Making Linked Data Discoverable In The Context Of Wildlife Data Observatories

Abstract

In recent years, Linked Data (LD) and Semantic Web technologies have gained traction as powerful frameworks for integrating and querying distributed datasets across disciplines. Despite their potential, the complexity of technologies such as RDF triplestores and SPARQL query languages continues to hinder adoption among non-expert users, particularly within bioscience and wildlife research, where observational data is prevalent. This thesis addresses the usability gap by exploring how LD technologies can be made more accessible for domain specialists without technical expertise in Semantic Web technologies.

A mixed-methods approach was adopted, combining a systematic literature review, ethnographic fieldwork, and iterative interface design. Findings highlighted key limitations in existing LD access techniques, particularly their inability to support observational data patterns and spatiotemporal querying needs. In response, this research presents an integrated approach that combines graphical and conversational user interfaces to assist domain specialists in constructing queries without prior technical knowledge. This approach enables users to formulate complex semantic queries either through visual interactions or by describing their information needs in natural language, with the system translating these into executable queries, supporting key features that include dynamic filter generation, spatial selection, and ontology-aware entity linking.

The approach was evaluated through a task-based user study involving bioscience researchers. Results demonstrate that the integrated interface significantly improves usability, task accuracy, task completion time (over 50% improvement across most tasks) and user satisfaction compared to graphical or conversational UIs. Furthermore, this thesis explores the integration of Large Language Models (LLMs) via a Retrieval-Augmented Generation (RAG) approach to enhance semantic interpretation and user support. Across multiple use cases, integrating LLMs enhances the expressivity of natural language queries, allowing previously unsupported queries to be answered with over 89% accuracy and up to 100% for many tasks. Overall, this research contributes to the field by introducing accessible LD retrieval methods for non-experts in ecological data observatories.