Fabien Amarger

Agronomic taxon

In this paper, we describe the development of the first ontology module for observation of pest attacks in crop production. We applied the NeOn methodology and more particularly the ontology engineering method based on Ontology Design Pattern.

Taking SPARQL 1.1 Extensions into Account in the SWIP System

The SWIP system aims at hiding the complexity of expressing a query in a graph query language such as SPARQL. We propose a mechanism by which a query expressed in natural language is translated into a SPARQL query. Our system analyses the sentence in order to exhibit concepts, instances and relations. Then it generates a query in an internal format called the pivot language. Finally, it selects pre-written query patterns and instantiates them with regard to the keywords of the initial query. These queries are presented by means of explicative natural language sentences among which the user can select the query he/she is actually interested in. We are currently focusing on new kinds of queries which are handled by the new version of our system, which is now based on the 1.1 version of SPARQL.

SKOS Sources Transformations for Ontology Engineering: Agronomical Taxonomy Use Case

Sources like thesauri or taxonomies are already used as input in ontology development process. Some of them are also published on the LOD using the SKOS format. Reusing this type of sources to build an ontology is not an easy task. The ontology developer has to face different syntax and different modelling goals. We propose in this paper a new methodology to transform several non-ontological sources into a single ontology. We take into account: the redundancy of the knowledge extracted from sources in order to discover the consensual knowledge and Ontology Design Patterns (ODPs) to guide the transformation process. We have evaluated our methodology by creating an ontology on wheat taxonomy from three sources: Agrovoc thesaurus, TaxRef taxonomy, NCBI taxonomy.

Cross-Querying LOD Datasets Using Complex Alignments: An Application to Agronomic Taxa

Farmers have new information needs to change their agricultural practices. The Linked Open Data is a considerable source of knowledge, separated into several heterogeneous and complementary datasets. This paper presents a process to query LOD datasets from a known ontology using complex alignments. The approach was applied on AgronomicTaxon, a taxonomic classification ontology, to query Agrovoc and DBpedia.

Knowledge Engineering Method Based on Consensual Knowledge and Trust Computation: The MUSCKA System

We propose a method for building a knowledge base addressing specific issues such as covering end-users’ needs. After designing an ontology module representing the knowledge needed, we enrich and populate it automatically with knowledge extracted from existing sources such as thesauri or classifications. The originality of our proposition is to propose ontological object candidates from existing sources according to their relatedness to the ontological module and to their trust score. This paper describes the trust measures we propose which are obtained by analysing the consensus found in existing sources. We consider that knowledge is more reliable if it has been extracted from several sources. Our measures has been evaluated on a real case study with experts from the agriculture domain.

Dealing with Incompatibilities During a Knowledge Bases Fusion Process

More and more data sets are published on the linked open data. Reusing these data is a challenging task as for a given domain, several data sets built for specific usage may exist. In this article we present an approach for existing knowledge bases fusion by taking into account incompatibilities that may appear in their representations. Equivalence mappings established by an alignment tool are considered in order to generate a subset of compatible candidates. The approach has been evaluated by domain experts on datasets dealing with agriculture.