Entity Prediction in Knowledge Graphs with Joint Embeddings

Abstract

Knowledge Graphs (KGs) have become increasingly popular in the recent years. However, as knowledge constantly grows and changes, it is inevitable to extend existing KGs with entities that emerged or became relevant to the scope of the KG after its creation. Research on updating KGs typically relies on extracting named entities and relations from text. However, these approaches cannot infer entities or relations that were not explicitly stated. Alternatively, embedding models exploit implicit structural regularities to predict missing relations, but cannot predict missing entities. In this article, we introduce a novel method to enrich a KG with new entities given their textual description. Our method leverages joint embedding models, hence does not require entities or relations to be named explicitly. We show that our approach can identify new concepts in a document corpus and transfer them into the KG, and we find that the performance of our method improves substantially when extended with techniques from association rule mining, text mining, and active learning.