A mapping from conceptual graphs to formal concept analysis

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 a 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

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

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

Validating directed graphs by applying formal concept analysis to conceptual graphs

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 assisted in reasoning with and validating their models

Discovering the gaps in enterprise systems via conceptual graphs and formal concept analysis

Enterprise systems such as SAP are software applications that are intended to bring the productivity of computers to bear on the human endeavour of enterprise. An industrial-strength SAP enterprise information model was rendered as meta-object!relation!meta-object in Conceptual Graphs (CGs). Then Formal Concept Analysis (FCA)'s CGtoFCA algorithm was used to generate the meta-objectarelation!metaobject binaries, revealing gaps in some of model's key performance indicators that human decision-makers need to realise the enterprise's vision

Conceptual structures in LEADing and best enterprise practices

Conceptual Structures, namely Conceptual Graphs (CGs) and Formal Concept Analysis (FCA) are beginning to make an impact in Industry. This is evidenced in LEAD as it seeks to provide its 3100+ industry practitioners in many Fortune 500 and public organisations with capabilities that can handle ontology and semantics. The existing ontology and semantics work in LEAD,supported by the Global University Alliance, is described and how CGs, FCA and their tools (e.g. CoGui, CG-FCA) enhance this endeavour

CGs to FCA Including Peirce's Cuts

Previous work has demonstrated a straightforward mapping from Conceptual Graphs (CGs) to Formal Concept Analysis (FCA), and the combined benefits these types of Conceptual Structures bring in capturing and reasoning about the semantics in system design. As in that work, a CGs Transaction Model (or `Transaction Graph') exemplar is used, but in the form of a richer Financial Trading (FT) case study that has its business rules visualised in Peirce's cuts. The FT case study highlights that cuts can meaningfully be included in the CGs to FCA mapping. Accordingly, the case study's CGs Transaction Graph with its cuts is translated into a form suitable for the CGtoFCA algorithm described in that previous work. The process is tested through the CG-FCA software that implements the CGtoFCA algorithm. The algorithm describes how a Conceptual Graph (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-conceptnrelation thus creating a formal context in FCA. Cuts though can now be included in the same formal, rigorous, reproducible and general way. The mapping develops the Transaction Graph into a Transaction Concept, capturing and unifying the features of Conceptual Structures that CGs and FCA collectively embody

Enhancing layered enterprise architecture development through conceptual structures

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 meta-models 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 ‘bottom-up’ 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

Environmental Scanning and Knowledge Representation for the Detection of Organised Crime Threats

ePOOLICE aims at developing an efficient and effective strategic early warning system that utilises environmental scanning for the early warning and detection of current, emergent and future organised crime threats. Central to this concept is the use of environmental scanning to detect ‘weak signals’ in the external environment to monitor and identify emergent and future threats prior to their materialization into tangible criminal activity. This paper gives a brief overview of the application of textual concept extraction and categorization, and the Semantic Web technologies Formal Concept Analysis and Conceptual Graphs as part of the systems technological architecture, describing their benefits in aiding effective early warning

25 years development of knowledge graph theory: the results and the challenge

The project on knowledge graph theory was begun in 1982. At the initial stage, the goal was to use graphs to represent knowledge in the form of an expert system. By the end of the 80's expert systems in medical and social science were developed successfully using knowledge graph theory. In the following stage, the goal of the project was broadened to represent natural language by knowledge graphs. Since then, this theory can be considered as one of the methods to deal with natural language processing. At the present time knowledge graph representation has been proven to be a method that is language independent. The theory can be applied to represent almost any characteristic feature in various languages.\ud The objective of the paper is to summarize the results of 25 years of development of knowledge graph theory and to point out some challenges to be dealt with in the next stage of the development of the theory. The paper will give some highlight on the difference between this theory and other theories like that of conceptual graphs which has been developed and presented by Sowa in 1984 and other theories like that of formal concept analysis by Wille or semantic networks