![]() ![]() ![]() Also, the study found that an automated agent can be used to rank semantic similarity across ontologies constructed from graphs. Conclusionįrom the nurse’s perspective, graphs are a useful “stand-alone” knowledge acquisition tool for the visualization of the process domain and its semantics. Ontologies were constructed from graphs and the agent found no identical semantics existed across all four ontologies after the terms “patient” and “doctor” were discounted. esults: Nurses constructed node-to-arc graphs that revealed semantics and processes showing differences in clusters of “responsibility” and different foci on nursing roles. One OWL-DL (Web ontology language-description logic) ontology is constructed from each graph and an automated agent ranks semantic similarity across the four ontologies. Underpinning this pilot study is a design science framework in which a purposive sample of four specialist front-line nurses from one Australian hospital each produce one node-to-arc graph depicting a process domain and its semantics. Construct ontologies from the semantics and rank semantic similarity.Enable front-line nurses to capture their own process domain semantics.For example, they may not be a “true” reflection of the original meaning. The use of second-hand semantics may have considerable limitations. Possibly, “second-hand” semantics, that is, semantics obtained from nursing focus groups, document review or databases may be used in data sets or electronic health records as “proxy” first-hand semantics. The knowledge is underpinned by “first-hand” semantics which describe “real world” patient care processes. Tacit clinical knowledge acquired by front-line nurses over many years of practice is notoriously difficult to capture and codify. ![]()
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