Semantic Web Health Care and Life Sciences Interest Group - Publications
Notes
This document describes a consensus among participating stakeholders in the Health Care and the Life Sciences domain on the description of datasets using the Resource Description Framework (RDF). This specification meets key functional requirements, reuses existing vocabularies to the extent that it is possible, and addresses elements of data description, versioning, provenance, discovery, exchange, query, and retrieval.
The Ontology of Rhetorical Blocks is a formalization capturing the coarse-grained rhetorical structure of scientific publications. This note is designed to provide a general overview of the motivation and use-cases supporting ORB, in addition to the actual conceptual elements, as well as, practical examples of how to use it in conjunction with different representation languages.
Developing cures for highly complex diseases, such as neurodegenerative disorders, requires extensive interdisciplinary collaboration and exchange of biomedical information in context. Our ability to exchange such information across sub-specialties today is limited by the current scientific knowledge ecosystem’s inability to properly contextualize and integrate data and discourse in machine-interpretable form. This inherently limits the productivity of research and the progress toward cures for devastating diseases such as Alzheimer’s and Parkinson’s. The SWAN (Semantic Web Applications in Neuromedicine) ontology is an ontology for modeling scientific discourse and has been developed in the context of building a series of applications for biomedical researchers, as well as extensive discussions and collaborations with the larger bio-ontologies community. This document describes the SWAN ontology of scientific discourse.
As in several other scientific domains, the use of social software (such as blogs or wikis) and social networking applications is now commonly accepted in the Health Care and Life Science (HCLS) research community, with services such as the SWAN Alzheimer Knowledge Base, myExperiment, WikiProfessional Concept Web, Connotea and Nature Networks. In general, however, these applications suffer from a lack of interoperability, and this makes the reuse of information a complex task. The SIOC Ontology - Semantically-Interlinked Online Communities - aims to solve these issues and provides a comprehensive model to represent online communities and related user-generated content items thanks to Semantic Web technologies. This note describes the changes to the SIOC Core Ontology since its W3C Member Submission in June 2007, the SIOC Types Module, and their relevance in the Health Care and Life Sciences context.
This notes describes the alignment between the SWAN - Semantic Web Applications in Neuromedicine - and SIOC - Semantically-Interlinked Online Communities - ontologies, providing a complete model to represent Scientific Discourse in online communities at different levels of granularity (discourse elements and content items). The goal of this alignment is to make the discourse structure and component relationships much more accessible to computation, so that information can be navigated, compared and understood in context far better that at present, across and within domains.
The prototype we describe is a biomedical knowledge base, constructed for a demonstration at Banff WWW2007 , that integrates 15 distinct data sources using currently available Semantic Web technologies such as the W3C standard Web Ontology Language [OWL] and Resource Description Framework [RDF]. This report outlines which resources were integrated, how the knowledge base was constructed using free and open source triple store technology, how it can be queried using the W3C Recommended RDF query language SPARQL [SPARQL], and what resources and inferences are involved in answering complex queries. While the utility of the knowledge base is illustrated by identifying a set of genes involved in Alzheimer's Disease, the approach described here can be applied to any use case that integrates data from multiple domains.
One of the challenges facing Semantic Web for Health Care and Life Sciences is that of converting relational databases into Semantic Web format. The issues and the steps involved in such a conversion have not been well documented. To this end, we have created this document to describe the process of converting SenseLab databases into OWL. SenseLab is a collection of relational (Oracle) databases for neuroscientific research. The conversion of these databases into RDF/OWL format is an important step towards realizing the benefits of Semantic Web in integrative neuroscience research. This document describes how we represented some of the SenseLab databases in Resource Description Framework (RDF) and Web Ontology Language (OWL), and discusses the advantages and disadvantages of these representations. Our OWL representation is based on the reuse and extension of existing standard OWL ontologies developed in the biomedical ontology communities. The purpose of this document is to share our implementation experience with the community.