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Modeling Narrative Structures in Logical Overlays on Top of Knowledge Repositories

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Conceptual Modeling (ER 2020)

Abstract

An important part of the scientific discourse is the exchange of knowledge in the form of stringent, well-arranged, and interconnected arguments. These ‘scientific storylines’ allow to put central entities, observations, experiments, etc. into perspective and thus ease the understanding of underlying mechanisms, dependencies, or theories. Moreover, taking a bird’s eye view allows to discern recurring narrative patterns that have proven helpful for validating, comparing, and fusing information across individual publications and even between disciplines. However, current knowledge repositories still struggle with representing such information in a structured way. This is because narratives do not only contain factual bits of information, but also parts like temporal developments, causal dependencies, etc. In this paper, we present an innovative conceptual model using a logical overlay structure to bridge the gaps between individual types of knowledge repositories. We also explain how narrative bindings validate modeled narratives in the sense of provenance. In brief, narrative overlays plus adequate bindings allow to effectively fuse knowledge and improve retrieval and discovery tasks by structurally aligning underlying repositories only driven by some narrative. Finally, we practically demonstrate the usefulness of our model by applying it to a scientific narrative in the PubMed bio-medical collection.

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Notes

  1. 1.

    https://www.w3.org/TR/rdf-sparql-query/.

  2. 2.

    https://skr3.nlm.nih.gov/SemMedDB/.

  3. 3.

    https://pubmed.ncbi.nlm.nih.gov.

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Kroll, H., Nagel, D., Balke, WT. (2020). Modeling Narrative Structures in Logical Overlays on Top of Knowledge Repositories. In: Dobbie, G., Frank, U., Kappel, G., Liddle, S.W., Mayr, H.C. (eds) Conceptual Modeling. ER 2020. Lecture Notes in Computer Science(), vol 12400. Springer, Cham. https://doi.org/10.1007/978-3-030-62522-1_18

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  • DOI: https://doi.org/10.1007/978-3-030-62522-1_18

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