Catálogo de publicaciones - libros

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 15 Annual International Conference on Conceptual Structures (ICCS 2007, www.iccs2007.info).

There is a need for information, application, and other enterprise architectures which are robust and flexible enough to meet the challenges of today’s heterogeneous, rapidly changing, digitally networked environment. Developing advanced architectures may prove essential for achieving emerging research, business, and social goals. Indeed, the profoundly changed landscape suggests that a new paradigm may be needed, an (I-EA) informing architectures capable of integrating all key components and processes in an increasingly interconnected environment. To meet this challenge, a systems architectonicis outlined that is based on the trichotomic category theory of Charles S. Peirce. involves a pragmatic approach to the observation andmanipulationof diagrams as models of enterprise and inter-enterprise processes.

This invited contribution motivates the research programme, investigating the reading, writing and contesting of ideas as hypermedia networks grounded in discourse schemes. We are striving for : fluid enough to serve as augmentations to group working memory, yet structured enough to support long term memory. I will describe how such networks can be (i) mapped by multiple analysts to visualize and interrogate the claims and arguments in a literature, and (ii) mapped in real time to manage a team’s information sources, competing interpretations, arguments and decisions, particularly in time- pressured scenarios where harnessing collective intelligence is a priority. Given the current geo-political and environmental context, the growth in distributed teamwork, and the need for multidisciplinary approaches to wicked problems, there has never been a greater need for sensemaking tools to help diverse stakeholders build common ground.

We briefly give an overview of Dynamic Epistemic Logic (DEL), mainly in semantic terms. We focus on the simplest of epistemic actions in DEL, called public announcements. We also sketch the effect of more complex epistemic actions, and briefly show how als factual change can be modelled in the same framework. We then apply the logic of public announcements in DEL to the analysis of a knowledge puzzle, called ‘What Sum’.

Charles Sanders Peirce often emphasized the importance of iconic representations in all cognition. After addressing some general issues about the analysis of iconic representation, the paper examines the cognitive role of diagrammatic representations, and explores the implications for his theory of perception of Peirce’s claim that ideas are like composite photographs. The discussions also consider the role of imagination in cognition.

In this paper we propose a qualitative model to represent and reason about dynamic phenomena in a geographic space. Our model is based on linguistic cognitive archetypes, ontological definitions of geographic space and Conceptual Graphs (CGs). In a first part, we present the main concepts of the model and how we define them using CGs. In a second part, we present an overview of how this model is applied to the multiagent geosimulation domain in the context of the MAGS-COA project. Our model is original for two main reasons. First we use a linguistic approach to qualitatively model dynamic situations in a geographic environment. Second, we use CGs to represent the knowledge associated with such situations. Using CGs makes our model computationally feasible and useable to carry out spatial qualitative reasoning.

We propose in this paper an extension of Conceptual Graphs that allows to use datatypes (strings, numbers, ...) for typing concept nodes. Though the model-theoretic semantics of these datatypes is inspired by the work done for RDF/RDFS, keeping sound and complete projection-based algorithms for deduction has led to strong syntactic restrictions (datatyped concept nodes of a target graph cannot be generic). This restriction, however, allows us to smoothly upgrade our extension to rules, and to introduce functional relations (that compute the value of datatyped concept nodes) while keeping sound and complete reasonings.

Knowledge management has emerged as a field blending a systems approach with methods drawn from organizational management and learning. In contrast, knowledge representation, a branch of artificial intelligence, is grounded in formal methods. Research in the separate behavioral and the structural disciplines - knowledge management and knowledge engineering - have not traditionally cross-pollinated, preventing the development of many practical uses. Organization managers lack guidance in where to direct improvement efforts targeted at specific groups of knowledge workers. Demonstrated here is Knowledge Improvement Measurement System, an optimization solution that employs marginal utility theory in a metric space, and formal reasoning via software agents realized in conceptual graphs. This allows for repeated evaluation of knowledge improvement measurements. The KIMS method can measure activities that organize and encourage knowledge sharing to achieve competitive advantage. The solution takes into account the body of knowledge related to human understanding and learning, and formal methods of knowledge organization.

This work takes place in the general context of the construction and validation of a domain expertise. It aims at the cooperation of two kinds of knowledge, heterogeneous by their granularity levels and their formalisms: expert statements represented in the conceptual graph model and experimental data represented in the relational model. We propose to automate two stages: firstly, the generation of an ontology (terminological part of the conceptual graph model) guided both by the relational schema and by the data it contains; secondly, the evaluation of the validity of the expert statements within the experimental data, using annotated conceptual graph patterns.

This paper presents a Conceptual Graph () framework to the Generation of Referring Expressions (). Employing Conceptual Graphs as the underlying formalism allows a new rigorous, semantically rich, approach to : the intended referent is indentified by a combination of facts that can be deduced in its presence but not if it would be absent. Since s allow a substantial generalisation of the GRE problem, we show how the resulting formalism can be used by a algorithm that uniquely to objects in the scene.