Online annotation – Research and practices

Annotation can be a valuable exercise when trying to understand new information. The technique can be used to create a ‘condensed’ version of the original information for later review and to add additional information into the existing document. The growth in web-based learning materials and information sources has created requirement for systems that allow annotations to be attached to these new sources and, potentially, shared with other learners. This paper discusses annotation in an educational context and introduces some of the web annotation systems currently available. It also provides an overview of the development of a new system, eLAWS, by the authors, based upon the Web Service architecture. Finally, the paper provides suggestions for the future development of e-Learning Annotation tools.     

Open annotations on multimedia Web resources

Many Web portals allow users to associate additional information with existing multimedia resources such as images, audio, and video. However, these portals are usually closed systems and user-generated annotations are almost always kept locked up and remain inaccessible to the Web of Data. We believe that an important step to take is the integration of multimedia annotations and the Linked Data principles. We present the current state of the Open Annotation Model, explain our design rationale, and describe how the model can represent user annotations on multimedia Web resources. Applying this model in Web portals and devices, which support user annotations, should allow clients to easily publish and consume, thus exchange annotations on multimedia Web resources via common Web standards.     

Online annotation – Research and practices

Annotation can be a valuable exercise when trying to understand new information. The technique can be used to create a ‘condensed’ version of the original information for later review and to add additional information into the existing document. The growth in web-based learning materials and information sources has created requirement for systems that allow annotations to be attached to these new sources and, potentially, shared with other learners. This paper discusses annotation in an educational context and introduces some of the web annotation systems currently available. It also provides an overview of the development of a new system, eLAWS, by the authors, based upon the Web Service architecture. Finally, the paper provides suggestions for the future development of e-Learning Annotation tools.     

Exploring Social Annotations with the Application to Web Page Recommendation

Collaborative social annotation systems allow users to record and share their original keywords or tag attachments to Web resources such as Web pages, photos, or videos. These annotations are a method for organizing and labeling information. They have the potential to help users navigate the Web and locate the needed resources. However, since annotations are posted by users under no central control, there exist problems such as spam and synonymous annotations. To efficiently use annotation information to facilitate knowledge discovery from the Web, it is advantageous if we organize social annotations from semantic perspective and embed them into algorithms for knowledge discovery. This inspires the Web page recommendation with annotations, in which users and Web pages are clustered so that semantically similar items can be related. In this paper we propose four graphic models which cluster users, Web pages and annotations and recommend Web pages for given users by assigning items to the right cluster first. The algorithms are then compared to the classical collaborative filtering recommendation method on a real-world data set. Our result indicates that the graphic models provide better recommendation performance and are robust to fit for the real applications.     

A Web 2.0-based collaborative annotation system for enhancing knowledge sharing in collaborative learning environments

A limitation of current Web-based collaborative learning is the restricted ability of students to create and share individual annotations with annotated documents. Applying Web 2.0 collaborative annotation systems and analyzing students’ annotation behavior has attracted attention to improve collaborative learning. This study designed a personalized annotation management system 2.0 (PAMS 2.0) for managing, sharing, and reusing individual and collaborative annotations as well as providing a shared mechanism for discussion about shared annotations among multiple users.     

Enhancement of digital reading performance by using a novel web-based collaborative reading annotation system with two quality annotation filtering mechanisms

Collaboratively annotating digital texts allows learners to add valued information, share ideas, and create knowledge. However, excessive annotations and poor-quality annotations in a digital text may cause information overload and divert attention from the main content. The increased cognitive load ultimately reduces the effectiveness of collaborative annotations in promoting reading comprehension. Thus, this work develops a web-based collaborative reading annotation system (WCRAS-TQAFM) with two quality annotation filtering mechanisms—high-grade and master annotation filters—to promote the reading performance of learners. Ninety-seven students from three classes of a senior high school in Taiwan were invited to participate in an 80-min reading activity in which individual readers use WCRAS with or without annotation filters. Analytical results indicate that digital reading performance is significantly better in readers who use the high-grade annotation filter compared to those who read all annotations. Moreover, the high-grade annotation filter can enhance the reading comprehension of learners in all considered question types (i.e., recall, main idea, inference, and application). Also, the Cohen’s kappa statistics was used for assessing whether the annotation selected by the high-grade annotation filter is in agreement with the annotations selected by a domain expert. The statistic results indicate that the proposed high-grade annotation filter is valid to some degree. Finally, neither of the proposed quality annotation filtering approaches significantly reduces cognitive load.     

Scalable search-based image annotation

With the popularity of digital cameras, more and more people have accumulated considerable digital images on their personal devices. As a result, there are increasing needs to effectively search these personal images. Automatic image annotation may serve the goal, for the annotated keywords could facilitate the search processes. Although many image annotation methods have been proposed in recent years, their effectiveness on arbitrary personal images is constrained by their limited scalability, i.e. limited lexicon of small-scale training set. To be scalable, we propose a search-based image annotation algorithm that is analogous to information retrieval. First, content-based image retrieval technology is used to retrieve a set of visually similar images from a large-scale Web image set. Second, a text-based keyword search technique is used to obtain a ranked list of candidate annotations for each retrieved image. Third, a fusion algorithm is used to combine the ranked lists into a final candidate annotation list. Finally, the candidate annotations are re-ranked using Random Walk with Restarts and only the top ones are reserved as the final annotations. The application of both efficient search techniques and Web-scale image set guarantees the scalability of the proposed algorithm. Moreover, we provide an annotation rejection scheme to point out the images that our annotation system cannot handle well. Experimental results on U. Washington dataset show not only the effectiveness and efficiency of the proposed algorithm but also the advantage of image retrieval using annotation results over that using visual features.     

Dealing With Complex Linguistic Annotations Within a Language Processing Framework

In this paper we present AWA, a general purpose Annotation Web Architecture for representing, storing, and accessing the information produced by different linguistic processors. The objective of AWA is to establish a coherent and flexible representation scheme that will be the basis for the exchange and use of linguistic information. In morphologically-rich languages as Basque it is necessary to represent and provide easy access to complex phenomena such as intraword structure, declension, derivation and composition features, constituent discontinousness (in multiword expressions) and so on. AWA provides a well-suited schema to deal with these phenomena. The annotation model relies on XML technologies for data representation, storage and retrieval. Typed feature structures are used as a representation schema for linguistic analyses. A consistent underlying data model, which captures the structure and relations contained in the information to be manipulated, has been identified and implemented. AWA is integrated into LPAF, a multilayered Language Processing and Annotation Framework, whose goal is the management and integration of diverse NLP components and resources. Moreover, we introduce EULIA, an annotation tool which exploits and manipulates the data created by the linguistic processors. Two real corpora have been processed and annotated within this framework.      

扩展Java元模型以实现诠释的可视化建模

诠释（annotation）是JavaSE5引入的一种新的编程语言成分，目前得到广泛关注。但从可视化建模来看，已有的Java元模型和UML2元模型并不支持诠释规范，这导致诠释的语义特征在高层抽象中难以体现，也不能实现诠释的可视化、规范化建模。本文扩展已有的Java元模型，以支持诠释的可视化建模。文章提出了诠释的4个重要特征，添加了3个元类以扩展Java元模型，使其能反映诠释特征，并给出2个图符以支持诠释的可视化建模。新的Java元模型符合MOF规范，设计简单，方便实现以支持MDA及相关工具的开发。     

Effective Annotations Over 3D Models

Annotation support in interactive systems is often considered a simple task by the CG community, since it entails the apparently easy selection of a region and its connection with some information. The reality appears more complex. The scope of this paper is two-fold: first, to review the status of this domain, discussing and characterizing several approaches proposed in literature to manage annotations over geometric models; second, to present in detail an innovative solution proposed and assessed in the framework of Cultural Heritage (CH) applications, called ClippingVolumes. At the annotation definition stage ClippingVolumes uses 3D data to characterize the annotation region; subsequently, annotations are visualized by adopting a two-pass rendering solution which uses stencil buffers, thus without introducing new geometric elements, changing the topology or duplicating geometry elements. It solves most of the issues that afflict the current state of the art, such as fragmentation, annotation transfer to multiple representations and multi-resolution data encoding. The latter is a mandatory requirement to produce efficient web-based systems. We implemented and we fully tested this approach in the framework of a complex system that supports the documentation of CH restoration projects.     

中文网页语义标注:由句子到RDF表示

语义网远景的实现需要自动化的语义标注方法,提出了一种在领域本体指导下,针对中文网页的语义标注方法,运用统计学方法与自然语言处理技术,以文档中句子为处理对象,采取识别和组合两个阶段来完成句子向RDF表示的映射,它具有以下特点:以统计方法获得领域相关词汇,构造领域词汇标注列表作为外部领域知识,降低对通用语言本体的依赖;显式的属性类型标注方法识别出句子中表达关系的词汇,标注为属性类型,利于后续关系抽取;构造句子的句法依存关系树(森林),按照依存关系对词汇进行组合,形成RDF陈述.实验结果显示此方法较基于主谓宾语法关系的语义标注方法更为有效.     

Adaptive optimized residual convolutional image annotation model with bionic feature selection model

The advancements in Internet and Web 2.0 technologies resulted in the easier generation and distribution of digital images. Many images are stored without any form of significant labelling. This makes searching for desired images tedious and time-consuming, resulting in an increased demand for an effective image retrieval technique. Image annotation provides one or more labels to an image that describes its content. These annotations greatly help to retrieve images from large databases using different related queries. Automatic Image Annotation (AIA) applications in computer vision, data mining and web search are increasing progressively. This work proposes a hybrid Deep Learning (DL) based optimized image annotation model for AIA. Initially, the pre-processing of an input image is carried out to remove the image noise using the triple Gaussian guided filter. In the second stage, the image's features like shape, colour and texture are extracted using Slantlet transform, YCrCb Colour space and Local Binary Pattern (LBP). The feature dimensionality is reduced by selecting features based on the adaptive red colobuses monkey bionic model. Finally, the adaptive image annotation is obtained using the deep-optimized convolutional residual image annotation model. The loss function reduction and updating of weight parameters are facilitated using the hunger game bionic model. The experiments are performed in Python using Corel-5 K, ESP-Game and MIR FLICKR datasets. The proposed model attains an overall precision of 56, 99.2 and 90%, respectively. Thus, the experimental evaluation results demonstrate the excellent performance of a proposed model over existing approaches.     

From paper-and-pen annotations to artefact-based mobile learning

Making annotations on books and learning materials is part of students' everyday life. Although there are many computer-based annotation systems, many people prefer to print digital documents and make annotations on physical paper. We introduce a classification scheme for computer-based annotation systems and provide an overview of different systems using this scheme. During our investigations, we identified several shortcomings in existing annotation systems: limitation to single document formats, no capturing of relations to reflect lateral reading, loss of context information an annotation was created in and limited sharing capabilities among collaborative users. To overcome these shortcomings, we introduce the concepts of Anchor Points , Artefacts and Spaces . Anchor Points define locations in arbitrary documents. Artefacts capture annotations on multiple Anchor Points in several documents, allowing students to map their current working context that typically contains several open documents and a relationship between them. Spaces are repositories for the learning materials and annotations that are automatically replicated among collaborative mobile users.     

Ontology-based mammography annotation and Case-based Retrieval of breast masses

This paper describes ontology-based annotation of mammography and a Case-based Retrieval approach for breast masses from digital mammography archive. We first present our Mammography Annotation Ontology focusing on its main concepts and relationships, as well as the annotation tool. Then, we propose a model for similarity calculation between breast masses based on their high, mid and low-level features. We use Semantic Query-enhanced Web Rule Language (SQWRL) to process retrieval of similar masses from annotated mammography collection in OWL. We give both retrieving process and results we obtained from experimentations, in detail.     

Annotations and the collaborative digital library: Effects of an aligned annotation interface on student argumentation and reading strategies

Recent research on annotation interfaces provides provocative evidence that anchored, annotation-based discussion environments may lead to better conversations about a text. However, annotation interfaces raise complicated tradeoffs regarding screen real estate and positioning. It is argued that solving this screen real estate problem requires limiting the number of annotations displayed to users. In order to understand which annotations have the most learning value for students, this paper presents two complementary studies examining the effects of annotations on students performing a reading-to-write task. The first study used think-aloud protocols and a within-subjects methodology, finding that annotations appeared to provoke students to reflect more critically upon the primary text. This effect was particularly strong when students encountered pairs of annotations presenting different viewpoints on the same section of text. Student interviews suggested that annotations were most helpful when they caused the reader to consider and weigh conflicting viewpoints. The second study used a between-subjects methodology and a more naturalistic task to provide complementary evidence that annotations encourage more reflective responses to a text. This study found that students who received annotated materials both perceived themselves and were perceived by instructors as less reliant on unreflective summary strategies than students who received the same content but in a different format. These findings indicate that the learning value of an annotation lies in its ability to provoke students to consider and weigh new perspectives on the primary text. When selected effectively, annotations provide a critical scaffolding that can support students’ critical thinking and argumentation activities. Collaborative digital libraries and applications for the Web 2.0 should be designed with this learning framework in mind.     

The Linguistic Annotation Framework: a standard for annotation interchange and merging

This paper overviews the International Standards Organization–Linguistic Annotation Framework (ISO–LAF) developed in ISO TC37 SC4. We describe the XML serialization of ISO–LAF, the Graph Annotation Format (GrAF) and discuss the rationale behind the various decisions that were made in determining the standard. We describe the structure of the GrAF headers in detail and provide multiple examples of GrAF representation for text and multi-media. Finally, we discuss the next steps for standardization of interchange formats for linguistic annotations.