Lily Results for OAEI 2019

The ultimate goal is to incorporate methods that exploit different types of infor- mation available (lexical, semantic, structural) at various settings

We combine do- main specific representations of physical science (e.g. CML, Chemical Markup Language), MathML formulae and computational specifications (DeXML) to create

CANARD Complex Matching System: Results of the 2019 OAEI Evaluation Campaign?. Elodie Thi´ eblin, Ollivier Haemmerl´ e,

In The 4th International Workshop on Ontology Matching, Washington Dc., USA (2009) [2] Peng Wang, Baowen Xu: Lily: Ontology Alignment Results for OAEI 2008.

Generic ontology matching method The similarity computation is based on the semantic subgraphs, which means all the information used in the simi- larity computation comes from

In The 4th International Workshop on Ontology Matching, Washington Dc., USA (2009) [2] Peng Wang, Baowen Xu: Lily: Ontology Alignment Results for OAEI 2008.

The availability of the OAEI test cases has revealed that MAMBA needs to be signif- icantly improved to become a robust matching systems instead of being just a set of scripts that

Lily has three main matching functions: (1) Generic Ontology Matching (GOM) is used for common matching tasks with normal size ontologies.. (2) Large scale