Exploring, Reasoning with and Validating Directed Graphs by Applying Formal Concept Analysis to Conceptual Graphs

Self Cite

Enterprise Architecture (EA) enables organisations to align their information technology with their business needs. Layered EA Development (LEAD) enhances EA by using meta-models made up of layered meta-objects, interconnected by semantic relations. Organisations can use these meta-models to benefit from a novel, ontology-based, object-oriented way of EA thinking and working. Furthermore, the metamodels are directed graphs that can be read linearly from a Top Down View (TDV) or a Bottom Up View (BUV) perspective. Conceptual Structures through CG-FCA (where CG refers to Conceptual Graph and FCA to Formal Concept Analysis) is thus used to traverse the TDV and BUV directions using the LEAD Industry 4.0 meta-model as an illustration. The motivation for CG-FCA is stated. It is discovered that CG-FCA: a) identifies any unwanted cycles in the 'top-down' or 'bottomup' directions, and b) conveniently arranges the many pathways by which the meta-models can be traversed and understood in a Formal Concept Lattice. Through the LEAD meta-model exemplar, the wider appeal of CG-FCA and directed graphs are also identified.

Section: The Meta-model Diagrammentioning

confidence: 99%

Section: The Meta-model Diagrammentioning

confidence: 99%

Section: Discovering Pathwaysmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Layered Enterprise Architecture Development Through Conceptual Structures

Polovina

Laurier

Etzel³

2019

Self Cite

“…A second form of conceptual structure known as Formal Concept Analysis (FCA) which is used in information science [5]. FCA provides an objective mathematical interpretation of CGs' logical but subjective human interpretations and is brought to bear through the CGtoFCA algorithm [1]. A Formal Concept in FCA is the result of when certain conditions are met in a formal context:…”

Section: Conceptual Structuresmentioning

confidence: 99%

“…Manual inspection of the CG makes identifying these much harder, especially for the much more comprehensive meta-models encountered in practice than the simple illustration given in Table 1. And we haven't accounted for discovering and rectifying errors in drawing the CGs, such as the arrows in the CGs pointing in the wrong direction or mistyped concepts as simple examples from other work have demonstrated [1,4].…”

Section: The Business Layermentioning

confidence: 99%

Using Conceptual Structures in Enterprise Architecture to Develop a New Way of Thinking and Working for Organisations

Polovina

2018

Self Cite

Enterprise Architecture (EA) is a discipline that provides generic patterns that any organisation can reuse throughout its own business, informatics and technical components. However, EA's current way of thinking and working to achieve this aim is not standardised. EA thus continues to "reinvent the wheel" that causes mistakes or wastes resources on rediscovering what should already be known. We, therefore, represent the specific business, information and technology meta-models as patterns that can be fully reintegrated in one repeatable meta-model for the whole organisation. The outcome is a new agile way of thinking and working, highlighted by how EA works better in enterprise layers, sub-layers and levels of abstraction. To test the meta-models, two forms of Conceptual Structures known as Conceptual Graphs (CGs) and Formal Concept Analysis (FCA) are brought together through the CGtoFCA algorithm. The algorithm identifies how the layered metamodels can share meaning and truth and without having to recombine them into one large, unwieldy meta-model as the repeatable structure.

Ontology-Informed Lattice Reduction Using the Discrimination Power Index

Quboa

Behnaz

Mehandjiev

et al. 2019

The increasing reliance on data for decision making has led to a number of techniques for automatic knowledge acquisition such as Formal Concept Analysis (FCA). FCA creates a lattice comprising partial order relationships between sets of object instances in a domain (extent) and their properties (intent). This is mapped onto a semantic knowledge structure comprising domain concepts with their instances and properties. However, this automatic extraction of structure from a large number of instances usually leads to a lattice which is too complex for practical use. Algorithms to reduce the lattice exist. However, these mainly rely on the lattice structure and are agnostic about any prior knowledge about the domain. In contrast, this paper uses existing domain knowledge encoded in a semantic ontology and a novel relevance index to inform the reduction process. We demonstrate the utility of the proposed approach, achieving a significant reduction of lattice nodes, even when the ontology only provides partial coverage of the domain of interest.