Different Kinds of Comparisons between Fuzzy Conceptual Graphs

Thomopoulos, Rallou; Buche, Patrice; Haemmerlé, Ollivier

doi:10.1007/978-3-540-45091-7_4

Cited by 15 publications

(12 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The only difference is the comparison operator we use between concept vertices. But we show in (Thomopoulos et al, 2003a) that our algorithm remains polynomial. That is the reason why we limit the view graphs (and then the query graphs) to acyclic conceptual graphs (see Definition 8).…”

Section: Query Processingmentioning

confidence: 84%

“…In (Thomopoulos et al, 2003a), we proposed different kinds of comparisons between fuzzy conceptual graphs (like specialization or more flexible comparisons). In the conceptual graph subsystem of the MIEL system, the query processing consists in searching for conceptual graphs which contain a more precise information than the information contained in the query (we search for specializations of the query graph) or, at least, for conceptual graphs which contain "approximate" answers.…”

Section: Query Processingmentioning

confidence: 99%

See 1 more Smart Citation

The MIEL system: Uniform interrogation of structured and weakly-structured imprecise data

2007

Self Cite

View full text Add to dashboard Cite

Abstract.We present an information system developed to help assessing the microbiological risk in food. That information system contains experimental results in microbiology, mainly extracted from scientific publications. The increasing amount of the experimental results available and the difficulty to integrate them into a classic relational database schema led us to design a system composed of two distinct subsystems queried through a common interface. The first subsystem is a classic relational database. The second subsystem is a database containing weakly-structured pieces of information expressed in terms of conceptual graphs. The data stored in both bases can be fuzzy ones in order to take into account the specificities of the biological information. The uniform query language used on both relational database and conceptual graph database allows the users to express preferences by using fuzzy sets in their queries. The MIEL system is now operational and used by the microbiologists involved in the Sym'Previus French project.

show abstract

Section: Query Processingmentioning

confidence: 84%

Section: Query Processingmentioning

confidence: 99%

The MIEL system: Uniform interrogation of structured and weakly-structured imprecise data

2007

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthur to previous studies [1,2], expressing preferences using fuzzy sets [12] in an unambiguous way implies that the definition domains of these fuzzy sets were exhaustive and composed of exclusive elements. Both properties are characteristics of partitions.…”

Section: Clarifying the Semantics Of Preferencesmentioning

confidence: 95%

“…Previous results [1,2] concerning the expression of preferences in an ontology led to two issues: firstly, the need to simplify graphical user interface, and secondly, the necessity of expressing preferences on domains composed of exclusive and exhaustive elements. Therefore in Section 2 we introduce the notion of viewpoint in an ontology.…”

Section: Introductionmentioning

confidence: 99%

Expressing Preferences in a Viewpoint Ontology

Thomopoulos

2005

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…A MIEL++ query is an instanciation of a view by the end-user, who specify among the set of querryable attributes of the view what are the selection attributes and their searched values, and what are the projection attributes (with the meaning of the relational model). An important specificity of a MIEL++ query is that searched values may be expressed as fuzzy sets (see [8][9][10]), which use allows end-users to represent their preferences in a gradual way.…”

Section: Sparql Querying Of Rdf Graphsmentioning

confidence: 99%

Data Reliability Assessment in a Data Warehouse Opened on the Web

Destercke

Buche

Charnomordic

2011

Flexible Query Answering Systems

Self Cite

View full text Add to dashboard Cite

Abstract. This paper presents an ontology-driven workflow that feeds and queries a data warehouse opened on the Web. Data are extracted from data tables in Web documents. As web documents are very heterogeneous in nature, a key issue in this workflow is the ability to assess the reliability of retrieved data. We first recall the main steps of our method to annotate and query Web data tables driven by a domain ontology. Then we propose an original method to assess Web data table reliability from a set of criteria by the means of evidence theory. Finally, we show how we extend the workflow to integrate the reliability assessment step.

show abstract

Different Kinds of Comparisons between Fuzzy Conceptual Graphs

Cited by 15 publications

References 8 publications

The MIEL system: Uniform interrogation of structured and weakly-structured imprecise data

The MIEL system: Uniform interrogation of structured and weakly-structured imprecise data

Expressing Preferences in a Viewpoint Ontology

Data Reliability Assessment in a Data Warehouse Opened on the Web

Contact Info

Product

Resources

About