ALIN results for OAEI 2016

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

The results depicted in Table 47 and Figure 11 indicate that the progress made in ontology matching has also a positive impact on other related matching problems, like it is the

We will call this “recall-oriented.” Figure 1 (top) shows the results of PARIS, Soft TF-IDF, and PropString on the YAGO-DBPedia property alignment task using this definition

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

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

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

As only equivalence relationships will be evaluated in the alignment contest, we did not use this year the techniques which generate isA relationship (except in the Task 3) and