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)
this https URL. 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.

本文介绍了一种使用两个基于人工智能的聚类算法来推荐 Open Research Knowledge Graph（ORKG）中资源有关谓词（在 RDF 语句中）的方法，以促进 ORKG 中术语汇聚的服务。实验证明，该方法具有很高的准确性和相对较高的召回率，并提供了有关谓词组的新见解，这些谓词组自动在跨 44 个研究领域的学术知识的语义化模式中积累。