While Knowledge Graphs (KG) are graph shaped by nature, most traditional data mining and machine learning (ML) software expect data in a vector form. Several node embedding techniques have been proposed to represent each node in the KG as a low-dimensional feature vector. A node embedding technique should preferably be task independent. Therefore, when a new method has been developed, it should be tested on the tasks it was designed for as well as on other tasks. We present the design and implementation of a ready to use evaluation framework to simplify the node embedding technique testing phase. The provided tests range from ML tasks, semantic tasks to semantic analogies.
A Configurable Evaluation Framework for Node Embedding Techniques
Pellegrino M. A.
;Garofalo M.;
2019
Abstract
While Knowledge Graphs (KG) are graph shaped by nature, most traditional data mining and machine learning (ML) software expect data in a vector form. Several node embedding techniques have been proposed to represent each node in the KG as a low-dimensional feature vector. A node embedding technique should preferably be task independent. Therefore, when a new method has been developed, it should be tested on the tasks it was designed for as well as on other tasks. We present the design and implementation of a ready to use evaluation framework to simplify the node embedding technique testing phase. The provided tests range from ML tasks, semantic tasks to semantic analogies.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.