This paper proposes a collaborative methodology for developing semantic data models. The proposed methodology for the semantic model development follows a "meet-in-the-middle" approach. On the one hand, the concepts emerged in a bottom-up fashion from analyzing the domain and interviewing the domain experts regarding their data needs. On the other hand, it followed a top-down approach whereby existing ontologies, vocabularies and data models were analyzed and integrated with the model. The identified elements were then fed to a multiphase abstraction exercise in order to get the concepts of the model. The derived model is also evaluated and validated by domain experts. The methodology is applied on the creation of the Cancer Chemoprevention semantic model that formally defines the fundamental entities used for annotating and describing interconnected cancer chemoprevention related data and knowledge resources on the Web. This model is meant to offer a single point of reference for biomedical researchers to search, retrieve and annotate linked cancer chemoprevention related data and web resources. The model covers four areas related to Cancer Chemoprevention: i) concepts from the literature that refer to cancer chemoprevention, ii) facts and resources relevant for cancer prevention, iii) collections of experimental data, procedures and protocols and iv) concepts to facilitate the representation of results related to virtual screening of chemopreventive agents.
BackgroundThe value and usefulness of data increases when it is explicitly interlinked with related data. This is the core principle of Linked Data. For life sciences researchers, harnessing the power of Linked Data to improve biological discovery is still challenged by a need to keep pace with rapidly evolving domains and requirements for collaboration and control as well as with the reference semantic web ontologies and standards. Knowledge organization systems (KOSs) can provide an abstraction for publishing biological discoveries as Linked Data without complicating transactions with contextual minutia such as provenance and access control.We have previously described the Simple Sloppy Semantic Database (S3DB) as an efficient model for creating knowledge organization systems using Linked Data best practices with explicit distinction between domain and instantiation and support for a permission control mechanism that automatically migrates between the two. In this report we present a domain specific language, the S3DB query language (S3QL), to operate on its underlying core model and facilitate management of Linked Data.ResultsReflecting the data driven nature of our approach, S3QL has been implemented as an application programming interface for S3DB systems hosting biomedical data, and its syntax was subsequently generalized beyond the S3DB core model. This achievement is illustrated with the assembly of an S3QL query to manage entities from the Simple Knowledge Organization System. The illustrative use cases include gastrointestinal clinical trials, genomic characterization of cancer by The Cancer Genome Atlas (TCGA) and molecular epidemiology of infectious diseases.ConclusionsS3QL was found to provide a convenient mechanism to represent context for interoperation between public and private datasets hosted at biomedical research institutions and linked data formalisms.
Abstract. PPEPR is software to connect healthcare enterprises. Healthcare is a complex domain and any integration system that connects healthcare enterprise applications must facilitate heterogeneous healthcare systems at all levels -data, services, processes, healthcare vendors, standards, legacy systems, and new information systems, all of which must interoperate to provide healthcare services. The lack of interoperability within healthcare standards (e.g. HL7) adds complexity to the interoperability initiatives. HL7's user base has been growing since the early 2000s. There are many interoperability issues between the widely adopted HL7 v2 and its successor, HL7 v3, in terms of consistency, data/message modeling, precision, and useability. We have proposed an integration platform called PPEPR: (Plug and Play Electronic Patient Records) which is based on a semantic Service-oriented Architecture (sSOA). PPEPR connects HL7 (v2 & v3) compliant healthcare enterprises. Our main goal is to provide seamless integration between healthcare enterprises without imposing any constraint on existing or proposed EPRs.
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