IEEE Internet Computing
Supporting Collaborative Ontology Development in Protégé
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
OntoWiki – a tool for social, semantic collaboration
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Flexible SPARQL Querying of Web Data Tables Driven by an Ontology
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Converting and annotating quantitative data tables
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
A flexible bipolar querying approach with imprecise data and guaranteed results
Fuzzy Sets and Systems
An ontology-based method for duplicate detection in web data tables
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
An ontological and terminological resource for n-ary relation annotation in web data tables
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part II
Data reliability assessment in a data warehouse opened on the web
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
Controlled knowledge base enrichment from web documents
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Entity discovery and annotation in tables
Proceedings of the 16th International Conference on Extending Database Technology
Scalable column concept determination for web tables using large knowledge bases
Proceedings of the VLDB Endowment
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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).