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Search Results: 1 - 10 of 4440 matches for " Jane Hunter "
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Post-Publication Peer Review: Opening Up Scientific Conversation
Jane Hunter
Frontiers in Computational Neuroscience , 2012, DOI: 10.3389/fncom.2012.00063
Reconciling MPEG-7 and MPEG-21 Semantics through a Common Event-Aware Metadata Model
Jane Hunter
Computer Science , 2002,
Abstract: The "event" concept appears repeatedly when developing metadata models for the description and management of multimedia content. During the typical life cycle of multimedia content, events occur at many different levels - from the events which happen during content creation (directing, acting, camera panning and zooming) to the events which happen to the physical form (acquisition, relocation, damage of film or video) to the digital conversion, reformatting, editing and repackaging events, to the events which are depicted in the actual content (political, news, sporting) to the usage, ownership and copyright agreement events and even the metadata attribution events. Support is required within both MPEG-7 and MPEG-21 for the clear and unambiguous description of all of these event types which may occur at widely different levels of nesting and granularity. In this paper we first describe an event-aware model (the ABC model) which is capable of modeling and yet clearly differentiating between all of these, often recursive and overlapping events. We then illustrate how this model can be used as the foundation to facilitate semantic interoperability between MPEG-7 and MPEG-21. By expressing the semantics of both MPEG-7 and MPEG-21 metadata terms in RDF Schema (and some DAML+OIL extensions) and attaching the MPEG-7 and MPEG-21 class and property hierarchies to the appropriate top-level classes and properties of the ABC model, we are essentially able to define a single distributed machine-understandable ontology, which will enable interoperability of data and services across the entire multimedia content delivery chain.
Towards Annotopia—Enabling the Semantic Interoperability of Web-Based Annotations
Jane Hunter,Anna Gerber
Future Internet , 2012, DOI: 10.3390/fi4030788
Abstract: This paper describes the results of a collaborative effort that has reconciled the Open Annotation Collaboration (OAC) ontology and the Annotation Ontology (AO) to produce a merged data model [the Open Annotation (OA) data model] to describe Web-based annotations—and hence facilitate the discovery, sharing and re-use of such annotations. Using a number of case studies that include digital scholarly editing, 3D museum artifacts and sensor data streams, we evaluate the OA model’s capabilities. We also describe our implementation of an online annotation server that supports the storage, search and retrieval of OA-compliant annotations across multiple applications and disciplines. Finally we discuss outstanding problem issues associated with the OA ontology, and the impact that certain design decisions have had on the efficient storage, indexing, search and retrieval of complex structured annotations.
The Bone Dysplasia Ontology: integrating genotype and phenotype information in the skeletal dysplasia domain
Tudor Groza, Jane Hunter, Andreas Zankl
BMC Bioinformatics , 2012, DOI: 10.1186/1471-2105-13-50
Abstract: We introduce the design considerations and implementation details of the Bone Dysplasia Ontology. We also describe the different components of the ontology, including a comprehensive and formal representation of the skeletal dysplasia domain as well as the related genotypes and phenotypes. We then briefly describe SKELETOME, a community-driven knowledge curation platform that is underpinned by the Bone Dysplasia Ontology. SKELETOME enables domain experts to use, refine and extend and apply the ontology without any prior ontology engineering experience--to advance the body of knowledge in the skeletal dysplasia field.The Bone Dysplasia Ontology represents the most comprehensive structured knowledge source for the skeletal dysplasias domain. It provides the means for integrating and annotating clinical and research data, not only at the generic domain knowledge level, but also at the level of individual patient case studies. It enables links between individual cases and publicly available genotype and phenotype resources based on a community-driven curation process that ensures a shared conceptualisation of the domain knowledge and its continuous incremental evolution.Skeletal dysplasias are a heterogeneous group of genetic disorders affecting skeletal development. There are currently over 450 recognised types, clustered in 40 groups. Patients with skeletal dysplasias have complex medical issues including short stature, degenerative joint disease, scoliosis and neurological complications. These patients are also a precious resource for biomedical research as they enable scientists to study the effects of single genes on human bone and cartilage development and function. The resulting insights lead to a better understanding of the pathogenesis of more common connective tissue disorders such as arthritis or osteoporosis.Despite their importance, bone dysplasias are not exploited to their full potential in biomedical research. Since most conditions are rare (< 1:10'000 b
Recognizing Scientific Artifacts in Biomedical Literature
Tudor Groza, Hamed Hassanzadeh and Jane Hunter
Biomedical Informatics Insights , 2012, DOI: 10.4137/BII.S11572
Abstract: Today’s search engines and digital libraries offer little or no support for discovering those scientific artifacts (hypotheses, supporting/contradicting statements, or findings) that form the core of scientific written communication. Consequently, we currently have no means of identifying central themes within a domain or to detect gaps between accepted knowledge and newly emerging knowledge as a means for tracking the evolution of hypotheses from incipient phases to maturity or decline. We present a hybrid Machine Learning approach using an ensemble of four classifiers, for recognizing scientific artifacts (ie, hypotheses, background, motivation, objectives, and findings) within biomedical research publications, as a precursory step to the general goal of automatically creating argumentative discourse networks that span across multiple publications. The performance achieved by the classifiers ranges from 15.30% to 78.39%, subject to the target class. The set of features used for classification has led to promising results. Furthermore, their use strictly in a local, publication scope, ie, without aggregating corpus-wide statistics, increases the versatility of the ensemble of classifiers and enables its direct applicability without the necessity of re-training.
Decomposing Phenotype Descriptions for the Human Skeletal Phenome
Tudor Groza, Jane Hunter and Andreas Zankl
Biomedical Informatics Insights , 2012, DOI: 10.4137/BII.S10729
Abstract: Over the course of the last few years there has been a significant amount of research performed on ontology-based formalization of phenotype descriptions. The intrinsic value and knowledge captured within such descriptions can only be expressed by taking advantage of their inner structure that implicitly combines qualities and anatomical entities. We present a meta-model (the Phenotype Fragment Ontology) and a processing pipeline that enable together the automatic decomposition and conceptualization of phenotype descriptions for the human skeletal phenome. We use this approach to showcase the usefulness of the generic concept of phenotype decomposition by performing an experimental study on all skeletal phenotype concepts defined in the Human Phenotype Ontology.
Recognizing Scientific Artifacts in Biomedical Literature
Tudor Groza,Hamed Hassanzadeh,Jane Hunter
Biomedical Informatics Insights , 2013,
Decomposing Phenotype Descriptions for the Human Skeletal Phenome
Tudor Groza,Jane Hunter,Andreas Zankl
Biomedical Informatics Insights , 2013,
Supervised segmentation of phenotype descriptions for the human skeletal phenome using hybrid methods
Groza Tudor,Hunter Jane,Zankl Andreas
BMC Bioinformatics , 2012, DOI: 10.1186/1471-2105-13-265
Abstract: Background Over the course of the last few years there has been a significant amount of research performed on ontology-based formalization of phenotype descriptions. In order to fully capture the intrinsic value and knowledge expressed within them, we need to take advantage of their inner structure, which implicitly combines qualities and anatomical entities. The first step in this process is the segmentation of the phenotype descriptions into their atomic elements. Results We present a two-phase hybrid segmentation method that combines a series individual classifiers using different aggregation schemes (set operations and simple majority voting). The approach is tested on a corpus comprised of skeletal phenotype descriptions emerged from the Human Phenotype Ontology. Experimental results show that the best hybrid method achieves an F-Score of 97.05% in the first phase and F-Scores of 97.16% / 94.50% in the second phase. Conclusions The performance of the initial segmentation of anatomical entities and qualities (phase I) is not affected by the presence / absence of external resources, such as domain dictionaries. From a generic perspective, hybrid methods may not always improve the segmentation accuracy as they are heavily dependent on the goal and data characteristics.
Mining Skeletal Phenotype Descriptions from Scientific Literature
Tudor Groza, Jane Hunter, Andreas Zankl
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0055656
Abstract: Phenotype descriptions are important for our understanding of genetics, as they enable the computation and analysis of a varied range of issues related to the genetic and developmental bases of correlated characters. The literature contains a wealth of such phenotype descriptions, usually reported as free-text entries, similar to typical clinical summaries. In this paper, we focus on creating and making available an annotated corpus of skeletal phenotype descriptions. In addition, we present and evaluate a hybrid Machine Learning approach for mining phenotype descriptions from free text. Our hybrid approach uses an ensemble of four classifiers and experiments with several aggregation techniques. The best scoring technique achieves an F-1 score of 71.52%, which is close to the state-of-the-art in other domains, where training data exists in abundance. Finally, we discuss the influence of the features chosen for the model on the overall performance of the method.
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