SOREC: A Semantic Content-Based Recommendation System for Parsimonious Sociology Theory Construction

Theory construction is the process of formulating scientific theories with reference to explicit logical and semantic criteria. Definitions and associated terms are essential components of the theory, in which parsimony is a crucial criterion for theory evaluation. The present work offers a novel semantic content-based recommendation system with supervised machine learning model for theoretical parsimony evaluation by checking the semantic consistency of definitions while constructing theories. Specifically, we evaluate the XGBoost tree-based classifier with the combination of 15 low-level features and 11 high-level features on our dataset. A sociologist annotated in-house dataset consisting of 2,235 definition pairs drawn from the sociological literature is used for evaluating the proposed methods. The experiment results showed that the proposed system achieves 86.16% accuracy, 84.42% F-measure and 86% precision in suggesting semantically related sociological definitions.