Annotation

About: Annotation is a research topic. Over the lifetime, 6719 publications have been published within this topic receiving 203463 citations. The topic is also known as: note & markup.

WebAnno: A Flexible, Web-based and Visually Supported System for Distributed Annotations

Abstract: We present WebAnno, a general purpose web-based annotation tool for a wide range of linguistic annotations. WebAnno offers annotation project management, freely configurable tagsets and the management of users in different roles. WebAnno uses modern web technology for visualizing and editing annotations in a web browser. It supports arbitrarily large documents, pluggable import/export filters, the curation of annotations across various users, and an interface to farming out annotations to a crowdsourcing platform. Currently WebAnno allows part-ofspeech, named entity, dependency parsing and co-reference chain annotations. The architecture design allows adding additional modes of visualization and editing, when new kinds of annotations are to be supported.

Semantic Annotation of Image Collections

Abstract: In this paper we discuss a tool for semantic annotation and search in a collection of art images. Multiple existing ontologies are used to support this process, including the Art and Architecture Thesaurus, WordNet, ULAN and Iconclass. We discuss knowledge-engineering aspect such as the annotation structure and links between the ontologies. The annotation and search process is illustrated with an application scenario.

Baselines for Image Annotation

Abstract: Automatically assigning keywords to images is of great interest as it allows one to retrieve, index, organize and understand large collections of image data. Many techniques have been proposed for image annotation in the last decade that give reasonable performance on standard datasets. However, most of these works fail to compare their methods with simple baseline techniques to justify the need for complex models and subsequent training. In this work, we introduce a new and simple baseline technique for image annotation that treats annotation as a retrieval problem. The proposed technique utilizes global low-level image features and a simple combination of basic distance measures to find nearest neighbors of a given image. The keywords are then assigned using a greedy label transfer mechanism. The proposed baseline method outperforms the current state-of-the-art methods on two standard and one large Web dataset. We believe that such a baseline measure will provide a strong platform to compare and better understand future annotation techniques.

CREAM: creating relational metadata with a component-based, ontology-driven annotation framework

Abstract: Richly interlinked, machine-understandable data constitutes the basis for the Semantic Web. Annotating web documents is one of the major techniques for creating metadata on the Web. However, annotation tools so far are restricted in their capabilities of providing richly interlinked and truely machine-understandable data. They basically allow the user to annotate with plain text according to a template structure, such as Dublin Core. We here present CREAM (Creating RElational, Annotation-based Metadata), a framework for an annotation environment that allows to construct relational metadata, i.e. metadata that comprises class instances and relationship instances. These instances are not based on a fix structure, but on a domain ontology. We discuss some of the requirements one has to meet when developing such a framework, e.g. the integration of a metadata crawler, inference services, document management and information extraction, and describe its implementation, viz. Ont-O-Mat a component-based, ontology-driven annotation tool.