TL;DR
An iterative, ontology-driven pipeline that transforms unstructured documents into structured, queryable knowledge graphs operating entirely on local large language models (LLMs) is presented.
Ontology-driven knowledge graph construction from industrial text remains challenging due to the domain specificity of documents, the scarcity of annotated resources, and the complexity of ontology engineering workflows. This paper presents and investigates the applicability of an ontology learning pipeline that combin...
The rapid expansion of scientific literature far outpaces the speed of manual curation, creating a fundamental bottleneck in extracting structured knowledge from scientific texts. To address this, we introduce an agentic, ontology-guided framework for knowledge graph (KG) construction. Our framework utilizes specialize...
Construction contract analysis increasingly relies on combining Knowledge Graphs (KGs), which provide structured, interpretable representations of contractual entities and relationships, with Large Language Models (LLMs), which offer flexible extraction and reasoning over unstructured clause text. Existing hybrid KG–LL...
Knowledge Graphs (KGs) have become increasingly critical in enterprise environments; however, existing academic methodologies do not always explicitly address requirements such as governance, infrastructure scalability, modularity, and integration with legacy systems. This paper examines limitations in the explicit cov...
A modular agentic-AI platform that converts a heterogeneous corpus of process-development documents into a queryable, dual-layer knowledge graph is presented and it is anticipated graph-based architectures will see broader adoption in pharma as a means of transforming fragmented document repositories into structured pr...
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