Rallou Thomopoulos

Griwes: Generic Model and Preliminary Specifications for a Graph-Based Knowledge Representation Toolkit

Griwes is an initiative to develop a common model and an open-source freeware platform shared by different graph-based frameworks. We provide an overview of its objectives, architecture and specifications. We detail some of the basic mathematical structures that are used to characterize the primitives for graph-based knowledge representation. We then propose to factorize recurrent knowledge representation primitives that can be shared across specific graph-based languages and we provide a proof of concept by showing how two languages (Simple Conceptual Graphs and RDF) can be described in this framework.

Default Conceptual Graph Rules: Preliminary Results for an Agronomy Application

In this paper, we extend Simple Conceptual Graphs with Reiters default rules. The motivation for this extension came from the type of reasonings involved in an agronomy application, namely the simulation of food processing. Our contribution is many fold: first, the expressivity of this new language corresponds to our modeling purposes. Second, we provide an effective characterization of sound and complete reasonings in this language. Third, we identify a decidable subclass of Reiters default logics. Last we identify our language as a superset of

Conceptual Graphs as Cooperative Formalism to Build and Validate a Domain Expertise

mathcal{SREC}^-

Reference Fusion and Flexible Querying

, and provide the lacking semantics for the latter language.

Representing and Reasoning about Different Viewpoints: An Agronomy Application

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.

Aide à la décision en ingénierie inverse : une approche s'appuyant sur l'argumentation

This paper deals with the issue of data fusion, which arises once reconciliations between references have been determined. The objective of this task is to fusion the descriptions of references that refer to the same real world entity so as to obtain a unique representation. In order to deal with the problem of uncertainty in the values associated with the attributes, we have chosen to represent the results of the fusion of references in a formalism based on fuzzy sets. We indicate how the confidence degrees are computed. Finally we propose a representation in Fuzzy RDF, as well as its flexible querying by queries expressing users preferences.

Ontology-driven possibilistic reference fusion

Real-world applications are often complex systems where several ways of analysing a given situation can be expressed, depending on actors viewpoints. This paper proposes a semantically sound syntactic extension to Conceptual Graphs, namely Conceptual Graph Assemblies (CGAs), that allows the representation of multiple viewpoints on the same situation. Several reasoning mechanisms, based on the projection operation, corresponding to different strength levels and adapted to multi-viewpoints situations are then demonstrated. Several modelling scenarios are then proposed and our work is put in the context of real world examples from the agri-food domain.

Artificial intelligence-based decision support system to manage quality of durum wheat products

Evaluating food quality is a complex process since it relies on numerous criteria historically grouped into four main types: nutritional, sensorial, practical and hygienic qualities. They may be completed by other emerging preoccupations such as the environmental impact, economic phenomena, etc. However, all these aspects of quality and their various components are not always compatible and their simultaneous improvement is a problem that sometimes has no obvious solution, which corresponds to a real issue for decision making. This paper proposes a decision support method in reverse engineering, i.e. guided by the objectives defined for the end products of an agrifood chain. It is materialized by a backward chaining approach based on argumentation.

Virtual Grain: a Data Warehouse for Mesh Grid Representation of Cereal Grain Properties

It often happens that different references (i.e. data descriptions), possibly coming from different heterogeneous data sources, concern the same real world entity. In such cases, it is necessary: (i) to detect, through reconciliation methods, whether different data descriptions refer to the same real world entity and (ii) to fuse them into a unique representation. Here we assume the reference reconciliation is solved, and we propose a fusion method based on possibility theory, able to cope with uncertainty and with ontological knowledge. An implementation using W3C standards is provided. Rising from the fusion process, an ontology enrichment procedure is proposed to complete the global ontology.