English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A System of Systems (SoS) is a complex set of IS (Information Systems) created by the aggregation and interconnection of ISs. SoS brings unexpected behavior and functionality during its construction to the benefit of its users and the SoS itself. The integration of SoSs is a matter of time. However, as SoSs can behave as an organization, it may be inappropriate to try to integrate individual IS members separately. On the other hand, manually integrating the SoS as a whole can be unfeasible due to its complexity. If an SoS has ontologies modeling the knowledge, the integration of SoSs can be translated into a problem of a network of ontologies alignment. However, it creates another challenge: computing each possible pair of entities inside each network&#x2019;s ontology can have unfeasible execution time, even using the best matchers available. In this article, we propose to mine the data from the networks using random walks and frequent item sets algorithm and discover relevant nodes elected as candidate entities. Next, the networks are pruned by an algebraic method eliminating identical entities. The relevant nodes are reinserted in the network to avoid losing essential correspondences. After the pre-processing step, data is sent to two matchers to obtain metrics and compare the results with the pairwise brute force approach and previous work. We identified relevant nodes with recall up to 0.75. The results are promising since precision and recall are closer to the force brute, and execution time is shorter, even more, when the size of the networks and the number of ontologies to be compared increases. We validate our approach using ontologies created from the OAEI (Ontology Alignment Evaluation Initiative).

Matching Network of Ontologies: A Random Walk and Frequent Itemsets Approach