Conceptual Graphs in CAD

This paper elaborates on the use of conceptual graphs in a prototype of a computer based support system for re-design. Re-design support involves the modelling of assemblies and components. The requirements of the components to be modelled are a compromise between the functioning of the assembly and the manufacturability of the individual components. Conceptual graphs provide for an elegant way of representing both functioning and manufacturing aspects. In the prototype system, conceptual graphs are used for representing and defining assemblies, components and features as well as the relations between these entities. Constraints, such as kinematic, tolerance and manufacturing constraints are also represented using conceptual graphs

Semantics representation in a sentence with concept relational model (CRM)

The current way of representing semantics or meaning in a sentence is by using the conceptual graphs. Conceptual graphs define concepts and conceptual relations loosely. This causes ambiguity because a word can be classified as a concept or relation. Ambiguity disrupts the process of recognizing graphs similarity, rendering difficulty to multiple graphs interaction. Relational flow is also altered in conceptual graphs when additional linguistic information is input. Inconsistency of relational flow is caused by the bipartite structure of conceptual graphs that only allows the representation of connection between concept and relations but never between relations per se. To overcome the problem of ambiguity, the concept relational model (CRM) described in this article strictly organizes word classes into three main categories; concept, relation and attribute. To do so, CRM begins by tagging the words in text and proceeds by classifying them according to a predefi ned mapping. In addition, CRM maintains the consistency of the relational flow by allowing connection between multiple relations as well. CRM then uses a set of canonical graphs to be worked on these newly classified components for the representation of semantics. The overall result is better accuracy in text engineering related task like relation extraction

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

The suitabilty of conceptual graphs in strategic management accountancy

The hypothesis of the research is "conceptual graphs are a suitable knowledge-base decision support tool for use by managemenat ccountants in strategic planning", explained as follows. Knowledge-based approaches can help accountants apply their skills in the direction of strategic management problems. Such problem domains cannot be modelled effectively by computer alone, hence we are only interested in those advanced knowledge-based methodologies that can be adequately reviewed by strategic management accountants in the light of their own continually changing tacit and implicit knowledge. Structured diagram techniques, such as flowcharting, are well known by accountants and are a clearly understandable yet important aid in problem review. Apart from being founded on a logically complete reasoning system, the knowledge-based methodology of conceptual graphs was formulated to be an enhancement of these other methods. Furthermore the graphical form of conceptual graphs enjoy an apparent similarity to the 'negating' brackets in the accountant's traditional bookkeeping model. After conducting a comparative study with two similar methodologies in current use showing the technical advantages of conceptual graphs, the Conceptual Analysis and Review Environment computer software was devised and implemented. CARE was used to test the - accepted graphical form of conceptual graphs through a series of user evaluation sessions. The evaluations started out with subjects from the conceptual graphs community itself, then key business school staff, and culminated in a session with senior practising accountants. In addition, CARE was enhanced iteratively in accordance with the results of each evaluation session. Despite their strong prima facie attractiveness and positive response from the conceptual graphs community session, as the user evaluations progressed it became increasingly evident that the inherent complexity of conceptual graphs fundamentally undern-tined them as a viable tool, other than for very trivial problems well below the level needed to be viable for strategic management accountancy. Therefore the original contribution of this research is that its hypothesis turns out to be false

RDF to Conceptual Graphs Translations

International audienceIn this paper we will discuss two different translations between RDF (Resource Description Format) and Conceptual Graphs (CGs). These translations will allow tools like Cogui and Cogitant to be able to import and export RDF(S) documents. The first translation is sound and complete from a reasoning view point but is not visual nor a representation in the spirit of Conceptual Graphs (CGs). The second translation has the advantage of being natural and fully exploiting the CG features, but, on the other hand it does not apply to the whole RDF(S). We aim this paper as a preliminary report of ongoing work looking in detail at different pro and the cons of each approach

Extensions of Simple Conceptual Graphs: the Complexity of Rules and Constraints

Simple conceptual graphs are considered as the kernel of most knowledge representation formalisms built upon Sowa's model. Reasoning in this model can be expressed by a graph homomorphism called projection, whose semantics is usually given in terms of positive, conjunctive, existential FOL. We present here a family of extensions of this model, based on rules and constraints, keeping graph homomorphism as the basic operation. We focus on the formal definitions of the different models obtained, including their operational semantics and relationships with FOL, and we analyze the decidability and complexity of the associated problems (consistency and deduction). As soon as rules are involved in reasonings, these problems are not decidable, but we exhibit a condition under which they fall in the polynomial hierarchy. These results extend and complete the ones already published by the authors. Moreover we systematically study the complexity of some particular cases obtained by restricting the form of constraints and/or rules

Enterprise model verification and validation : an approach

This article presents a verification and validation approach which is used here in order to complete the classical tool box the industrial user may utilize in enterprise modeling and integration domain. This approach, which has been defined independently from any application domain is based on several formal concepts and tools presented in this paper. These concepts are property concepts, property reference matrix, properties graphs, enterprise modeling domain ontology, conceptual graphs and formal reasoning mechanisms

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Using Conceptual Graphs to Represent Database Inference Security Analysis

This paper describes an approach to database inference analysis based on conceptual graphs. The database inference problem is briefly described. Previous approaches are summarized, followed by a presentation of our inference model, called AERIE. The notions of an inference target class and an inference method class are introduced with examples given. Conceptual graphs are introduced as our means of representing database inference knowledge, as a first step toward analyzing and detecting database inference problems. The classification of inference target classes and the use of conceptual graphs for database inference detection are two important contributions of this paper. Four examples are used to illus trate the approach. We discuss some interesting issues raised by this work, and offer conclusions and our plans for future research