Patrice Buche

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.

Fuzzy semantic tagging and flexible querying of XML documents extracted from the Web

The relational database model is widely used in real applications. We propose a way of complementing such a database with an XML data warehouse. The approach we propose is generic, and driven by a domain ontology. The XML data warehouse is built from data extracted from the Web, which are semantically tagged using terms belonging to the domain ontology. The semantic tagging is fuzzy, since, instead of tagging the values of the Web document with one value of the domain ontology, we propose to use tags expressed in terms of a possibility distribution representing a set of possible terms, each term being weighted by a possibility degree. The querying of the XML data warehouse is also fuzzy: the end-users can express their preferences by means of fuzzy selection criteria. We present our approach on a first application domain: predictive microbiology.

Fuzzy Annotation of Web Data Tables Driven by a Domain Ontology

We propose an automatic system for annotating accurately data tables extracted from the web. This system is designed to provide additional data to an existing querying system called MIEL, which relies on a common vocabulary used to query local relational databases. We will use the same vocabulary, translated into an OWL ontology, to annotate the tables. Our annotation system is unsupervised. It uses only the knowledge defined in the ontology to automatically annotate the entire content of tables, using an aggregation approach: first annotate cells, then columns, then relations between those columns. The annotations are fuzzy: instead of linking an element of the table with a precise concept of the ontology, the elements of the table are annotated with several concepts, associated with their relevance degree. Our annotation process has been validated experimentally on scientific domains (microbial risk in food, chemical risk in food) and a technical domain (aeronautics).

Fuzzy Web Data Tables Integration Guided by an Ontological and Terminological Resource

In this paper, we present the design of ONDINE system which allows the loading and the querying of a data warehouse opened on the Web, guided by an Ontological and Terminological Resource (OTR). The data warehouse, composed of data tables extracted from Web documents, has been built to supplement existing local data sources. First, we present the main steps of our semiautomatic method to annotate data tables driven by an OTR. The output of this method is an XML/RDF data warehouse composed of XML documents representing data tables with their fuzzy RDF annotations. We then present our flexible querying system which allows the local data sources and the data warehouse to be simultaneously and uniformly queried, using the OTR. This system relies on SPARQL and allows approximate answers to be retrieved by comparing preferences expressed as fuzzy sets with fuzzy RDF annotations.

A flexible bipolar querying approach with imprecise data and guaranteed results

In this paper, we propose an approach to query a database when the user preferences are bipolar (i.e., express both constraints and wishes about the desired result) and the data stored in the database are imprecise. Results are then completely ordered with respect to these bipolar preferences, giving priority to constraints over wishes. Additionally, we propose a treatment that allows us to guarantee that any query will return a result, even if no element satisfies all constraints specified by the user. Such a treatment may be useful when users constraints are unrealistic (i.e., cannot be all satisfied simultaneously) and when the user desires a guaranteed result. The approach is illustrated on a real-world problem concerning the selection of optimal packaging for fresh fruits and vegetables.

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.

Towards a Unified Querying System of Both Structured and Semi-structured Imprecise Data Using Fuzzy View

This work is part of a national project which aims at building a tool for the analysis of microbial risks in food products. As a first step of this work, we propose a unified querying system which simultaneously scans two different bases: a relational database containing structured information and a conceptual graph knowledge base containing semi-structured information. These two bases contain microbiological information. To achieve this, we propose a way of translating a database query expressed in a speci fic language into a query represented by a conceptual graph. This graph is projected into the base. It can also be generalized in order to avoid silent answers.

Classification of pig myofibres and assessment of post-mortem glycogen depletion according to fibre type by computerized image analysis

A computer-aided method for muscle fibre type determination and qualitative analysis of glycogen at a cellular level is described. The operational system consists of a microscope, a CCD videocamera, an image analysis card, a colour monitor and a standard workstation computer (32 Mb central memory, 22 mips) running under the UNIX operating system. The programme was developed with 512 × 512 pixel images. Four main steps can be distinguished: digitization, network extraction, network matching and measurement of staining intensities. The data generated for each analysed fibre included, diameter, cross-sectional area, ATPase staining intensity and type. Ten minutes of batch processing and 36-41 min of interactive work were needed to analyse 200-300 fibres. Results have shown that this image analysis system can distinguish four types of myofibres denoted I, IIA, IIB and IIC, on the basis of myosin ATPase sensitivity at three preincubation pH values (4·10, 4·35 and 10·4). Preliminary results have also shown that the image analysis system can be used to measure post-mortem glycogen depletion according to fibre type.

A Decision Support System to design modified atmosphere packaging for fresh produce based on a bipolar flexible querying approach

We define a multi-criteria Decision Support System for designing fresh food packaging.A modified atmosphere packaging simulation module is included in the DSS.A flexible querying module handles imprecise data stored in a packaging database.Using the DSS the user will have only one trial to perform validation step. To design new packaging for fresh food, stakeholders of the food chain express their needs and requirements, according to some goals and objectives. These requirements can be gathered into two groups: (i) fresh food related characteristics and (ii) packaging intrinsic characteristics. Modified Atmosphere Packaging (MAP) is an efficient way to delay senescence and spoilage and thus to extend the very short shelf life of respiring products such as fresh fruits and vegetables. Consequently, packaging O2/CO2 permeabilities must fit the requirements of fresh fruits and vegetable as predicted by virtual MAP simulating tools. Beyond gas permeabilities, the choice of a packaging material for fresh produce includes numerous other factors such as the cost, availability, potential contaminants of raw materials, process ability, and waste management constraints. For instance, the user may have the following multi-criteria query for his/her product asking for a packaging with optimal gas permeabilities that guarantee product quality and optionally a transparent packaging material made from renewable resources with a cost for raw material less than 3?/kg. To help stakeholders taking a rational decision based on the expressed needs, a new multi-criteria Decision Support System (DSS) for designing biodegradable packaging for fresh produce has been built. In this paper we present the functional specification, the software architecture and the implementation of the developed tool. This tool includes (i) a MAP simulation module combining mass transfer models and respiration of the food, (ii) a multi-criteria flexible querying module which handles imprecise, uncertain and missing data stored in the database. We detail its operational functioning through a real life case study to determine the most satisfactory materials for apricots packaging.

Flexible SPARQL Querying of Web Data Tables Driven by an Ontology

This paper concerns the design of a workflow which permits to feed and query a data warehouse opened on the Web, driven by a domain ontology. This data warehouse has been built to enrich local data sources and is composed of data tables extracted from Web documents. We recall the main steps of our semi-automatic method to annotate Web data tables driven by a domain ontology. The output of this method is an XML/RDF data warehouse composed of XML documents representing Web data tables with their fuzzy RDF annotations. We then present how to query simultaneously the local data sources and the XML/RDF data warehouse, using the domain ontology, through a flexible querying language. This language allows preferences to be expressed in selection criteria using fuzzy sets. We study more precisely how to retrieve approximate answers extracted from the Web data tables by comparing preferences expressed as fuzzy sets with fuzzy annotations using SPARQL.