Towards Ontologically Grounded and Language-Agnostic Knowledge Graphs

TL;DR

This paper proposes an ontologically grounded, language-agnostic approach to improve the integration and updating of knowledge graphs across different domains and languages.

Contribution

It introduces a novel method that reifies abstract objects and distinguishes concepts from types to enhance knowledge graph interoperability.

Findings

Improved KG integration across domains and languages

Enhanced updating processes for knowledge graphs

Better handling of multilingual and multi-domain data

Abstract

Knowledge graphs (KGs) have become the standard technology for the representation of factual information in applications such as recommendation engines, search, and question-answering systems. However, the continual updating of KGs, as well as the integration of KGs from different domains and KGs in different languages, remains to be a major challenge. What we suggest here is that by a reification of abstract objects and by acknowledging the ontological distinction between concepts and types, we arrive at an ontologically grounded and language-agnostic representation that can alleviate the difficulties in KG integration.