RiMOM results for OAEI 2015

SLINT+ performs four steps interlinking including predicate selection, predicate alignment, candidate generation, and instance matching. SLINT+ gives a promising result at the

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

For Instance Matching, our main idea is that we classify individuals by their classes, complete information of each individual as complete as possible, run matching algorithm for

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

the predicate constants relating to the type of techniques applied in the identification of a mapping by TaxoMap, the predicate constants expressing structural relations

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

Then we give new features in instance matching compared with traditional ontology matching (schema matching) and introduce the specific techniques we used for the 3 different

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