Towards a Reference Model for an Information Extraction Lifecycle supporting Data Collection, Data Preparation, Information Extraction, Information Organization, Knowledge Organization, Information Access and Retrieval

The exponential growth of unstructured and multimodal data across domains such as healthcare, sustainability, event analytics, and digital humanities has intensified the demand for scalable methods to collect, prepare, extract, organize, and retrieve information. Established conceptual models highlight that ”data must be progressively transformed into information, knowledge, and wisdom” and that ”knowledge creation unfolds as an iterative interplay between tacit and explicit knowledge.” These perspectives offer valuable foundations, yet practical implementations remain fragmented, brittle to evolving vocabularies, and bound to domain-specific contexts. Information extraction techniques promise to ”identify structured representations of entities, events, and relationships from unstructured data”, but their outputs often lack adaptability, reusability, and semantic integration. Likewise, cloud reference architectures provide robust frameworks for secure and interoperable services, though they rarely connect directly to knowledge-centric workflows. Collaborative infrastructures further illustrate the potential of layered, modular environments, yet their scope typically remains confined to narrow disciplinary settings. To overcome these limitations, this habilitation introduces the Information Extraction Lifecycle (IELC) reference model. Building on conceptual clarity, scalable cloud-based architectures, and layered modularity, the IELC reference model establishes a domain-independent framework that unifies all information extraction activities. By embedding feedback and iteration, it supports continuous knowledge creation and ensures scalability, interoperability, explainability, reusability, and multimodal integration, thereby advancing the development of sustainable knowledge ecosystems.