Active Semantic Relations in Layered Enterprise Architecture Development

BAXTER, M, POLOVINA, Simon, LAURIER, W and ROSING, MV (2021). Active Semantic Relations in Layered Enterprise Architecture Development. In: COCHEZ, M, CROITORU, M, MARQUIS, P and RUDOLPH, S, (eds.) Graph Structures for Knowledge Representation and Reasoning. 6th International Workshop, GKR 2020 Virtual Event, September 5, 2020 Revised Selected Papers. Lecture Notes in Artificial Intelligence (12640). Cham, Switzerland, Springer International Publishing, 3-16.

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Official URL: https://link.springer.com/chapter/10.1007/978-3-03...
Open Access URL: https://link.springer.com/content/pdf/10.1007%2F97... (Published version)
Link to published version:: https://doi.org/10.1007/978-3-030-72308-8_1
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    Abstract

    Enterprise Architecture (EA) metamodels align an organisation’s business, information and technology resources so that these assets best meet the organisation’s purpose. The Layered EA Development (LEAD) Ontology enhances EA practices by a metamodel with layered metaobjects as its building blocks interconnected by semantic relations. Each metaobject connects to another metaobject by two semantic relations in opposing directions, thus highlighting how each metaobject views other metaobjects from its perspective. While the resulting two directed graphs reveal all the multiple pathways in the metamodel, more desirable would be to have one directed graph that focusses on the dependencies in the pathways. Towards this aim, using CG-FCA (where CG refers to Conceptual Graph and FCA to Formal Concept Analysis) and a LEAD case study, we determine an algorithm that elicits the active as opposed to the passive semantic relations between the metaobjects resulting in one directed graph metamodel. We also identified the general applicability of our algorithm to any metamodel that consists of triples of objects with active and passive relations.

    Item Type: Book Section
    Additional Information: Series ISSN - 1611-3349
    Uncontrolled Keywords: Artificial Intelligence & Image Processing
    Identification Number: https://doi.org/10.1007/978-3-030-72308-8_1
    Page Range: 3-16
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 08 Jun 2021 10:19
    Last Modified: 08 Jun 2021 10:30
    URI: http://shura.shu.ac.uk/id/eprint/28722

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