STRIM results for OAEI 2015 instance matching evaluation

Once the entities are normalized, CroLOM uses the Yandex translator in order to trans- late the entities described in different natural languages in English as a pivot language..

Then, by analyzing the value similarity of every instance pair in training set, njuLink finds some property pairs named discriminative property pairs, which have the ability to

We believe that due to the large amount of available geo-spatial datasets employed in Linked Data and in several domains, it is critical that benchmarks for geo-spatial link

In this section, we present the results obtained by running InsMT+ on instance matching track of OAEI 2015 evaluation campaign.. 2.1 Author

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

The systems InsMT (Instance Matching at Terminological level) and InsMTL (Instance Matching at Terminological and Linguistic level) are realized for this

In the previous year, MaasMatch was unable to produce a result alignment within the set time limit for the FMA-SNOMED matching task.. This year, the system did produce an

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