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Journal articles on the topic 'Web-based annotation'

Author: Grafiati

Published: 25 May 2024

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1

Rau, Pei-Luen Patrick, and Sho-Hsen Chen. "A Study of Electronic Annotation on Web Documents." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 5 (September 2002): 680–84. http://dx.doi.org/10.1177/154193120204600517.

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This study develops an electronic annotation system, allowing users to annotate on hypertexts, to build up knowledge structure, and to browse instructions provided by the system administrator or the instructor electronically. The electronic annotation system is a distributed World Wide Web application based on HTTP access and allows annotations on HTML documents. The major functions of the electronic annotation system include highlighting texts, inserting and editing annotations, and organizing and presenting annotations hierarchically. The five interactive components of the electronic annotation system are Main Tool Bar, Hypertext, Annotation Editor, Hierarchy Viewer, and Instruction Viewer. A user test was conducted to investigate the effect of the location of electronic annotating (Annotation Editor) on reading performance in terms of recall and degree of satisfaction.

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Hori, Masahiro, Goh Kondoh, Kouichi Ono, Shin-ichi Hirose, and Sandeep Singhal. "Annotation-based Web content transcoding." Computer Networks 33, no. 1-6 (June 2000): 197–211. http://dx.doi.org/10.1016/s1389-1286(00)00068-2.

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Jindia, Anjali, and Sonal Chawla. "ANNOTATIONS IN E-LEARNING." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 9 (February 18, 2014): 3852–59. http://dx.doi.org/10.24297/ijct.v12i9.2825.

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Web-based learning has become an important way to enhance learning and teaching, offering many learning opportunities. A limitation of current Web-based learning is the restricted ability of students to personalize and annotate the learning materials. Providing personalized tools and analyzing some types of learning behavior, such as studentsâ€™ annotation, has attracted attention as a means to enhance Web-based learning. There has been a sharp increase in the volume and quality of electronic publishing on the web in the past few years. Many research journals are going on-line. The advantages of pelectronic publishing are obvious and enormous: instantaneous access to archives, paperless media and fast document search to name a few. However, annotation of documents in electronic form has been surprisingly underdeveloped. Existing word processing software offers some tools for electronic document annotation. But as of today, these annotation features are so modest and limited, that they lose out to the convenient common practice of working with paper versions of documents [1]. The results of a surveys conducted came as no surprise: the absolute majority of researchers and students prefer to print out an electronic paper before reading and annotating it. Our claim is that electronic annotations can not only be as convenient as their paper counterparts, but they are superior in terms of the additional advanced capabilities they can offer. This claim makes the basis of our Annotation Technology (AT). AT is a set of principles that form a foundation for development of advanced and successful electronic annotation systems.

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Mazhoud, Omar, Anis Kalboussi, and Ahmed Hadj Kacem. "Educational Recommender System based on Learner’s Annotative Activity." International Journal of Emerging Technologies in Learning (iJET) 16, no. 10 (May 25, 2021): 108. http://dx.doi.org/10.3991/ijet.v16i10.19955.

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In recent years, Educational Recommender Systems (ERSs) have attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. These systems play a key role in helping learners to find educational resources relevant and pertinent to their profiles and context. So, it is necessary to identify information that helps learner’s profile definition and in identifying requests and interests. In this context, we suggest to take advantage of the annotation activity used usually in the learning context for different purposes and which may reflect certain learner’s characteristics useful as input data for the recommendation process. Therefore, we propose an educational recommender system of web services based on learner’s annotative activity to assist him in his learning activity. This process of recommendation is founded on two preparatory phases: the phase of modelling learner’s personality profile through analysis of annotation digital traces in learning environment realized through a profile constructor module and the phase of discovery of web services which can meet the goals of annotations made by learner via the web service discovery module. The evaluation of the developed annotation based recommendation system through empirical studies realized on groups of learners based on the Student’s t-test showed significant results.

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Kerui, Chen, Wanli Zuo, Fengling He, Yongheng Chen, and Ying Wang. "Data extraction and annotation based on domain-specific ontology evolution for deep web." Computer Science and Information Systems 8, no. 3 (2011): 673–92. http://dx.doi.org/10.2298/csis101011023k.

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Deep web respond to a user query result records encoded in HTML files. Data extraction and data annotation, which are important for many applications, extracts and annotates the record from the HTML pages. We proposed an domain-specific ontology based data extraction and annotation technique; we first construct mini-ontology for specific domain according to information of query interface and query result pages; then, use constructed mini-ontology for identifying data areas and mapping data annotations in data extraction; in order to adapt to new sample set, mini-ontology will evolve dynamically based on data extraction and data annotation. Experimental results demonstrate that this method has higher precision and recall in data extraction and data annotation.

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Bryan-Kinns, Nick. "Annotating Distributed Scores for Mutual Engagement in Daisyphone and Beyond." Leonardo Music Journal 21 (December 2011): 51–55. http://dx.doi.org/10.1162/lmj_a_00061.

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Written and drawn annotations of musical scores form a core part of the music composition process for both individuals and groups. This article reflects on the annotations made in new forms of distributed music-making wherein the score and its annotations are shared across the web. Four kinds of annotation are identified from 8 years of studies of mutual engagement through distributed music-making systems. It is suggested that new forms of web-based music-making might benefit from shared and persistent graphical annotation mechanisms.

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Saklofske, Jon. "brat Rapid Annotation Tool. Web-based annotation and visualization tool." Renaissance and Reformation 42, no. 2 (October 24, 2019): 180–89. http://dx.doi.org/10.7202/1065133ar.

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Rydell, Christopher, and Joakim Lindblad. "CytoBrowser: a browser-based collaborative annotation platform for whole slide images." F1000Research 10 (March 22, 2021): 226. http://dx.doi.org/10.12688/f1000research.51916.1.

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We present CytoBrowser, an open-source (GPLv3) JavaScript and Node.js driven environment for fast and accessible collaborative online visualization, assessment, and annotation of very large microscopy images, including, but not limited to, z-stacks (focus stacks) of cytology or histology whole slide images. CytoBrowser provides a web-based viewer for high-resolution zoomable images and facilitates easy remote collaboration, with options for joint-view visualization and simultaneous collaborative annotation of very large datasets. It delivers a unique combination of functionalities not found in other software solutions, making it a preferred tool for large scale annotation of whole slide image data. The web browser interface is directly accessible on any modern computer or even on a mobile phone, without need for additional software. By sharing a "session", several remote users can interactively explore and jointly annotate whole slide image data, thereby reaching improved data understanding and annotation quality, effortless project scaling and distribution of resources to/from remote locations, efficient creation of "ground truth" annotations for methods' evaluation and training of machine learning-based approaches, a user-friendly learning environment for medical students, to just name a few. Rectangle and polygon region annotations complement point-based annotations, each with a selectable annotation-class as well as free-form text fields. The default setting of CytoBrowser presents an interface for the Bethesda cancer grading system, while other annotation schemes can easily be incorporated. Automatic server side storage of annotations is complemented by JSON-based import/export options facilitating easy interoperability with other tools. CytoBrowser is available here: https://mida-group.github.io/CytoBrowser/.

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Lee, Eduardo, Gregg A. Helt, Justin T. Reese, Monica C. Munoz-Torres, Chris P. Childers, Robert M. Buels, Lincoln Stein, Ian H. Holmes, Christine G. Elsik, and Suzanna E. Lewis. "Web Apollo: a web-based genomic annotation editing platform." Genome Biology 14, no. 8 (2013): R93. http://dx.doi.org/10.1186/gb-2013-14-8-r93.

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Islamaj, Rezarta, Dongseop Kwon, Sun Kim, and Zhiyong Lu. "TeamTat: a collaborative text annotation tool." Nucleic Acids Research 48, W1 (May 8, 2020): W5—W11. http://dx.doi.org/10.1093/nar/gkaa333.

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Abstract Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for figure display, project management, and multi-user team annotation. In response, we developed TeamTat (https://www.teamtat.org), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC (uploaded locally or automatically retrieved from PubMed/PMC), and output format is BioC with inline annotations. TeamTat displays figures from the full text for the annotator's convenience. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat provides corpus quality assessment via inter-annotator agreement statistics, and a user-friendly interface convenient for annotation review and inter-annotator disagreement resolution to improve corpus quality.

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Chen, Yen-Chen, Yun-Ching Chen, Wen-Dar Lin, Chung-Der Hsiao, Hung-Wen Chiu, and Jan-Ming Ho. "Bio301: A Web-Based EST Annotation Pipeline That Facilitates Functional Comparison Studies." ISRN Bioinformatics 2012 (November 22, 2012): 1–5. http://dx.doi.org/10.5402/2012/139842.

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In this postgenomic era, a huge volume of information derived from expressed sequence tags (ESTs) has been constructed for functional description of gene expression profiles. Comparative studies have become more and more important to researchers of biology. In order to facilitate these comparative studies, we have constructed a user-friendly EST annotation pipeline with comparison tools on an integrated EST service website, Bio301. Bio301 includes regular EST preprocessing, BLAST similarity search, gene ontology (GO) annotation, statistics reporting, a graphical GO browsing interface, and microarray probe selection tools. In addition, Bio301 is equipped with statistical library comparison functions using multiple EST libraries based on GO annotations for mining meaningful biological information.

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Jaspe-Villanueva, Alberto, Moonisa Ahsan, Ruggero Pintus, Andrea Giachetti, Fabio Marton, and Enrico Gobbetti. "Web-based Exploration of Annotated Multi-Layered Relightable Image Models." Journal on Computing and Cultural Heritage 14, no. 2 (June 2021): 1–29. http://dx.doi.org/10.1145/3430846.

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We introduce a novel approach for exploring image-based shape and material models registered with structured descriptive information fused in multi-scale overlays. We represent the objects of interest as a series of registered layers of image-based shape and material data. These layers are represented at different scales and can come out of a variety of pipelines. These layers can include both Reflectance Transformation Imaging representations, and spatially varying normal and Bidirectional Reflectance Distribution Function fields, possibly as a result of fusing multi-spectral data. An overlay image pyramid associates visual annotations to the various scales. The overlay pyramid of each layer is created at data preparation time by either one of the three subsequent methods: (1) by importing it from other pipelines, (2) by creating it with the simple annotation drawing toolkit available within the viewer, and (3) with external image editing tools. This makes it easier for the user to seamlessly draw annotations over the region of interest. At runtime, clients can access an annotated multi-layered dataset by a standard web server. Users can explore these datasets on a variety of devices; they range from small mobile devices to large-scale displays used in museum installations. On all these aforementioned platforms, JavaScript/WebGL2 clients running in browsers are fully capable of performing layer selection, interactive relighting, enhanced visualization, and annotation display. We address the problem of clutter by embedding interactive lenses. This focus-and-context-aware (multiple-layer) exploration tool supports exploration of more than one representation in a single view. That allows mixing and matching of presentation modes and annotation display. The capabilities of our approach are demonstrated on a variety of cultural heritage use-cases. That involves different kinds of annotated surface and material models.

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Saravanan, Vijayakumar, and Primanayagam Shanmughavel. "An automated protein annotation filter for integrating web-based annotation tools." Bioinformation 2, no. 2 (August 16, 2007): 76–77. http://dx.doi.org/10.6026/97320630002076.

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YUAN, Liu. "Ontology-Based Annotation for Deep Web Data." Journal of Software 19, no. 2 (July 9, 2008): 237–45. http://dx.doi.org/10.3724/sp.j.1001.2008.00237.

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Yamamoto, Daisuke, and Katashi Nagao. "Web-based Video Annotation and its Applications." Transactions of the Japanese Society for Artificial Intelligence 20 (2005): 67–75. http://dx.doi.org/10.1527/tjsai.20.67.

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Ke, Xiao, Shaozi Li, and Guolong Chen. "Real web community based automatic image annotation." Computers & Electrical Engineering 39, no. 3 (April 2013): 945–56. http://dx.doi.org/10.1016/j.compeleceng.2012.09.017.

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Duan, Daoyu, Sijia He, Emina Huang, Ziyi Li, and Hao Feng. "NeuCA web server: a neural network-based cell annotation tool with web-app and GUI." Bioinformatics 38, no. 8 (February 17, 2022): 2361–63. http://dx.doi.org/10.1093/bioinformatics/btac108.

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Abstract Summary Correctly annotating individual cell’s type is an important initial step in single-cell RNA sequencing (scRNA-seq) data analysis. Here, we present NeuCA web server, a neural network-based scRNA-seq cell annotation tool with web-app portal and graphical user interface, for automatically assigning cell labels. NeuCA algorithm is accurate and exhaustive, maximizing the usage of measured cells for downstream analysis. NeuCA web server provides over 20 ready-to-use pre-trained classifiers for commonly used tissue types. As the first web-app tool with neural-network infrastructure implemented, NeuCA web will facilitate the research community in analyzing and annotating scRNA-seq data. Availability and implementation NeuCA web server is implemented with R Shiny application online at https://statbioinfo.shinyapps.io/NeuCA/. Supplementary information Supplementary data are available at Bioinformatics online.

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Sánchez, David, David Isern, and Miquel Millan. "Content annotation for the semantic web: an automatic web-based approach." Knowledge and Information Systems 27, no. 3 (May 21, 2010): 393–418. http://dx.doi.org/10.1007/s10115-010-0302-3.

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Yueh, Hsiu-Ping, Ya-Ting Teng, Weijane Lin, Yu-Ching Wang, and Chiu-Fan Hu. "Web-Based Annotation Learning System: Construction and Application." Creative Education 03, no. 08 (2012): 1297–300. http://dx.doi.org/10.4236/ce.2012.38189.

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Jiang, Xia, and Yingming Li. "Web Service Matching Based on Natural Semantic Annotation." Information Technology Journal 12, no. 4 (April 1, 2013): 857–61. http://dx.doi.org/10.3923/itj.2013.857.861.

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Yamamoto, Daisuke, Tomoki Masuda, Shigeki Ohira, and Katashi Nagao. "Video Scene Annotation Based on Web Social Activities." IEEE Multimedia 15, no. 3 (July 2008): 22–32. http://dx.doi.org/10.1109/mmul.2008.67.

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LEE, Daesang, Hwajung SEO, Chankyu PARK, and Kiejung PARK. "WeGAS: A Web-Based Microbial Genome Annotation System." Bioscience, Biotechnology, and Biochemistry 73, no. 1 (January 23, 2009): 213–16. http://dx.doi.org/10.1271/bbb.80567.

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Azouaou, Faiçal, Hakim Mokeddem, Lamia Berkani, Abdelaziz Ouadah, and Belkacem Mostefai. "WebAnnot: a learner's dedicated web-based annotation tool." International Journal of Technology Enhanced Learning 5, no. 1 (2013): 56. http://dx.doi.org/10.1504/ijtel.2013.055949.

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Hemmerich, Chris, Aaron Buechlein, Ram Podicheti, Kashi V. Revanna, and Qunfeng Dong. "An Ergatis-based prokaryotic genome annotation web server." Bioinformatics 26, no. 8 (March 1, 2010): 1122–24. http://dx.doi.org/10.1093/bioinformatics/btq090.

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Karimzadeh, Morteza, and Alan MacEachren. "GeoAnnotator: A Collaborative Semi-Automatic Platform for Constructing Geo-Annotated Text Corpora." ISPRS International Journal of Geo-Information 8, no. 4 (March 27, 2019): 161. http://dx.doi.org/10.3390/ijgi8040161.

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Ground-truth datasets are essential for the training and evaluation of any automated algorithm. As such, gold-standard annotated corpora underlie most advances in natural language processing (NLP). However, only a few relatively small (geo-)annotated datasets are available for geoparsing, i.e., the automatic recognition and geolocation of place references in unstructured text. The creation of geoparsing corpora that include both the recognition of place names in text and matching of those names to toponyms in a geographic gazetteer (a process we call geo-annotation), is a laborious, time-consuming and expensive task. The field lacks efficient geo-annotation tools to support corpus building and lacks design guidelines for the development of such tools. Here, we present the iterative design of GeoAnnotator, a web-based, semi-automatic and collaborative visual analytics platform for geo-annotation. GeoAnnotator facilitates collaborative, multi-annotator creation of large corpora of geo-annotated text by generating computationally-generated pre-annotations that can be improved by human-annotator users. The resulting corpora can be used in improving and benchmarking geoparsing algorithms as well as various other spatial language-related methods. Further, the iterative design process and the resulting design decisions can be used in annotation platforms tailored for other application domains of NLP.

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Lee, Kye Hwa, Hyunsung Lee, Jin-Hyeok Park, Yi-Jun Kim, and Youngho Lee. "ANNO: A General Annotation Tool for Bilingual Clinical Note Information Extraction." Healthcare Informatics Research 28, no. 1 (January 31, 2022): 89–94. http://dx.doi.org/10.4258/hir.2022.28.1.89.

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Objectives: This study was conducted to develop a generalizable annotation tool for bilingual complex clinical text annotation, which led to the design and development of a clinical text annotation tool, ANNO.Methods: We designed ANNO to enable human annotators to support the annotation of information in clinical documents efficiently and accurately. First, annotations for different classes (word or phrase types) can be tagged according to the type of word using the dictionary function. In addition, it is possible to evaluate and reconcile differences by comparing annotation results between human annotators. Moreover, if the regular expression set for each class is updated during annotation, it is automatically reflected in the new document. The regular expression set created by human annotators is designed such that a word tagged once is automatically labeled in new documents.Results: Because ANNO is a Docker-based web application, users can use it freely without being subjected to dependency issues. Human annotators can share their annotation markups as regular expression sets with a dictionary structure, and they can cross-check their annotated corpora with each other. The dictionary-based regular expression sharing function, cross-check function for each annotator, and standardized input (Microsoft Excel) and output (extensible markup language [XML]) formats are the main features of ANNO.Conclusions: With the growing need for massively annotated clinical data to support the development of machine learning models, we expect ANNO to be helpful to many researchers.

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Wang, Qishan, Hongbo Zhao, and Yuchun Pan. "SNPknow: a web server for functional annotation of cattle SNP markers." Canadian Journal of Animal Science 91, no. 2 (June 2011): 247–53. http://dx.doi.org/10.4141/cjas2010-032.

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Wang, Q., Zhao, H. and Pan, Y. 2011. SNPknow: a web server for functional annotation of cattle SNP markers. Can. J. Anim. Sci. 91: 247–253. Single nucleotide polymorphisms (SNP) microarray technology provides new insights to identify the genetic factors associated with the traits of interest. To meet the immediate need for a framework of genome-wide association study (GWAS), we have developed SNPknow, a suite of CGI-based tools that provide enrichment analysis and functional annotation for cattle SNP markers and allow the users to navigate and analysis large sets of high-dimensional data from the gene ontology (GO) annotation systems. SNPknow is the only web server currently providing functional annotations of cattle SNP markers in three commercial platforms and dbSNP database. The web server may be particularly beneficial for the analysis of combining SNP association analysis with the gene set enrichment analysis and is freely available at http://klab.sjtu.edu.cn/SNPknow .

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Stefanics, Daniela, and Markus Fox. "COCO Annotator." ACM SIGMultimedia Records 13, no. 3 (September 2021): 1. http://dx.doi.org/10.1145/3578495.3578502.

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COCO Annotator is an image annotation tool that allows the labelling of images to create training data for object detection and localization. It provides many features, including the ability to label an image segment by drawing, label objects with disconnected visible parts, efficiently store and export annotations in the well-known COCO format as well as importing existing publicly available datasets in COCO format. Once installed, or started with Docker, the interface is web-based and customizable, and provides different tools for creating datasets. The exported annotations can be used for the training of modern deep learning algorithms (Mask R-CNN, YOLO, etc.). Import of the COCO format is supported by several machine learning frameworks. COCO Annotator is developed by Justin Brooks and is supported by a lively community on Github [1].

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Park, Yeon-Ji, Min-a. Lee, Geun-Je Yang, Soo Jun Park, and Chae-Bong Sohn. "Biomedical Text NER Tagging Tool with Web Interface for Generating BERT-Based Fine-Tuning Dataset." Applied Sciences 12, no. 23 (November 24, 2022): 12012. http://dx.doi.org/10.3390/app122312012.

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In this paper, a tagging tool is developed to streamline the process of locating tags for each term and manually selecting the target term. It directly extracts the terms to be tagged from sentences and displays it to the user. It also increases tagging efficiency by allowing users to reflect candidate categories in untagged terms. It is based on annotations automatically generated using machine learning. Subsequently, this architecture is fine-tuned using Bidirectional Encoder Representations from Transformers (BERT) to enable the tagging of terms that cannot be captured using Named-Entity Recognition (NER). The tagged text data extracted using the proposed tagging tool can be used as an additional training dataset. The tagging tool, which receives and saves new NE annotation input online, is added to the NER and RE web interfaces using BERT. Annotation information downloaded by the user includes the category (e.g., diseases, genes/proteins) and the list of words associated to the named entity selected by the user. The results reveal that the RE and NER results are improved using the proposed web service by collecting more NE annotation data and fine-tuning the model using generated datasets. Our application programming interfaces and demonstrations are available to the public at via the website link provided in this paper.

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Asswad, Mohammad Mourhaf AL, Sergio de Cesare, and Mark Lycett. "A Query-based Approach for Semi-Automatic Annotation of Web Services." International Journal of Information Systems and Social Change 2, no. 2 (April 2011): 37–54. http://dx.doi.org/10.4018/jissc.2011040103.

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Semantic Web services (SWS) have attracted increasing attention due to their potential to automate discovery and composition of current syntactic Web services. An issue that prevents a wider adoption of SWS relates to the manual nature of the semantic annotation task. Manual annotation is a difficult, error-prone, and time-consuming process and automating the process is highly desirable. Though some approaches have been proposed to semi-automate the annotation task, they are difficult to use and cannot perform accurate annotation for the following reasons: (1) They require building application ontologies to represent candidate services and (2) they cannot perform accurate name-based matching when labels of candidate service elements and ontological entities contain Compound Nouns (CN). To overcome these two deficiencies, this paper proposes a query-based approach that can facilitate semi-automatic annotation of Web services. The proposed approach is easy to use because it does not require building application ontologies to represent services. Candidate service elements that need to be annotated are extracted from a WSDL file and used to generate query instances by filling a Standard Query Template. The resulting query instances are executed against a repository of ontologies using a novel query execution engine to find appropriate correspondences for candidate service elements. This query execution engine employs name-based and structural matching mechanisms that can perform effective and accurate similarity measurements between labels containing CNs. The proposed semi-automatic annotation approach is evaluated by employing it to annotate existing Web services using published domain ontologies. Precision and recall are used as evaluation metrics. The resulting precision and recall values demonstrate the effectiveness and applicability of the proposed approach.

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Belhajjame, Khalid, Suzanne M. Embury, Norman W. Paton, Robert Stevens, and Carole A. Goble. "Automatic annotation of Web services based on workflow definitions." ACM Transactions on the Web 2, no. 2 (April 2008): 1–34. http://dx.doi.org/10.1145/1346337.1346239.

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Nalini, Ponnuru, and M. Mohana Deepthi. "Tag Based Relational Web Annotation Approach For Decision Making." International Journal of Computer & Organization Trends 15, no. 1 (December 25, 2014): 29–34. http://dx.doi.org/10.14445/22492593/ijcot-v15p306.

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Wang, Xin-Jing, Lei Zhang, and Wei-Ying Ma. "Duplicate-Search-Based Image Annotation Using Web-Scale Data." Proceedings of the IEEE 100, no. 9 (September 2012): 2705–21. http://dx.doi.org/10.1109/jproc.2012.2193109.

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Rajput, Quratulain. "Ontology based Semantic Annotation of Urdu Language Web Documents." Procedia Computer Science 35 (2014): 662–70. http://dx.doi.org/10.1016/j.procs.2014.08.148.

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Wei, Liu, Lin Can, and Meng Xiaofeng. "Web database query interface annotation based on user collaboration." Wuhan University Journal of Natural Sciences 11, no. 5 (September 2006): 1403–6. http://dx.doi.org/10.1007/bf02829275.

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Hwang, Wu-Yuin, Chin-Yu Wang, and Mike Sharples. "A study of multimedia annotation of Web-based materials." Computers & Education 48, no. 4 (May 2007): 680–99. http://dx.doi.org/10.1016/j.compedu.2005.04.020.

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Bontcheva, Kalina, Hamish Cunningham, Ian Roberts, Angus Roberts, Valentin Tablan, Niraj Aswani, and Genevieve Gorrell. "GATE Teamware: a web-based, collaborative text annotation framework." Language Resources and Evaluation 47, no. 4 (February 2, 2013): 1007–29. http://dx.doi.org/10.1007/s10579-013-9215-6.

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Lourenço, André, Hugo Plácido da Silva, Carlos Carreiras, Ana Priscila Alves, and Ana L. N. Fred. "A web-based platform for biosignal visualization and annotation." Multimedia Tools and Applications 70, no. 1 (March 20, 2013): 433–60. http://dx.doi.org/10.1007/s11042-013-1397-9.

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Kavasidis, Isaak, Simone Palazzo, Roberto Di Salvo, Daniela Giordano, and Concetto Spampinato. "An innovative web-based collaborative platform for video annotation." Multimedia Tools and Applications 70, no. 1 (March 26, 2013): 413–32. http://dx.doi.org/10.1007/s11042-013-1419-7.

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Saklofske, Jon. "Pyysalo, Sampo, project lead. brat Rapid Annotation Tool. Web-based annotation and visualization tool." Renaissance and Reformation 42, no. 2 (August 27, 2019): 180–89. http://dx.doi.org/10.33137/rr.v42i2.32993.

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Kashoob, Said, James Caverlee, and Ying Ding. "A Categorical Model for Discovering Latent Structure in Social Annotations." Proceedings of the International AAAI Conference on Web and Social Media 3, no. 1 (March 19, 2009): 82–89. http://dx.doi.org/10.1609/icwsm.v3i1.13943.

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The advent of social tagging systems has enabled a new community-based view of the Web in which objects like images, videos, and Web pages are annotated by thousands of users. Understanding the emergent semantics inherent in the socially-generated collection of annotations has important research implications for information discovery and knowledge sharing. To this end, we propose a novel probabilistic generative model for discovering latent structure in large-scale social annotations. The generative model identifies latent community-based ``categories'' of interest that can be used to group semantically-related tags and to augment traditional content-based information search and discovery. We illustrate the proposed approach over large collections of Web objects annotated by the Flickr and Delicious communities. Additionally, we show how to integrate the annotation-based categorical model with traditional content-based approaches for the effective focused discovery and exploration of Web objects.

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Fu, Ju Chun. "An Approach of Image Semantic Automatic Tagging Based on SVM." Applied Mechanics and Materials 530-531 (February 2014): 382–85. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.382.

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To find the need of images from Internet is a heavy work, image annotation techniques can be used to help us quickly find the required images, the traditional image annotation method can't between the image and text classification, cannot achieve most web users demand, this paper proposes an image semantic annotation technology automatically, based on the SVM technology. The first we map image visual feature to one or more images of rough concept by using annotation, then we preprocess web page text information, lastly, we selected high similarity of keywords as image annotation. From the experimental effect it can effectively improve the image tagging precision.

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Merin, J. Brindha, Dr W. Aisha Banu, Akila R., and Radhika A. "Semantic Annotation Based Mechanism for Web Service Discovery and Recommendation." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 14, no. 3 (September 30, 2023): 169–85. http://dx.doi.org/10.58346/jowua.2023.i3.013.

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Web Mining is regarded as one among the data mining techniques that aids in fetching and extraction of necessary data from the web. Conversely, Web usage mining discovers and extracts essential patterns usage over the webs which are being further utilized by various web applications. In order to discover and explore web services that are registered with documents of Web Services-Inspection, Discovery and Integration registry, Universal Description wants specific search circumstance similar to URL, category and service name. The document of Web Service Description Language (WSDL) offers a condition of the web services customers to take out operations, communications and the service format of right message. Therefore, WSDL is being utilized together with semantic explanation dependent substantiation for the extraction of different web services for related purpose, other supporting operations and attributes. The reason is that there subsist different web services having corresponding functionalities however altered or changeable attributes that are non–functional. Resultant, recognize the preeminent web service become tiresome for the user. A method is projected which caters the analysis of service resemblance with the aid of semantic annotation and machine learning (ML) algorithms depending on the analysis intended for enhancing the classification through capturing useful web services semantics related with real world. The emphasizes on the research technique of choosing preeminent web service for the user based on the semantic annotation. The research work in turn recommends a web mining technique that determines the best web service automatically thus ranking concepts in service textual documentation and classifies services on behalf of particular domains. Parallel computation is made easy with web services. The different management stages in the system of recommendation entail collection of dataset through WSDL on the semantic annotation basis, thereby recognizing the best service with the DOBT-Dynamic operation dependent discovering method, ranking through mechanisms MDBR - Multi-Dimensional based ranking, recommendation and classification. In this work, it has been employed a combination of fundamental ML estimators, namely Multinomial Naive Bayes (MNB) and Support Vector Machines (SVM), as well as ensemble techniques such as Bagging, Random Forests, and AdaBoost, to perform classification of Web services. It was observed from the investigate work that the adapted system of best web services recommendation defers high performance in contradiction of the existing recommendation technique regarding accuracy, efficiency in addition to processing time.

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Chen, Chih-Ming, and Ming-Yueh Tsay. "Applications of collaborative annotation system in digital curation, crowdsourcing, and digital humanities." Electronic Library 35, no. 6 (November 6, 2017): 1122–40. http://dx.doi.org/10.1108/el-08-2016-0172.

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Purpose Collaboratively annotating digital texts allow users to add valued information, share ideas and create knowledge. Most importantly, annotated content can help users obtain a deeper and broader understanding of a text compared to digital content without annotations. This work proposes a novel collaborative annotation system (CAS) with four types of multimedia annotations including text annotation, picture annotation, voice annotation and video annotation which can embedded into any HTML Web pages to enable users to collaboratively add and manage annotations on these pages and provide a shared mechanism for discussing shared annotations among multiple users. By applying the CAS in a mashup on static HTML Web pages, this study aims to discuss the applications of CAS in digital curation, crowdsourcing and digital humanities to encourage existing strong relations among them. Design/methodology/approach This work adopted asynchronous JavaScript (Ajax) and a model-view-controller framework to implement a CAS with reading annotation tools for knowledge creating, archiving and sharing services, as well as applying the implemented CAS to support digital curation, crowdsourcing and digital humanities. A questionnaire survey method was used to investigate the ideas and satisfaction of visitors who attended a digital curation with CAS support in the item dimensions of the interactivity with displayed products, the attraction and the content absorption effect. Also, to collect qualitative data that may not be revealed by the questionnaire survey, semi-structured interviews were performed at the end of the digital curation exhibition activity. Additionally, the effects of the crowdsourcing and digital humanities with CAS support on collecting and organizing ideas and opinions for historical events and promoting humanity research outcomes were considered as future works because they all need to take a long time to investigate. Findings Based on the questionnaire survey, this work found that the digital curation with CAS support revealed the highest rating score in terms of the item dimension of attraction effect. The result shows applying CAS to support digital curation is practicable, novel and interesting to visitors. Additionally, this work also successfully applied the developed CAS to crowdsourcing and digital humanities so that the two research fields may be brought into a new ground. Originality/value Based on the CAS, this work developed a novel digital curation approach which has a high degree of satisfaction on attraction effect to visitors, an innovative crowdsourcing platform that combined with a digital archive system to efficiently gather collective intelligence to solve the difficult problems of identifying digital archive contents and a high potential digital humanity research mode that can assist humanities scholars to annotate the texts with distinct interpretation and viewpoints on an ancient map, as well as discuss with other humanities scholars to stimulate discussion on more issues.

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Ghashghaei, Mehrnaz, Ebrahim Bagheri, John Cuzzola, Ali A. Ghorbani, and Zeinab Noorian. "Semantic Disambiguation and Linking of Quantitative Mentions in Textual Content." International Journal of Semantic Computing 10, no. 01 (March 2016): 121–42. http://dx.doi.org/10.1142/s1793351x16500021.

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Semantic annotation techniques provide the basis for linking textual content with concepts in well grounded knowledge bases. In spite of their many application areas, current semantic annotation systems have some limitations. One of the prominent limitations of such systems is that none of the existing semantic annotator systems are able to identify and disambiguate quantitative (numerical) content. In textual documents such as Web pages, specially technical contents, there are many quantitative information such as product specifications that need to be semantically qualified. In this paper, we propose an approach for annotating quantitative values in short textual content. In our approach, we identify numeric values in the text and link them to an existing property in a knowledge base. Based on this mapping, we are then able to find the concept that the property is associated with, whereby identifying both the concept and the specific property of that concept that the numeric value belongs to. Results obtained from the developed gold standard dataset show that the proposed automated semantic annotation platform is quite effective in detecting and disambiguating numerical content, and connecting them to associated properties on the external knowledge base. Our experiments show that our proposed approach is able to reach an accuracy of over 70% for semantically annotating quantitative content.

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Ramakrishnaiah, Yashpal, Adam P. Morris, Jasbir Dhaliwal, Melcy Philip, Levin Kuhlmann, and Sonika Tyagi. "Linc2function: A Comprehensive Pipeline and Webserver for Long Non-Coding RNA (lncRNA) Identification and Functional Predictions Using Deep Learning Approaches." Epigenomes 7, no. 3 (September 15, 2023): 22. http://dx.doi.org/10.3390/epigenomes7030022.

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Long non-coding RNAs (lncRNAs), comprising a significant portion of the human transcriptome, serve as vital regulators of cellular processes and potential disease biomarkers. However, the function of most lncRNAs remains unknown, and furthermore, existing approaches have focused on gene-level investigation. Our work emphasizes the importance of transcript-level annotation to uncover the roles of specific transcript isoforms. We propose that understanding the mechanisms of lncRNA in pathological processes requires solving their structural motifs and interactomes. A complete lncRNA annotation first involves discriminating them from their coding counterparts and then predicting their functional motifs and target bio-molecules. Current in silico methods mainly perform primary-sequence-based discrimination using a reference model, limiting their comprehensiveness and generalizability. We demonstrate that integrating secondary structure and interactome information, in addition to using transcript sequence, enables a comprehensive functional annotation. Annotating lncRNA for newly sequenced species is challenging due to inconsistencies in functional annotations, specialized computational techniques, limited accessibility to source code, and the shortcomings of reference-based methods for cross-species predictions. To address these challenges, we developed a pipeline for identifying and annotating transcript sequences at the isoform level. We demonstrate the effectiveness of the pipeline by comprehensively annotating the lncRNA associated with two specific disease groups. The source code of our pipeline is available under the MIT licensefor local use by researchers to make new predictions using the pre-trained models or to re-train models on new sequence datasets. Non-technical users can access the pipeline through a web server setup.

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Wei, Chih-Hsuan, Alexis Allot, Robert Leaman, and Zhiyong Lu. "PubTator central: automated concept annotation for biomedical full text articles." Nucleic Acids Research 47, W1 (May 22, 2019): W587—W593. http://dx.doi.org/10.1093/nar/gkz389.

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AbstractPubTator Central (https://www.ncbi.nlm.nih.gov/research/pubtator/) is a web service for viewing and retrieving bioconcept annotations in full text biomedical articles. PubTator Central (PTC) provides automated annotations from state-of-the-art text mining systems for genes/proteins, genetic variants, diseases, chemicals, species and cell lines, all available for immediate download. PTC annotates PubMed (29 million abstracts) and the PMC Text Mining subset (3 million full text articles). The new PTC web interface allows users to build full text document collections and visualize concept annotations in each document. Annotations are downloadable in multiple formats (XML, JSON and tab delimited) via the online interface, a RESTful web service and bulk FTP. Improved concept identification systems and a new disambiguation module based on deep learning increase annotation accuracy, and the new server-side architecture is significantly faster. PTC is synchronized with PubMed and PubMed Central, with new articles added daily. The original PubTator service has served annotated abstracts for ∼300 million requests, enabling third-party research in use cases such as biocuration support, gene prioritization, genetic disease analysis, and literature-based knowledge discovery. We demonstrate the full text results in PTC significantly increase biomedical concept coverage and anticipate this expansion will both enhance existing downstream applications and enable new use cases.

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Ting, Yu. "Multiple Features Image Fusion Based on Visual Dictionary." Advanced Materials Research 889-890 (February 2014): 1111–14. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.1111.

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It has been heavy work that to find the related pictures form Internet without annotation. Therefore, the automatic image annotation was extremely important in image retrieval. The traditional method were translated image visual feature into keywords simply, but it ignored the image similarity problem between the low-level visual features and high-level semantic. That is image "gap" problem, so image annotation was very lower. This paper puts forward a classification of web based image content automatic tagging mixing technology, the first it will map visual feature of image to one or more rough images, then we will preprocess the web page text information, finally we select some keywords similarity as image annotation by using similar semantic processing module. So it realizes the image and text combining the automatic annotation and it achieve high precision of image annotation.

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Barrett, Kristian, Cameron J. Hunt, Lene Lange, and Anne S. Meyer. "Conserved unique peptide patterns (CUPP) online platform: peptide-based functional annotation of carbohydrate active enzymes." Nucleic Acids Research 48, W1 (May 14, 2020): W110—W115. http://dx.doi.org/10.1093/nar/gkaa375.

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Abstract The CUPP platform includes a web server for functional annotation and sub-grouping of carbohydrate active enzymes (CAZymes) based on a novel peptide-based similarity assessment algorithm, i.e. protein grouping according to Conserved Unique Peptide Patterns (CUPP). This online platform is open to all users and there is no login requirement. The web server allows the user to perform genome-based annotation of carbohydrate active enzymes to CAZy families, CAZy subfamilies, CUPP groups and EC numbers (function) via assessment of peptide-motifs by CUPP. The web server is intended for functional annotation assessment of the CAZy inventory of prokaryotic and eukaryotic organisms from genomic DNA (up to 30MB compressed) or directly from amino acid sequences (up to 10MB compressed). The custom query sequences are assessed using the CUPP annotation algorithm, and the outcome is displayed in interactive summary result pages of CAZymes. The results displayed allow for inspection of members of the individual CUPP groups and include information about experimentally characterized members. The web server and the other resources on the CUPP platform can be accessed from https://cupp.info.

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Dijkshoorn, Chris, Victor De Boer, Lora Aroyo, and Guus Schreiber. "Accurator: Nichesourcing for Cultural Heritage." Human Computation 6 (June 2, 2019): 12–41. http://dx.doi.org/10.15346/hc.v6i1.91.

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With the increase of cultural heritage data published online, the usefulness of data in this open context hinges on the quality and diversity of descriptions of collection objects. In many cases, existing descriptions are not sufficient for retrieval and research tasks, resulting in the need for more specific annotations. However, eliciting such annotations is a challenge since it often requires domain-specific knowledge. Where crowdsourcing can be successfully used to execute simple annotation tasks, identifying people with the required expertise might prove troublesome for more complex and domain-specific tasks. Nichesourcing addresses this problem, by tapping into the expert knowledge available in niche communities. This paper presents Accurator, a methodology for conducting nichesourcing campaigns for cultural heritage institutions, by addressing communities, organizing events and tailoring a web-based annotation tool to a domain of choice. The contribution of this paper is fourfold: 1) a nichesourcing methodology, 2) an annotation tool for experts, 3) validation of the methodology in three case studies and 4) a dataset including the obtained annotations. The three domains of the case studies are birds on art, bible prints and fashion images. We compare the quality and quantity of obtained annotations in the three case studies, showing that the nichesourcing methodology in combination with the image annotation tool can be used to collect high-quality annotations in a variety of domains. A user evaluation indicates the tool is suited and usable for domain-specific annotation tasks.

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