Semantic Framework and Deep Learning Toolkit Collaboration for the Enhancement of the Decision Making in Agent-Based Marketplaces

Abstract

Collaborative manufacturing ecosystems provide a high volume of data, collected from factories and commonly related to machine data, sensor measurements and production processes. Wide variations are noted in these data alongside with the data related to the supply-chain and transactions over the aforementioned ecosystems. Semantics and ontologies are commonly used in order to bridge variances in datasets. Furthermore, deep learning techniques perform different kind of analyses over such high volumes of data. This paper introduces a collaboration scheme between a Semantic Framework and a Deep Learning Toolkit. More precisely, this work describes how the ecosystem’s data were modeled and stored using ontologies, became available and analyzed by the continuous learning algorithms of the Deep Learning Toolkit and finally how they are sent back to the Semantic Framework, enhancing a semantic matchmaker’s efficiency in order to support the automated decision making inside the collaborative ecosystem.