Set Of Tags Research Articles

Priming Effects and Strategic Influences in Social Tagging

In many social tagging systems, users can see the tags already created by others. Prior research has shown that this exposure leads users to create tags that are semantically related to the previous ones. We investigate two possible mechanisms through which this occurs, semantic priming and strategic choice. Semantic priming occurs when an existing tag subconsciously primes the user’s mind to suggest semantically related tags. In an experiment, no such effect is found, in contrast to prior research. A follow-up study shows that whether semantic priming occurs depends on whether the person uses others’ previously created tags or is just passively exposed to them. The second type of influence, strategic choice, occurs in ESP-type settings. It refers to behavior in which a user chooses words that are semantically related to an existing tag in order to increase the chances of matching one’s partner. Experimental results provide clear evidence of this strategic influence. In a follow-up study, we demonstrate that there is a meaningful difference in the tag sets that are created under the influence of strategic choice. Our work sheds light on the conditions and mechanisms through which existing tags influence subsequent tagging behavior.

Development of image collection representations for intelligent distributed systems

SummaryNumerous social networks, such as Flickr, Picasa, Instagram, and even news agencies, encourage their users to upload content in the form of annotated images. These image collections have become extremely large, containing hundreds of millions of images from millions of users. One consequence is that retrieval of a full set of thematic images, such as ancient ruins in Italy or mountain climbing in Peru, requires multiple collection searches. This is very time‐consuming for the user, particularly because these collections have not been developed for distributed system search and typically have no externally available collection description. In this paper, we present a system that can automatically generate an image collection description suitable for distributed search. Our approach enhances the image tag sets collected by the host system for development of a collection description that provides an extended vocabulary to match search query terms. The benefit of using collection descriptions in the image retrieval process is the ability to first select collections that are relevant to the query before retrieval of relevant images from those collections. This two‐step process improves query processing efficiency, because irrelevant collections need not be searched. Copyright © 2015 John Wiley & Sons, Ltd.

Construction and evaluation of ontological tag trees

Several expert systems have been proposed to address the sparsity of tags associated with online content such as images and videos. However most of such systems either necessitate extracting domain-specific features, or are solely based on tag semantics, or have significant space requirements to store corpus based tag statistics. To address these shortcomings, in this work we show how ontological tag trees can be used to encode information present in a given corpus pertaining to interaction between the tags, in a space efficient manner. An ontological tag tree is defined as an undirected, weighted tree on the set of tags where each possible tag is treated as a node in the tree. We formulate the tag tree construction as an optimization problem over the space of trees on the set of tags and propose a novel local search based approach utilizing the co-occurrence statistics of the tags in the corpus. To make the proposed optimization more efficient, we initialize using the semantic relationships between the tags. The proposed approach is used to construct tag trees over tags for two large corpora of images, one from Flickr and one from a set of stock images. Extensive data-driven evaluations demonstrate that the constructed tag trees can outperform previous approaches in terms of accuracy in predicting unseen tags using a partially observed set of tags, as well as in efficiency of predicting all applicable tags for a resource.

A Novel Hybrid Anticollision Algorithm for RFID System Based on Grouped Dynamic Framed Recognition and Binary Tree Recursive Process

Recently, RFID technology has come into end-user applications for monitoring, tracking, and so forth. In RFID system, a reader identifies a set of tags over a shared wireless channel. When multiple tags communicate with the same reader simultaneously, all packages will be lost and no tag can be recognized, which is known as tag collision. Tag collision is a significantly important issue for fast tag identification. We firstly make a thorough analysis among a variety of traditional RFID anticollision algorithms. Then a novel hybrid anticollision algorithm called T-GDFSA is proposed. Tags are assigned to different groups based on the initial tag estimation and then experience several dynamic read frames for identification. When a collision occurs in current slot, a tree-based recursive process will be deployed immediately. T-GDFSA combines the advantages of ALOHA-based and tree-based together and acquires higher system throughput by reducing unnecessary idle and collision slots and lower communication complexity by decreasing the data transmitted, which makes it identify tags faster with less power consumption. Simulations show that the theoretical values match well the simulation results. Moreover, T-GDFSA also has a good tolerance for the inaccuracy of initial tag estimation and the length variation of tag's ID.

A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Personalized microblog recommendations face challenges of user cold-start problems and the interest evolution of topics. In this paper, we propose a collaborative filtering recommendation algorithm based on a temporal interest evolution model and social tag prediction. Three matrices are first prepared to model the relationship between users, tags, and microblogs. Then the scores of the tags for each microblog are optimized according to the interest evolution model of tags. In addition, to address the user cold-start problem, a social tag prediction algorithm based on community discovery and maximum tag voting is designed to extract candidate tags for users. Finally, the joint probability of a tag for each user is calculated by integrating the Bayes probability on the set of candidate tags, and the top n microblogs with the highest joint probabilities are recommended to the user. Experiments using datasets from the microblog of Sina Weibo showed that our algorithm achieved good recall and precision in terms of both overall and temporal performances. A questionnaire survey proved user satisfaction with recommendation results when the cold-start problem occurred.

Using RNA Sequencing and In Silico Subtraction to Identify Resistance Gene Analog Markers for Lr16 in Wheat.

Leaf rust, caused by Puccinia triticina Eriks., is one of the most widespread diseases of wheat and breeding for resistance is one of the most effective methods of control. Lr16 is a wheat leaf rust resistance gene (R-gene) that provides resistance at both the seedling and adult stages. Simple-sequence repeat (SSR) markers have been used to map Lr16 to the distal end of chromosome 2B. The objectives of this study were to use RNA sequencing (RNA-seq) and in silico subtraction to identify new R-gene analogs (RGAs) and use them as Lr16 markers. RNA was isolated from the susceptible wheat cultivar Thatcher (Tc) and the resistant Tc isolines TcLr10, TcLr16, TcLr21, and sequenced using Illumina technology. Using in silico subtraction, sequences from the resistant Tc isolines were aligned to a Tc reference expressed sequence tag (EST) set. Sequences not aligning to the Tc reference were assembled into contigs and analyzed using BLASTx to determine putative gene functions. Primer pairs were designed for 181 RGA sequences, of which, 137 amplified in at least one of the parents. A mapping population was developed with 165 F2 lines from a cross between the rust-susceptible cultivar Chinese Spring (CS) and TcLr16. Two RGA markers XTaLr16_RGA266585 and XTaLr16_RGA22128 were identified that mapped proximally 1.2 and 23.8 cM from Lr16, respectively. Three SSR markers Xwmc764, Xwmc661, and Xbarc35 mapped between these two RGA markers at distances of 5.0, 10.9, and 16.1 cM from Lr16, respectively. In silico subtraction is an effective technique for isolating RGAs linked to R-genes of interest.

Completing tags by local learning: a novel image tag completion method based on neighborhood tag vector predictor

In this paper, we study the problem of tag completion. Given an image and a set of tags, only a few of the tags are known to be associated with this image or not, and the problem is to predict whether the other tags are associated with the image. To solve this problem, we propose to learn a tag scoring vector for each image and use it to predict the associated tags of the image. To learn the tag scoring vector, we use the method of local linear learning. A local linear function is used in the neighborhood of each image to predict the tag scoring vectors of its neighboring images. We construct a unified objective function for the learning of both tag scoring vectors and local linear function parameters. In this objective, we impose the learned tag scoring vectors to be consistent with the known associations to the tags of each image and also minimize the prediction error of each local linear function, while reducing the complexity of each local function. The objective function is optimized by an alternate optimization strategy and gradient descent methods in an iterative algorithm. We compare the proposed algorithm against different state-of-the-art tag completion methods, and the results show its advantages.

블로그 포스팅을 이용한 방송 콘텐츠 영상의 타임라인 단위 태그 클라우드 생성

Due to the recent increasement of user created contents like SNS, blog posts, and so on, broadcast contents are actively re-construction by its users. Especially, on some genres like drama, movie, various information from cars and film sites to clothes and watches in a content is spreaded out to other users through blog postings. Since such information can be an additional information for the content, they can be used for providing high-quality broadcast services. For this purpose, in this paper, we propose timeline tag cloud generation method for broadcasting contents. In the proposed method, blog postings on the target contents are first gathered and then, images and words around images are extracted from a blog post as a tag set. An extracted tag set is tagged on a specific timeline of the target content. In experiments, to prove the efficiency of the proposed method, we evaluated the performances of the proposed image matching and tag cloud generation methods.

The development and validation of a one‐bit comparison for evaluating the maturity of tag distributions in a Web 2.0 environment

Tags generated by domain experts reaching a consensus under social influence reflect the core concepts of the tagged resource. Such tags can act as navigational cues that enable users to discover meaningful and relevant information in a Web 2.0 environment. This is particularly critical for nonexperts for understanding formal academic or scientific resources, also known as hard content. The goal of this study was to develop a novel one‐bit comparison (OBC) metric and to assess in what circumstances a set of tags describing a hard‐content resource is mature and representative. We compared OBC with the conventional Shannon entropy approach to determine performance when distinguishing tags generated by domain experts and nonexperts in the early and later stages under social influence. The results indicated that OBC can accurately distinguish mature tags generated by a strong expert consensus from other tags, and outperform Shannon entropy. The findings support tag‐based learning, and provide insights and tools for the design of applications involving tags, such as tag recommendation and tag‐based organization.

On cold start for associative tag recommendation

Tag recommendation strategies that exploit term co‐occurrence patterns with tags previously assigned to the target object have consistently produced state‐of‐the‐art results. However, such techniques work only for objects with previously assigned tags. Here we focus on tag recommendation for objects with no tags, a variation of the well‐known \textit{cold start} problem. We start by evaluating state‐of‐the‐art co‐occurrence based methods in cold start. Our results show that the effectiveness of these methods suffers in this situation. Moreover, we show that employing various automatic filtering strategies to generate an initial tag set that enables the use of co‐occurrence patterns produces only marginal improvements. We then propose a new approach that exploits both positive and negative user feedback to iteratively select input tags along with a genetic programming strategy to learn the recommendation function. Our experimental results indicate that extending the methods to include user relevance feedback leads to gains in precision of up to 58% over the best baseline in cold start scenarios and gains of up to 43% over the best baseline in objects that contain some initial tags (i.e., no cold start). We also show that our best relevance‐feedback‐driven strategy performs well even in scenarios that lack user cooperation (i.e., users may refuse to provide feedback) and user reliability (i.e., users may provide the wrong feedback).

Improved lower bounds of DNA tags based on a modified genetic algorithm.

The well-known massively parallel sequencing method is efficient and it can obtain sequence data from multiple individual samples. In order to ensure that sequencing, replication, and oligonucleotide synthesis errors do not result in tags (or barcodes) that are unrecoverable or confused, the tag sequences should be abundant and sufficiently different. Recently, many design methods have been proposed for correcting errors in data using error-correcting codes. The existing tag sets contain small tag sequences, so we used a modified genetic algorithm to improve the lower bound of the tag sets in this study. Compared with previous research, our algorithm is effective for designing sets of DNA tags. Moreover, the GC content determined by existing methods includes an imprecise range. Thus, we improved the GC content determination method to obtain tag sets that control the GC content in a more precise range. Finally, previous studies have only considered perfect self-complementarity. Thus, we considered the crossover between different tags and introduced an improved constraint into the design of tag sets.

Enhancing of chemical compound and drug name recognition using representative tag scheme and fine-grained tokenization.

BackgroundThe functions of chemical compounds and drugs that affect biological processes and their particular effect on the onset and treatment of diseases have attracted increasing interest with the advancement of research in the life sciences. To extract knowledge from the extensive literatures on such compounds and drugs, the organizers of BioCreative IV administered the CHEMical Compound and Drug Named Entity Recognition (CHEMDNER) task to establish a standard dataset for evaluating state-of-the-art chemical entity recognition methods.MethodsThis study introduces the approach of our CHEMDNER system. Instead of emphasizing the development of novel feature sets for machine learning, this study investigates the effect of various tag schemes on the recognition of the names of chemicals and drugs by using conditional random fields. Experiments were conducted using combinations of different tokenization strategies and tag schemes to investigate the effects of tag set selection and tokenization method on the CHEMDNER task.ResultsThis study presents the performance of CHEMDNER of three more representative tag schemes-IOBE, IOBES, and IOB12E-when applied to a widely utilized IOB tag set and combined with the coarse-/fine-grained tokenization methods. The experimental results thus reveal that the fine-grained tokenization strategy performance best in terms of precision, recall and F-scores when the IOBES tag set was utilized. The IOBES model with fine-grained tokenization yielded the best-F-scores in the six chemical entity categories other than the "Multiple" entity category. Nonetheless, no significant improvement was observed when a more representative tag schemes was used with the coarse or fine-grained tokenization rules. The best F-scores that were achieved using the developed system on the test dataset of the CHEMDNER task were 0.833 and 0.815 for the chemical documents indexing and the chemical entity mention recognition tasks, respectively.ConclusionsThe results herein highlight the importance of tag set selection and the use of different tokenization strategies. Fine-grained tokenization combined with the tag set IOBES most effectively recognizes chemical and drug names. To the best of the authors' knowledge, this investigation is the first comprehensive investigation use of various tag set schemes combined with different tokenization strategies for the recognition of chemical entities.

Exploiting geotagged resources to spatial ranking by extending HITS algorithm

With a large amount of geotagged resources from smart devices, it is important to provide users with intelligent location-based services. Particularly, in this work, we focus on spatial ranking service, which can retrieve a set of relevant resources with a certain tag. This paper designs ranking algorithm in order to find out a list of locations which are collected from geotagged resources on SNSs. As extending HITS algorithm [13], we propose a novel method (called GeoHITS) that can analyze an undirected 2-mode graph composed with a set of tags and a set of locations. Thereby, meaningful relationships between the locations and a set of tags are discovered by integrating several weighting schemes and HITS algorithm. To evaluate the proposed spatial ranking approach, we have shows the experimental results from the recommendation applications. <br><br><font color="red"><b> This article has been corrected. Link to the correction <u><a href="http://dx.doi.org/10.2298/CSIS151203064E">10.2298/CSIS151203064E</a><u></b></font>

CREATION OF A DOMAIN MODEL BY PROBING GOOGLE SCHOLAR CITATIONS

The algorithm of constructing terminological networks – domain models based on sensing informational networks is proposed. A network of the concepts relevant to tags of the Google Scholar Citations service is considered to be a terminological network. Nodes in this network correspond to the concepts marked by tags. Edges correspond to some semantic link between them determined by adjacent interests of certain authors. The special algorithm of scanning the resources of the Google Scholar Citations service for receiving a representative set of tags as domain models bases is given. A relevant list of publications is automatically formed based on this network. Rules of creation of the list of bibliographic links are provided. The offered approach can be applied, in particular, to bibliographic databases in which authors and as tags – keywords are allocated in an explicit form. Proposed approach can be applied to many areas of science.

The DTA “Base Format”: A TEI Subset for the Compilation of a Large Reference Corpus of Printed Text from Multiple Sources

In this article we describe the DTA “Base Format” (DTABf), a strict subset of the TEI P5 tag set. The purpose of the DTABf is to provide a balance between expressiveness and precision as well as an interoperable annotation scheme for a large variety of text types of historical corpora of printed text from multiple sources. The DTABf has been developed on the basis of a large amount of historical text data in the core corpus of the project Deutsches Textarchiv (DTA) and text collections from 15 cooperating projects with a current total of 210 million tokens. The DTABf is a “living” TEI format which is continuously adjusted when new text candidates for the DTA containing new structural phenomena are encountered. We also focus on other aspects of the DTABf including consistency, interoperability with other TEI dialects, HTML and other presentations of the TEI texts, and conversion into other formats, as well as linguistic analysis. We include some examples of best practices to illustrate how external corpora can be losslessly converted into the DTABf, thus enabling third parties to use the DTABf in their specific projects. The DTABf is comprehensively documented, and several software tools are available for working with it, making it a widely used format for the encoding of historical printed German text.

Social image tagging with diverse semantics.

We have witnessed the popularity of image-sharing websites for sharing personal experiences through photos on the Web. These websites allow users describing the content of their uploaded images with a set of tags. Those user-annotated tags are often noisy and biased. Social image tagging aims at removing noisy tags and suggests new relevant tags. However, most existing tag enrichment approaches predominantly focus on tag relevance and overlook tag diversity problem. How to make the top-ranked tags covering a wide range of semantic is still an opening, yet challenging, issue. In this paper, we propose an approach to retag social images with diverse semantics. Both the relevance of a tag to image as well as its semantic compensations to the already determined tags are fused to determine the final tag list for a given image. Different from existing image tagging approaches, the top-ranked tags are not only highly relevant to the image but also have significant semantic compensations with each other. Experiments show the effectiveness of the proposed approach.

Open, web-based internationalization and localization tools

As many software applications have moved from a desktop software deployment model to a Software-as-a-Service (SaaS) model so we have seen tool vendors in the language service industry move to a SaaS model, e.g., for web-based Computer Assisted Translation (CAT) tools. However, many of these offerings fail to take full advantage of the Open Web Platform, i.e., the rich set of web browser-based APIs linked to HTML5. We examine the interoperability landscape that developers of web-based translation tools can benefit from, and in particular the potential offered by the open metadata defined in the W3C’s (World Wide Web Consortium) recent Internationalization Tag Set v2.0 Recommendation. We examine how this can be used in conjunction with the XML Localisation Interchange File Format (XLIFF) standardized by OASIS to exchange translation jobs between servers and Javascript-based CAT tools running in the web browser. We also explore how such open metadata can support activities in the multilingual web processing chain before and after translation.

Evaluating Digital Scholarship: Suggestions and Strategies for the Text Encoding Initiative

As part of a larger pilot study on the evaluation of digital scholarship, we consider what role, if any, the TEI Consortium and user community might play in evaluating scholarship that utilize the TEI tag set. Our rationale for focusing on the role of the TEI Consortium in the discussion of evaluation is twofold. First, the TEI Guidelines represents an encoding standard for texts that is supported by a large community actively interested in the application and development of these standards. Second, feedback concerning evaluation criteria for digital scholarship has not been explicitly gathered from the TEI community and may provide additional understanding of the value, process, and assessment of text encoding. Determining what to evaluate and how to do so reveals the community’s definitions of scholarship in general. The clarification and articulation of evaluation criteria, therefore, remains a high priority as digital scholarship continues to develop.

High-Performance Annotation Tagging over Solr Full-text Indexes

<p class="p1">In this work, we focus on the problem of “annotation tagging” over Information <span style="font-size: 10px;">Spaces of objects stored in a full-text index. In such a scenario, tags are </span><span style="font-size: 10px;">assigned to objects by “data curator” users with the purpose of classification, while </span><span style="font-size: 10px;">generic end-users will perceive tags as searchable and browsable object properties. </span><span style="font-size: 10px;">To carry out their activities, data curators need “annotation tagging tools” which </span><span style="font-size: 10px;">allow them to “bulk” tag or untag large sets of objects in temporary work sessions, </span><span style="font-size: 10px;">where they can “virtually” and in “real-time” experiment the effect of their actions </span><span style="font-size: 10px;">before making the changes visible to end-users. The implementation of these tools </span><span style="font-size: 10px;">over full-text indexes is a challenge, since bulk object updates in this context are </span><span style="font-size: 10px;">far from being real-time and in critical cases may slow down index performance. </span><span style="font-size: 10px;">We devised TagTick, a tool which offers to data curators a fully functional annotation </span><span style="font-size: 10px;">tagging environment over the full-text index Apache Solr, regarded as a </span><span style="font-size: 10px;">“de-facto standard” in this area. TagTick consists of a TagTick Virtualizer module, </span><span style="font-size: 10px;">which extends the APIs of Solr to support real-time, virtual, bulk-tagging operations, </span><span style="font-size: 10px;">and a TagTick User Interface module, which offers end-user functionalities </span><span style="font-size: 10px;">for annotation tagging. The tool scales optimally with the number and size of bulk </span><span style="font-size: 10px;">tag operations, without compromising index performance.</span></p>

Infinite Relational Model 기반 Co-Clustering을 이용한 영화 추천

사람의 영화에 대한 선호도에는 개인의 특성과 영화의 속성을 기반으로 하는 다양한 요인이 연관되어 있다. 영화 추천을 위한 사용자-영화-선호도 연관 관계의 분석 기법으로서, 다중 개념 탐색 기법의 특성을 지닌 infinite relational model (IRM)의 활용 가능성을 확인하고, 이를 기초로 영화 선호 유형에 따른 사용자-영화 군집을 탐색한다. 별점으로 표현되는 명시적인 선호도 데이터에 영화 컨텐츠 관련 메타데이터를 추가하여 학습 데이터를 구성하고, 이에 IRM을 적용하여 공군집화(co-clustering)를 수행한 결과, 해석 가능한 다양한 명시적 연관 관계를 발견하였다. 공군집화 결과를 기초로 개인화 추천에서의 다양한 활용 방안을 논의한다. Preferences of users on movies are observables of various factors that are related with user attributes and movie features. For movie recommendation, analysis methods for relation among users, movies, and preference patterns are mandatory. As a relational analysis tool, we focus on the Infinite Relational Model (IRM) which was introduced as a tool for multiple concept search. We show that IRM-based co-clustering on preference patterns and movie descriptors can be used as the first tool for movie recommender methods, especially content-based filtering approaches. By introducing a set of well-defined tag sets for movies and doing three-way co-clustering on a movie-rating matrix and a movie-tag matrix, we discovered various explainable relations among users and movies. We suggest various usages of IRM-based co-clustering, espcially, for incremental and dynamic recommender systems.