Clustering Semantic Predicates in the Open Research Knowledge Graph

TL;DR

This paper presents AI-based clustering algorithms to recommend predicates in the Open Research Knowledge Graph, promoting vocabulary convergence and improving semantic annotation of scholarly data across multiple research fields.

Contribution

It introduces tailored clustering methods for predicate recommendation in ORKG, enhancing terminology convergence and providing insights into semantic patterns across disciplines.

Findings

High precision and recall in predicate clustering

Linear runtime performance of algorithms

Identification of generic semantic patterns across 44 fields

Abstract

When semantically describing knowledge graphs (KGs), users have to make a critical choice of a vocabulary (i.e. predicates and resources). The success of KG building is determined by the convergence of shared vocabularies so that meaning can be established. The typical lifecycle for a new KG construction can be defined as follows: nascent phases of graph construction experience terminology divergence, while later phases of graph construction experience terminology convergence and reuse. In this paper, we describe our approach tailoring two AI-based clustering algorithms for recommending predicates (in RDF statements) about resources in the Open Research Knowledge Graph (ORKG) https://orkg.org/. Such a service to recommend existing predicates to semantify new incoming data of scholarly publications is of paramount importance for fostering terminology convergence in the ORKG. Our experiments show very promising results: a high precision with relatively high recall in linear runtime performance. Furthermore, this work offers novel insights into the predicate groups that automatically accrue loosely as generic semantification patterns for semantification of scholarly knowledge spanning 44 research fields.