Archive for October, 2014

Genre defined, a quote from John Swales

October 21st, 2014

A genre comprises a class of communicative events, the members of which share some set of communicative purposes. These purposes are recognized by the expert members of the parent discourse community and thereby constitute the rationale for the genre. This rationale shapes the schematic structure of the discourse and influences and constrains choice of content and style. Communicative purpose is both a privileged criterion and one that operates to keep the scope of a genre as here conceived narrowly focused on comparable rhetorical action. In addition to purpose, exemplars of a genre exhibit various patterns of similarity in terms of structure, style, content and intended audience. If all high probability expectations are realized, the exemplar will be viewed as prototypical by the parent discourse community. The genre names inherited and produced by discourse communities and imported by others constitute valuable ethnographic communication, but typically need further validation.1

  1. Genre defined, from John M. Swales, page 58, Chapter 3 “The concept of genre” in Genre Analysis: English in academic and research settings. Cambridge University Press 1990. Reprinted with other selections in
    The Discourse Studies Reader: Main currents in theory and analysis (see pages 305-316). []

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Linked Science 2014 paper: Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base

October 19th, 2014

Today I’m presenting a talk in the ISWC 2014 Workshop on Linked Science 2014—Making Sense Out of Data (LISC2014). The LISC2014 paper is joint work with Paolo Ciccarese, Tim Clark and Richard D. Boyce. Our goal is to make the evidence in a scientific knowledge base easier to access and audit — to make the knowledge base easier to maintain as scientific knowledge and drug safety regulations change. We are modeling evidence (data, methods, materials) from biomedical communications in the medication safety domain (drug-drug interactions).

The new architecture for the drug-drug interaction knowledge base is based on:

This is part of a 4-year National Library of Medicine project, “Addressing gaps in clinically useful evidence on drug-drug interactions” (1R01LM011838-01)

Abstract of our paper, “Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base.”:

Semantic web technologies can support the rapid and transparent validation of scientific claims by interconnecting the assumptions and evidence used to support or challenge assertions. One important application domain is medication safety, where more efficient acquisition, representation, and synthesis of evidence about potential drug-drug interactions is needed. Exposure to potential drug-drug interactions (PDDIs), defined as two or more drugs for which an interaction is known to be possible, is a significant source of preventable drug-related harm. The combination of poor quality evidence on PDDIs, and a general lack of PDDI knowledge by prescribers, results in many thousands of preventable medication errors each year. While many sources of PDDI evidence exist to help improve prescriber knowledge, they are not concordant in their coverage, accuracy, and agreement. The goal of this project is to research and develop core components of a new model that supports more efficient acquisition, representation, and synthesis of evidence about potential drug-drug interactions. Two Semantic Web models—the Micropublications Ontology and the Open Annotation Data Model—have great potential to provide linkages from PDDI assertions to their supporting evidence: statements in source documents that mention data, materials, and methods. In this paper, we describe the context and goals of our work, propose competency questions for a dynamic PDDI evidence base, outline our new knowledge representation model for PDDIs, and discuss the challenges and potential of our approach.

Citation: Schneider, Jodi, Paolo Ciccarese, Tim Clark, and Richard D. Boyce. “Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base.” Linked Science 2014 at ISWC 2014.

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