Prediction of Missing Semantic Relations in Lexical-Semantic Network using Random Forest Classifier

TL;DR

This paper presents a method using Random Forest classifiers to predict missing semantic relations in a French lexical-semantic network, leveraging node2vec for feature extraction and crowdsourced data for training.

Contribution

It introduces a novel approach combining node2vec and Random Forests to predict missing semantic relations in a lexical network, validated with crowdsourced data.

Findings

Achieved acceptable prediction accuracy for missing relations.

Demonstrated effectiveness of node2vec features in semantic relation prediction.

Validated approach with crowdsourced dataset.

Abstract

This study focuses on the prediction of missing six semantic relations (such as is_a and has_part) between two given nodes in RezoJDM a French lexical-semantic network. The output of this prediction is a set of pairs in which the first entries are semantic relations and the second entries are the probabilities of existence of such relations. Due to the statement of the problem we choose the random forest (RF) predictor classifier approach to tackle this problem. We take for granted the existing semantic relations, for training/test dataset, gathered and validated by crowdsourcing. We describe how all of the mentioned ideas can be followed after using the node2vec approach in the feature extraction phase. We show how this approach can lead to acceptable results.