linked Open Data cloud

This research proposes ACARDS (Augmented-Context bAsed RecommenDation Service) framework that is able to utilize knowledge over the Linked Open Data (LOD) cloud to recommend context-based services to users. To improve the level of user satisfaction with the result of the recommendation, the ACARDS framework implements a novel recommendation algorithm that can utilize the knowledge over the LOD cloud. In addition, the noble algorithm is able to use new concepts like the enriched tags and the augmented tags that originate from the hashtags on the SNSs materials. These tags are utilized to recommend the most appropriate services in the user’s context, which can change dynamically. Last but not least, the ACARDS framework implements the context-based reshaping algorithm on the augmented tag cloud. In the reshaping process, the ACARDS framework can recommend the highly receptive services in the users’ context and their preferences. To evaluate the performance of the ACARDS framework, we conduct four kinds of experiments using the Instagram materials and the LOD cloud. As a result, we proved that the ACARDS framework contributes to increasing the query efficiency by reducing the search space and improving the user satisfaction on the recommended services.     

Semi-automatic dynamic auxiliary-tag-aided image annotation

Image annotation is the foundation for many real-world applications. In the age of Web 2.0, image search and browsing are largely based on the tags of images. In this paper, we formulate image annotation as a multi-label learning problem, and develop a semi-automatic image annotation system. The presented system chooses proper words from a vocabulary as tags for a given image, and refines the tags with the help of the user's feedback. The refinement amounts to a novel multi-label learning framework, named Semi-Automatic Dynamic Auxiliary-Tag-Aided (SADATA), in which the classification result for one certain tag (target tag) can be boosted by the classification results of a subset of the other tags (auxiliary tags). The auxiliary tags, which have strong correlations with the target tag, are determined in terms of the normalized mutual information. We only select those tags whose correlations exceed a threshold as the auxiliary tags, so the auxiliary set is sparse. How much an auxiliary tag can contribute is dependent on the image, so we also build a probabilistic model conditioned on the auxiliary tag and the input image to adjust the weight of the auxiliary tag dynamically. For an given image, the user feedback on the tags corrects the outputs of the auxiliary classifiers and SADATA will recommend more proper tags next round. SADATA is evaluated on a large collection of Corel images. The experimental results validate the effectiveness of our dynamic auxiliary-tag-aided method. Furthermore, the performance also benefits from user feedbacks such that the annotation procedure can be significantly speeded up.     

i-TagRanker: an efficient tag ranking system for image sharing and retrieval using the semantic relationships between tags

Folksonomy, considered a core component for Web 2.0 user-participation architecture, is a classification system made by user’s tags on the web resources. Recently, various approaches for image retrieval exploiting folksonomy have been proposed to improve the result of image search. However, the characteristics of the tags such as semantic ambiguity and non-controlledness limit the effectiveness of tags on image retrieval. Especially, tags associated with images in a random order do not provide any information about the relevance between a tag and an image. In this paper, we propose a novel image tag ranking system called i-TagRanker which exploits the semantic relationships between tags for re-ordering the tags according to the relevance with an image. The proposed system consists of two phases: 1) tag propagation phase, 2) tag ranking phase. In tag propagation phase, we first collect the most relevant tags from similar images, and then propagate them to an untagged image. In tag ranking phase, tags are ranked according to their semantic relevance to the image. From the experimental results on a Flickr photo collection about over 30,000 images, we show the effectiveness of the proposed system.     

基于领域的Web服务查找方法

在构建面向服务的Web应用时，往往需要按照领域进行Web服务查找。现有的UDDI的Web服务查找方式是通过tModel分类信息和关键字匹配来进行的，不便于按照领域进行查找。通过直接在互联网上搜索WSDL文件，并利用支持向量机来构建基于领域的WSDL文件分类器，实现了按照领域进行Web服务查找的方法。实验证明该方法具有较高的精确度。     

Tag recommendation by machine learning with textual and social features

Tags are very popular in social media (like Youtube, Flickr) and provide valuable and crucial information for social media. But at the same time, there exist a great number of noisy tags, which lead to many studies on tag suggestion and recommendation for items including websites, photos, books, movies, and so on. The textual features of tags, likes tag frequency, have mostly been used in extracting tags that are related to items. In this paper, we address the problem of tag recommendation for social media users. This issue is as important as the tag recommendation for items, because the tags representing users are strongly related to the users’ favorite topics. We propose several novel features of tags for machine learning that we call social features as well as textual features. The experimental results of Flickr show that our proposed scheme achieves viable performance on tag recommendation for users.     

Topic-based ranking in Folksonomy via probabilistic model

Social tagging is an increasingly popular way to describe and classify documents on the web. However, the quality of the tags varies considerably since the tags are authored freely. How to rate the tags becomes an important issue. Most social tagging systems order tags just according to the input sequence with little information about the importance and relevance. This limits the applications of tags such as information search, tag recommendation, and so on. In this paper, we pay attention to finding the authority score of tags in the whole tag space conditional on topics and put forward a topic-sensitive tag ranking (TSTR) approach to rank tags automatically according to their topic relevance. We first extract topics from folksonomy using a probabilistic model, and then construct a transition probability graph. Finally, we perform random walk over the topic level on the graph to get topic rank scores of tags. Experimental results show that the proposed tag ranking method is both effective and efficient. We also apply tag ranking into tag recommendation, which demonstrates that the proposed tag ranking approach really boosts the performances of social-tagging related applications.     

XML-manipulating test case prioritization for XML-manipulating services

A web service may evolve autonomously, making peer web services in the same service composition uncertain as to whether the evolved behaviors are compatible with its original collaborative agreement. Although peer services may wish to conduct regression testing to verify the agreed collaboration, the source code of the former service may be inaccessible to them. Owing to the black-box nature of peer services, traditional code-based approaches to regression testing are inapplicable. In addition, traditional techniques assume that a regression test suite for verifying a web service is available. The location to store a regression test suite is also a problem. On the other hand, we note that the rich interface specifications of a web service provide peer services with a means to formulate black-box testing strategies. In this paper, we provide a strategy for black-box service-oriented testing. We also formulate new test case prioritization strategies using tags embedded in XML messages to reorder regression test cases, and reveal how the test cases use the interface specifications of web services. We experimentally evaluate the effectiveness of these black-box strategies in revealing regression faults in modified WS-BPEL programs. The results show that the new techniques can have a high chance of outperforming random ordering. Moreover, our experiment shows that prioritizing test cases based on WSDL tag coverage can achieve a smaller variance than that based on the number of tags in XML messages in regression test cases, even though their overall fault detection rates are similar.     

Agent based intelligent search framework for product information using ontology mapping

The Semantic Web and Web services provide many opportunities in various applications such as product search and comparison in electronic commerce. We implemented an intelligent meta-search and recommendation system for products through consideration of multiple attributes by using ontology mapping and Web services. Under the assumption that each shopping site offers product ontology and product search service with Web services, we proposed a meta-search framework to configure a customer’s search intent, make and dispatch proper queries to each shopping site, evaluate search results from shopping sites, and show the customer the relevant product list with associated rankings. Ontology mapping is used for generating proper queries for shopping sites that have different product categories. We also implemented our framework and performed empirical evaluation of our approach with two leading shopping sites in the world.