Alignment results of SOBOM for OAEI 2009

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

To compare the MMSS to the MM in OAEI 2011 AgreementMaker configuration using its matchers and hierarchical LWCs, experiments are performed with the OAEI 2011 anatomy track and

When compared with all measures in both tables 2 and 3, WP had the lowest SAQ value (rank of 15). If the ranking was not identical across all OA systems, a ranking was only

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

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

Semantic similarity is used to include similarity measures that use an ontology’s structure or external knowledge sources to determine similarity between

3.1 Effect of query profile size on similarity decay First, we wanted to determine how the pattern of similarity decline varies between different query profile sizes.. We

We compared query profiles having variable proportions of matching phenotypes to subject database profiles using both pairwise and groupwise Jaccard (edge-based) and Resnik