Ollivier Haemmerlé

Representation of weakly structured imprecise data for fuzzy querying

In the present paper, we introduce an extension of the conceptual graph model suitable to the representation of data which are modelized using fuzzy sets. We extend the specialization relation of the conceptual graph model to fuzzy conceptual graphs. Lastly we introduce a new way of comparing conceptual graphs, using the idea that a graph may be compatible with another graph with a given degree d, which allows to make more flexible comparisons of fuzzy conceptual graphs. This work takes place within a project that aims at building a tool for the analysis of microbial risks in food products.

A semantic validation of conceptual graphs

The research works on knowledge validation aim at enhancing the quality of knowledge bases. The conceptual graph model is a knowledge representation model which belongs to the family of the semantic networks. We give a solution to validate semantically a knowledge base expressed in terms of conceptual graphs. The semantic validation of a knowledge base consists in checking that the knowledge base respects a set of constraints given by an expert. We propose to express these constraints in terms of conceptual graphs. Two categories of constraints are introduced: the existential constraints which enable one to represent pieces of knowledge that must or must not exist in the knowledge base and the descriptive constraints which enable one to describe how some pieces of knowledge must be represented in the knowledge base. The checking of these constraints by a knowledge base is done by means of the projection operation which is the ground operation of the conceptual graph model.

Different Kinds of Comparisons between Fuzzy Conceptual Graphs

In the context of a microbiological application, our study proposes to extend the Conceptual Graph Model in order to allow one: (i) to represent imprecise data and queries that include preferences, by using fuzzy sets (from fuzzy set theory) in concept vertices, in order to describe either an imprecise concept type or an imprecise referent; (ii) to query a conceptual graph that may include imprecise data (factual graph) using a conceptual graph that may include preferences (query graph). This is performed in two steps: firstly by extending the projection operation to fuzzy concepts, secondly by defining a comparison operation characterised by two matching degrees: the possibility degree of matching and the necessity degree of matching between two graphs, and particularly between a query graph and a factual graph.

Integration of Heterogeneous, Imprecise and Incomplete Data: An Application to the Microbiological Risk Assessment

This paper presents an information system developed to help the assessment of the microbiological risk in food products. UQS (Unified Querying System) is composed of two distinct bases (a relational database and a conceptual graph knowledge base) which are integrated by means of a uniform querying language. The specificity of the system is that both bases include fuzzy data. Moreover, UQS allows the expression of preferences into the queries, by means of the fuzzy set theory.

Towards flexible querying of XML imprecise data in a dataware house opened on the Web

This paper describes a new subsystem of the Sym’Previus knowledge base. This knowledge base contains information useful to help experts in the field of predictive microbiology. Information has several specific properties: it is incomplete, imprecise and heterogeneous. In the pre-existing Sym’Previus knowledge base, stable data are stored in a relational database and data which do not fit the relational structure are stored in a conceptual graph knowledge base. The MIEL language permits to scan simultaneously both bases in a transparent way for the user, using fuzzy queries. The new subsystem described in the paper contains information found on the Web to complete the knowledge base. This information is stored in XML format. Firstly, we extend the XML model of the knowledge base to represent imprecise data as possibility distributions. Secondly, we present the mapping process used to translate a MIEL query into an XML query to scan the XML knowledge base.

Towards Category-Based Fuzzy Querying of Both Structured and Semi-Structured Imprecise Data

This work presents a part of a national project which aims at building a tool for the analysis of microbial risks in food products. As a first step, we propose a querying system using fuzzy values which must be compared to imprecise information stored in the database. This category-based unified querying system works in two steps. In the first one, the category of data concerned by the query is identified in order to build two queries which will be processed on two separate databases. In the second step, both previous queries scan simultaneously a relational database and a conceptual graph knowledge base, containing microbiological information; the results from the two scans are merged in a unique table format to be shown to the user. Fuzzy values and imprecise information are managed only in the relational database in this paper. It will be extended to conceptual graph knowledge base in a future paper.

Logical interpretations of fuzzy conceptual graphs

In previous studies, we have extended the conceptual graph model, which is a knowledge representation model belonging to the family of semantic networks, to be able to represent fuzzy values. The basic conceptual graph model has a logical interpretation in first-order logic. In this paper, we focus on the logical interpretation of the conceptual graph model extended to fuzzy values: we use logical implications stemming from fuzzy logic, so as to extend the logical interpretation of the model to fuzzy values and to comparisons between fuzzy conceptual graphs.

Hierarchical Fuzzy Sets To Query Possibilistic Databases

Within the framework of flexible querying of possibilistic databases, based on the fuzzy set theory, this chapter focuses on the case where the vocabulary used both in the querying language and in the data is hierarchically organized, which occurs in systems that use ontologies. We give an overview of previous works concerning two issues: firstly, flexible querying of imprecise data in the relational model; secondly, the introduction of fuzziness in hierarchies. Concerning the latter point, we develop an aspect where there is a lack of study in current literature: fuzzy sets whose definition domains are hierarchies. Hence we propose the concept of hierarchical fuzzy set and present its properties. We present its application in the MIEL flexible querying system, for the querying of two imprecise relational databases, including user interfaces and experimental results.

Constraints for Validation of Conceptual Graphs

The works on validation propose solutions to ensure the quality of knowledge based systems. We are interested in the validation of a specific model of knowledge representation: the conceptual graph model. We present a semantic validation of conceptual graphs based on constraints given by an expert. The semantic validation of a knowledge base composed of conceptual graphs consists in checking its quality according to two kinds of constraints. The existential constraints allow one to express knowledge which must be deduced or which must absolutely not be deduced from the knowledge base. The descriptive constraints characterize the properties a conceptual graph must verify in the knowledge base. Descriptive constraints can be minimal or maximal to express the notions of “at least” and “at most”. We introduce the notion of specification which is a combination of constraints linked by logical operators. The validation of a knowledge base is made according to these specifications, by means of a conceptual graph operation: the projection.