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Graph Neural Networks for Analyzing the Semantic Web – Reasoning about Linked Data

Jincheng Zhang
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)
Advanced Graph Neural Networks

Abstract

The Semantic Web, characterized by linked data and intricate relationships between entities, presents significant challenges for traditional database systems. Existing graph databases often struggle to effectively perform complex reasoning tasks over this data. This paper proposes a novel approach utilizing Graph Neural Networks (GNNs) to address this limitation. We leverage GNNs to learn embeddings of linked data entities and their associated relationships, transforming them into a format conducive to reasoning. These learned embeddings enable capabilities such as inference, query expansion, and knowledge graph completion. The core contribution lies in applying GNNs directly to the complexities of the Semantic Web, offering a powerful and flexible framework for reasoning over linked data. We explore the architecture of the GNN model, discuss the training process, and outline potential applications. The proposed method offers a promising direction for enhancing the intelligence and analytical capabilities of Semantic Web technologies.

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