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A definition for a document type within an organization represents an organizational norm about the way the organizational actors represent products and supporting evidence of organizational processes. Generating a good organization-specific document structure is, therefore, important since it can capture a shared understanding among the organizational actors about how certain business processes should be performed. Current tools that generate document type definitions focus on the underlying technology, emphasizing tags created in a single instance document. The tools, thus, fall short of capturing the shared understanding between organizational actors about how a given document type should be represented. We propose a method for inferring organization-specific document structures using multiple instance documents as inputs. The method consists of heuristics that combine individual document definitions, which may have been compiled using standard algorithms. We propose a number of heuristics utilizing artificial intelligence and natural language processing techniques. As the research progresses, the heuristics will be tested on a suite of test cases representing multiple instance documents for different document types. The complete methodology will be implemented as a research prototyp

Transitioning Existing Content: inferring organisation-specific documents