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.

General purpose annotation service for portal-based applications

Abstract: A method of providing annotations within a portal environment can include executing a portlet object having a tag, wherein the tag specifies an annotation service associated with an artifact presented by a graphical user interface of the portal environment. A visual identifier can be presented proximate to the artifact. The visual identifier can reference the annotation service. The method further can include creating an annotation using the annotation service responsive to the selection of the visual identifier.

Image Annotation Within the Context of Personal Photo Collections Using Hierarchical Event and Scene Models

Abstract: Most image annotation systems consider a single photo at a time and label photos individually. In this work, we focus on collections of personal photos and exploit the contextual information naturally implied by the associated GPS and time metadata. First, we employ a constrained clustering method to partition a photo collection into event-based subcollections, considering that the GPS records may be partly missing (a practical issue). We then use conditional random field (CRF) models to exploit the correlation between photos based on 1) time-location constraints and 2) the relationship between collection-level annotation (i.e., events) and image-level annotation (i.e., scenes). With the introduction of such a multilevel annotation hierarchy, our system addresses the problem of annotating consumer photo collections that requires a more hierarchical description of the customers' activities than do the simpler image annotation tasks. The efficacy of the proposed system is validated by extensive evaluation using a sizable geotagged personal photo collection database, which consists of over 100 photo collections and is manually labeled for 12 events and 12 scenes to create ground truth.

New Strategies for Image Annotation: Overview of the Photo Annotation Task at ImageCLEF 2010

Abstract: The ImageCLEF 2010 Photo Annotation Task poses the challenge of automated annotation of 93 visual concepts in Flickr photos. The participants were provided with a training set of 8,000 Flickr im- ages including annotations, EXIF data and Flickr user tags. Testing was performed on 10,000 Flickr images, dierentiated between approaches considering solely visual information, approaches relying on textual in- formation and multi-modal approaches. Half of the ground truth was acquired with a crowdsourcing approach. The evaluation followed two evaluation paradigms: per concept and per example. In total, 17 research teams participated in the multi-label classification challenge with 63 sub- missions. Summarizing the results, the task could be solved with a MAP of 0.455 in the multi-modal configuration, with a MAP of 0.407 in the visual-only configuration and with a MAP of 0.234 in the textual con- figuration. For the evaluation per example, 0.66 F-ex and 0.66 OS-FCS could be achieved for the multi-modal configuration, 0.68 F-ex and 0.65 OS-FCS for the visual configuration and 0.26 F-ex and 0.37 OS-FCS for the textual configuration.

OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction.

Abstract: Motivation Ontologies are widely used in biology for data annotation, integration and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotation axioms commonly used in ontologies include class labels, descriptions or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally unexploited by ontology-based analysis methods such as semantic similarity measures. Results We propose a novel method, OPA2Vec, to generate vector representations of biological entities in ontologies by combining formal ontology axioms and annotation axioms from the ontology meta-data. We apply a Word2Vec model that has been pre-trained on either a corpus or abstracts or full-text articles to produce feature vectors from our collected data. We validate our method in two different ways: first, we use the obtained vector representations of proteins in a similarity measure to predict protein-protein interaction on two different datasets. Second, we evaluate our method on predicting gene-disease associations based on phenotype similarity by generating vector representations of genes and diseases using a phenotype ontology, and applying the obtained vectors to predict gene-disease associations using mouse model phenotypes. We demonstrate that OPA2Vec significantly outperforms existing methods for predicting gene-disease associations. Using evidence from mouse models, we apply OPA2Vec to identify candidate genes for several thousand rare and orphan diseases. OPA2Vec can be used to produce vector representations of any biomedical entity given any type of biomedical ontology. Availability and implementation https://github.com/bio-ontology-research-group/opa2vec. Supplementary information Supplementary data are available at Bioinformatics online.