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.

An unsupervised model for exploring hierarchical semantics from social annotations

Abstract: This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations, for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.us as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model's applicability on different environments. The experimental results demonstrate our model's efficiency.

Building a visual ontology for video retrieval

Abstract: To ensure access to growing video collections, annotation is becoming more and more important using background knowledge in the form of ontologies or thesauri is a way to facilitate annotation in a broad domain. Current ontologies are not suitable for (semi-) automatic annotation of visual resources as they contain little visual information about the concepts they describe. We investigate how an ontology that does contain visual information can facilitate annotation in a broad domain and identify requirements that a visual ontology has to meet. Based on these requirements, we create a visual ontology out of two existing knowledge corpora (WordNet and MPEG-7) by creating links between visual and general concepts. We test performance of the ontology on 40 shots of news video, and discuss the added value of each visual property.

Medical Imaging on the Semantic Web: Annotation and Image Markup.

Abstract: Medical images are proliferating at an explosive pace, similar to other types of data in e-Science. Technological solutions are needed to enable machines to help researchers and physicians access and use these images optimally. While Semantic Web technologies are showing promise in tackling the information challenges in biomedicine, less attention is focused on leveraging similar technologies in imaging. We are developing methods and tools to enable the transparent discovery and use of large distributed collections of medical images in cyberspace as well as within hospital information systems. Our approach is to make the human and machine descriptions of image pixel content machine-accessible through annotation using ontologies. We created an ontology of image annotation and markup, specifying the entities and relations necessary to represent the semantics of medical image pixel content. We are creating a toolkit to collect the annotations directly from researchers and physicians as they view the images on medical imaging workstations. Image annotations, represented as instances in the ontology can be serialized to a variety of formats, enabling interoperability among a variety of systems that contain images: medical records systems, image archives in hospitals, and the Semantic Web. The ontology-based annotations will enable images to be related to non-image data having related semantics and relevance. Our ultimate goal is to enable semantic integration of images and all the related scientific data pertaining to their content so that researchers and physicians can have the best understanding of the biological and physiological significance of image content.

Assessment of NER solutions against the first and second CALBC Silver Standard Corpus.

Abstract: Background: Competitions in text mining have been used to measure the performance of automatic text processing solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is time-consuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To overcome these shortcomings, the CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions, the first version of the Silver Standard Corpus (SSC-I). The four semantic groups are chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). This corpus has been used for the First CALBC Challenge asking the participants to annotate the corpus with their text processing solutions. Results: All four PPs from the CALBC project and in addition, 12 challenge participants (CPs) contributed annotated data sets for an evaluation against the SSC-I. CPs could ignore the training data and deliver the annotations from their genuine annotation system, or could train a machine-learning approach on the provided preannotated data. In general, the performances of the annotation solutions were lower for entities from the categories CHED and PRGE in comparison to the identification of entities categorized as DISO and SPE. The best performance over all semantic groups were achieved from two annotation solutions that have been trained on the SSC-I. The data sets from participants were used to generate the harmonised Silver Standard Corpus II (SSC-II), if the participant did not make use of the annotated data set from the SSC-I for training purposes. The performances of the participants’ solutions were again measured against the SSC-II. The performances of the annotation solutions showed again better results for DISO and SPE in comparison to CHED and PRGE.

GenomeGraphs: integrated genomic data visualization with R

Abstract: Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses. We developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system. GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.