Graph Neural Network–Based Content Relationship Mapping for Enterprise Knowledge Mining

Abstract

Enterprise knowledge mining with the help of advanced tools is undoubtedly important for enterprises to get the intelligence that is hidden deep in their documents, mail, reports, and other forms of data, which are most of the time poorly managed. But still, most of the enterprises are facing problems with unstructured content, isolated data stores, and very little insight into how the topics, people, and processes are interrelated. This paper proposes a Graph Neural Network (GNN)–based approach to content relationship mapping that solves these problems by identifying the semantic and contextual links of the knowledge assets. Traditional search and classification methods often treat documents as separate entities, whereas GNNs get a deeper relational understanding by viewing content as interconnected nodes of a continuous knowledge graph. Our system is always on and it performs entity recognition, relation extraction, and cross-document linking for the production of a dynamic graph from unstructured texts, where GNN models progressively update link predictions and relevance scores. The new-age enterprise content management system (ECM) technology leverages NLP methods for text preprocessing, embedding creation, graph construction, and supervised or semi-supervised GNN training to pinpoint the relations of the network, like the relation of the concepts, dependencies, duplicate knowledge, or expert pathways. The executed scenario in a local business environment led to the enhancement of content discoverability, reduction of redundancies, and the unveiling of the hidden thematic clusters, which were the manual analysis intermediates; thus, there was a measurable increase of search precision, knowledge reuse, and decision support. The results demonstrate that GNN-powered mapping outperforms baseline similarity models, particularly in the cases of ambiguous terminology or cross-domain references.