This paper provides a lucid introduction to Conceptual Graphs (CG), a powerful knowledge representation and inference environment that exhibits the familiar object-oriented features of contemporary enterprise and web applications. An illustrative business case study is used to convey how CG adds value to data, including inference for new knowledge. It enables newcomers to conceptual structures to engage with this exciting field and to realise "Conceptual Structures: Knowledge Architectures for Smart Applications", the theme of the

Although tools exist to aid practitioners in the construction of directed graphs typified by Conceptual Graphs (CGs), it is still quite possible for them to draw the wrong model, mistakenly or otherwise. In larger or more complex CGs it is furthermore often difficult-without close inspection-to see clearly the key features of the model. This paper thereby presents a formal method, based on the exploitation of CGs as directed graphs and the application of Formal Concept Analysis (FCA). FCA elucidates key features of CGs such as pathways and dependencies, inputs and outputs, cycles, and joins. The practitioner is consequently empowered in exploring, reasoning with and validating their real-world models. Exploring, Reasoning with and Validating Directed Graphs by Applying Formal Concept Analysis to Conceptual Graphs

A straightforward mapping from Conceptual Graphs (CGs) to Formal Concept Analysis (FCA) is presented. It is shown that the benefits of FCA can be added to those of CGs, in, for example, formally reasoning about a system design. In the mapping, a formal attribute in FCA is formed by combining a CG source concept with its relation. The corresponding formal object in FCA is the corresponding CG target concept. It is described how a CG, represented by triples of the form source-concept, relation, target-concept, can be transformed into a set of binary relations of the form (target-concept, source-concept relation) creating a formal context in FCA. An algorithm for the transformation is presented and for which there is a software implementation. The approach is compared to that of Wille. An example is given of a simple University Transaction Model (TM) scenario that demonstrates how FCA can be applied to CGs, combining the power of each in an integrated and intuitive way.

The development of meta-models in Enterprise Modelling, Enterprise Engineering, and Enterprise Architecture enables an enterprise to add value and meet its obligations to its stakeholders. This value is however undermined by the complexity in the meta-models which have become difficult to visualise thus deterring the human-driven process. These experiences have driven the development of layers and levels in the modular meta-model. Conceptual Structures (CS), described as “Information Processing in Mind and Machine”, align the way computers work with how humans think. Using the Enterprise Information Meta-model Architecture (EIMA) as an exemplar, two forms of CS known as Conceptual Graphs (CGs) and Formal Concept Analysis (FCA) are brought together through the CGtoFCA algorithm, thereby mathematically evaluating the effectiveness of the layers and levels in these meta-models. The work reveals the useful contribution that this approach brings in actualising the modularising of complex meta-models in enterprise systems using conceptual structures.

Many enterprises risk business transactions based on information systems that