Content Tagging Research Articles

Research on Intelligent Knowledge Recommendation System for Police Applications

This paper proposed knowledge content tag recommendation algorithm in cloud computing Environment, and applied to police information knowledge. The algorithm analyzed user behavior history of operation and considered the similarity knowledge of the entries on the tag of police information, marked weight of tag in predicting when a user rating. On this basis, the police information implementations specific recommendations based on the specific user application knowledge. Meanwhile, combined the tag of system entry contents correlation with user correlation analysis, and solved the problems of system sparse matrix. Finally, the results demonstrated the effectiveness and superiority of recommendation algorithm.

Electronic Cleansing for 24-H Limited Bowel Preparation CT Colonography Using Principal Curvature Flow

CT colonography (CTC) is one of the recommended methods for colorectal cancer screening. The subject's preparation is one of the most burdensome aspects of CTC with a cathartic bowel preparation. Tagging of the bowel content with an oral contrast medium facilitates CTC with limited bowel preparation. Unfortunately, such preparations adversely affect the 3-D image quality. Thus far, data acquired after very limited bowel preparation were evaluated with a 2-D reading strategy only. Existing cleansing algorithms do not work sufficiently well to allow a primary 3-D reading strategy. We developed an electronic cleansing algorithm, aimed to realize optimal 3-D image quality for low-dose CTC with 24-h limited bowel preparation. The method employs a principal curvature flow algorithm to remove heterogeneities within poorly tagged fecal residue. In addition, a pattern recognition-based approach is used to prevent polyp-like protrusions on the colon surface from being removed by the method. Two experts independently evaluated 40 CTC cases by means of a primary 2-D approach without involvement of electronic cleansing as well as by a primary 3-D method after electronic cleansing. The data contained four variations of 24-h limited bowel preparation and was based on a low radiation dose scanning protocol. The sensitivity for lesions ≥ 6 mm was significantly higher for the primary 3-D reading strategy (84%) than for the primary 2-D reading strategy (68%) (p = 0.031). The reading time was increased from 5:39 min (2-D) to 7:09 min (3-D) (p = 0.005); the readers' confidence was reduced from 2.3 (2-D) to 2.1 (3-D) ( p = 0.013) on a three-point Likert scale. Polyp conspicuity for cleansed submerged lesions was similar to not submerged lesions (p = 0.06). To our knowledge, this study is the first to describe and clinically validate an electronic cleansing algorithm that facilitates low-dose CTC with 24-h limited bowel preparation.

An Introduction of a Tag Ratio Model and the Classification Examination for Recommender Systems

Tagging has emerged as a powerful mechanism that enables users to find and understand entities. However, there are three types of issues in traditional tagging systems. In this paper, we explore and seek a tag-algorithm that predicts tags of users and contents with a degree of relevance, which we called the tag ratio. We described our algorithm and evaluated them by the Naive Bayes classifier. Experiment results showed that all rating's precision of sought continuous number content's tag were better than raw binary valued content's tag's.

Building user experiences: Synchronizing user experience design and the supporting metadata and taxonomy infrastructure

AbstractEditor's SummaryFor best results in website architecture and operation, the process of user experience design and taxonomy and metadata development should be synchronized, not developed in isolation. Bringing the two work streams together enables full consideration of how users, represented as personas, would interact with the site, pursuing likely needs and preferences. The persona review informs the technical infrastructure and functionality as well as the terminology and content types. Analysis of the site design and comparison with similar sites can lead to better appreciation of the user experience and expanded vocabulary links to improve access to content. The Key Paths methodology used by the design firm Factor captures likely user journeys to define design elements, allowing for multiple routes to appropriate content. Metadata must be available to support design, and functional design and content tagging must take advantage of metadata. The dynamic interaction and mutual support of the site's content team and design team are keys to an effective product.

A fluorous Mukaiyama coupling reagent for a concise condensation reaction: utility of medium-fluorous strategy

The entire study of condensation reactions using various fluorous Mukaiyama reagents, including a novel medium-fluorous strategy, is described. A Mukaiyama reagent bearing a medium-fluorous content tag, between 40 and 60% fluorine by weight, was prepared and examined in ester and amide-forming condensation reactions. At the end of the reactions, the fluorous pyridone by-product was effectively separated from non-fluorous components by increasing the water content of the crude reaction mixture and subsequent filtration of the precipitate. It is also shown that Mukaiyama reagents bearing a fluorous tag increase the reaction rate considerably when compared to their non-fluorous tagged counterpart. Interestingly, it was observed that the longer the fluorous chain, the higher the activity of the Mukaiyama reagent.

In Tags We Trust: Trust modeling in social tagging of multimedia content

Tagging in online social networks is very popular these days, as it facilitates search and retrieval of multimedia content. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and content may be maliciously added for advertisement or self-promotion. This article surveys recent advances in techniques for combatting such noise and spam in social tagging. We classify the state-of-the-art approaches into a few categories and study representative examples in each. We also qualitatively compare and contrast them and outline open issues for future research.

Developing a content maintenance and governance strategy

The Missing Piece of Content Management Information and content governance is frequently a missing piece of a content management plan. Think of building the new content management system (CMS) as building a new house.You design the layout of the house and requirements based on the needs of your family. Then you move into the house. In preparation for the move, you put all of your possessions in boxes and mark those boxes to indicate where they will be going in the new home. If you are moving into a larger home, you’ll likely take items that were in a single room and put them into multiple rooms.

Riders Have Spoken: Replaying and Archiving Pervasive Performances

In this paper we present a practical study that formed part of the larger Creator project [1]. Data, such as GPS trails, video and audio files from a pervasive-performance were used to explore multi-disciplinary understandings of such ‘ephemeral’ pieces. Video and audio content tagging was also explored as a device to aid in developing the archive for replay. It is recommended that projects involving artistic practice should make documentation and subsequent archiving part of their overall research strategy.

Algorithm for Extracting Loosely Structured Data Records Through Digging Strict Patterns

Extracting loosely structured data records (LSDRs) has wide applications in many domains, such as forum pattern recognition, Weblogs data analysis, and books and news review analysis. Yet currently existing methods only work well for strongly structured data records (SDRs). In this paper, we propose to address the problem of extracting LSDRs through mining strict patterns. In our method, we utilize both content feature and tag tree feature to recognize the LSDRs, and propose a new algorithm to extract the Data Records (DRs) automatically. The experimental results demonstrate that our algorithm is able to effectively extract LSDRs with higher precision and recall.

When More is Better: A Counter-Narrative Regarding Keyword and Subject Retrieval in Digitized Diaries

Many commercial full-text databases and digital libraries provide keyword and preferredterm (subject) indexing, but few allow participatory tagging of content by users or provide ontologies in support of natural language information retrieval. Consequently, keyword and subject searching strategies still matter. But keyword searching, because it can yield results high in recall and low in precision, is often seen as a beginner's strategy best replaced by subject searching using authoritative headings and descriptors. In certain circumstance explored in this essay, keyword searching may be quite effective in and of itself for retrieving digitized primary sources for the study of history.

A case for automated large-scale semantic annotation

This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. To our knowledge, this is the largest scale semantic tagging effort to date. We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large-scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web.

A Case for Automated Large Scale Semantic Annotation

This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. To our knowledge, this is the largest scale semantic tagging effort to date. We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web.