Methods for Knowledge Graph Construction from Text Collections: Development and Applications

2026-03-26

AI Summary

This work addresses the challenge of automatically constructing knowledge graphs from multi-source, heterogeneous, and unstructured textual data by proposing an interpretable and interoperable approach that integrates generative AI with semantic web technologies. The method supports adaptive alignment across diverse text types and schema specifications, leveraging natural language processing, information extraction, and causal modeling to enable end-to-end knowledge graph construction. Domain-specific knowledge graphs were developed and validated in three real-world scenarios—news and social media, architectural engineering operations documentation, and electronic health records—yielding tailored algorithms, benchmark evaluations, and in-depth analytical insights. These resources effectively support applications such as digital transformation discourse analysis, scientific trend identification, and causal reasoning in biomedical research.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Knowledge Engineering
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Virtually every sector of society is experiencing a dramatic growth in the volume of unstructured textual data that is generated and published, from news and social media online interactions, through open access scholarly communications and observational data in the form of digital health records and online drug reviews. The volume and variety of data across all this range of domains has created both unprecedented opportunities and pressing challenges for extracting actionable knowledge for several application scenarios. However, the extraction of rich semantic knowledge demands the deployment of scalable and flexible automatic methods adaptable across text genres and schema specifications. Moreover, the full potential of these data can only be unlocked by coupling information extraction methods with Semantic Web techniques for the construction of full-fledged Knowledge Graphs, that are semantically transparent, explainable by design and interoperable. In this thesis, we experiment with the application of Natural Language Processing, Machine Learning and Generative AI methods, powered by Semantic Web best practices, to the automatic construction of Knowledge Graphs from large text corpora, in three use case applications: the analysis of the Digital Transformation discourse in the global news and social media platforms; the mapping and trend analysis of recent research in the Architecture, Engineering, Construction and Operations domain from a large corpus of publications; the generation of causal relation graphs of biomedical entities from electronic health records and patient-authored drug reviews. The contributions of this thesis to the research community are in terms of benchmark evaluation results, the design of customized algorithms and the creation of data resources in the form of Knowledge Graphs, together with data analysis results built on top of them.

Problem

• Knowledge Graph Construction
• Unstructured Text
• Semantic Knowledge Extraction
• Interoperability
• Scalable Information Extraction

Innovation

• Knowledge Graph Construction
• Generative AI
• Semantic Web
• Information Extraction
• Cross-domain Adaptation