Semantic Transformation Utilizing Graphs

Semantic transformation for data in motion has been a continuous problem, and various approaches have been proposed to solve it. This paper proposes a graph approach to handle semantic data transformations. By using a set of traversal patterns, an event log will provide the feature set for both machine-learning algorithms and graph algorithms to make missing node predictions. The paper compares the two approaches and presents significant differences in results.