Lily results for OAEI 2015

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

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

The OMReasoner is unique in that it creates an extensible framework for combination of multiple individual matchers, and reasons about ontology matching by using

Given two consistent ontologies, KOSIMap aligns entities in the source ontology to entities in the target ontology by extracting background knowledge about entities based on

To solve the matching problem without rich literal information, a similarity propagation matcher with strong propagation condition (SSP matcher) is presented, and the

Secondly, SOBOM utilizes Semantic Inductive Similarity Flooding algorithm to compute the similarity of the concepts between the sub-ontologies derived from the two

The second stage of AROMA discovers the subsumption relations by using the association rule model and the implication intensity measure [Gras et al., 2008]. an implication allowing

The Automated Semantic Mapping of Ontologies with Validation (ASMOV) algorithm for ontology alignment was one of the top performing algorithms in the 2007