An open annotation ontology for science on web 3.0

Abstract

BackgroundThere is currently a gap between the rich and expressive collection of published biomedical ontologies, and the natural language expression of biomedical papers consumed on a daily basis by scientific researchers. The purpose of this paper is to provide an open, shareable structure for dynamic integration of biomedical domain ontologies with the scientific document, in the form of an Annotation Ontology (AO), thus closing this gap and enabling application of formal biomedical ontologies directly to the literature as it emerges.MethodsInitial requirements for AO were elicited by analysis of integration needs between biomedical web communities, and of needs for representing and integrating results of biomedical text mining. Analysis of strengths and weaknesses of previous efforts in this area was also performed. A series of increasingly refined annotation tools were then developed along with a metadata model in OWL, and deployed for feedback and additional requirements the ontology to users at a major pharmaceutical company and a major academic center. Further requirements and critiques of the model were also elicited through discussions with many colleagues and incorporated into the work.ResultsThis paper presents Annotation Ontology (AO), an open ontology in OWL-DL for annotating scientific documents on the web. AO supports both human and algorithmic content annotation. It enables “stand-off” or independent metadata anchored to specific positions in a web document by any one of several methods. In AO, the document may be annotated but is not required to be under update control of the annotator. AO contains a provenance model to support versioning, and a set model for specifying groups and containers of annotation. AO is freely available under open source license at http://purl.org/ao/, and extensive documentation including screencasts is available on AO’s Google Code page: http://code.google.com/p/annotation-ontology/ .ConclusionsThe Annotation Ontology meets critical requirements for an open, freely shareable model in OWL, of annotation metadata created against scientific documents on the Web. We believe AO can become a very useful common model for annotation metadata on Web documents, and will enable biomedical domain ontologies to be used quite widely to annotate the scientific literature. Potential collaborators and those with new relevant use cases are invited to contact the authors.

Theophrastus: On demand and real-time automatic annotation and exploration of (web) documents using open linked data

“…It supports fully automated, semi-automated, and manual biomedical annotation with full representation of the provenance of annotations, as well as personal or community annotations with authorization and access control. Annotations are represented using the Annotation Ontology (AO) RDF model [12]. However, Domeo is currently being extended to also support the Open Annotation Data Model [22].…”

Biotea-Biolinks: A semantic infrastructure for exploring and analyzing scientific publications

“…Como resultado del análisis de ontologías existentes para representar conceptos asociados a términos y expresiones embebidas en textos científicos, se llevó a cabo una colaboración con el proyecto Annotation Ontology [4]. Este proyecto ha sido liderado desde sus inicios por Paolo Ciccarese y Timothy Clark de la Escuela de Medicina de la Universidad de Harvard y el Hospital General de Massachusetts.…”

“…The extracted entities are mapped to well-known ontologies in the Life Sciences domain and modelled as annotations. In order to semantically represent such annotations, we collaborate with the Annotation Ontology project [4]. This project has been led by Paolo Ciccarese and Timothy Clark from the Medicine School in the Harvard University and the Massachusetts General Hospital.…”

“…Additional to the model, algorithms to transform publications available in the Journal Article Tag Suite (JATS) to RDF were also defined. These models and algorithms 4 constitute to the first part of this thesis and correspond to the project named Biotea. [1].…”

“…Our RDFization process transcends the LS domain as it can be applied to scientific publications on any domain. In order to show how the access to relevant data can be improved 4 More information regarding the models and algorithms are available in GitHub (https://github.com/biotea) 5 Open Access files provided by PubMed Central can be downloaded at http://www.ncbi.nlm.nih.gov/pmc/tools/ftp/ while the corresponding RDF/XML files can be found at http://doi.org/10.5281/zenodo.376814 once the publications are part of the LOD, we focused on finding additional literature related to an already known publication, a reference article. Biolinks is our contribution in this regard [6,7], which constitutes the second part of this thesis.…”