Fostering Knowledge Infrastructures in Science Communication and Aerospace Engineering

2025-12-15

ACM/IEEE Joint Conference on Digital Libraries

AI Summary

This study addresses persistent challenges in scientific communication and aerospace engineering—namely data silos, insufficient collaboration incentives, and legal barriers—that hinder the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) principles. To overcome these limitations, this work proposes a novel, scalable knowledge infrastructure framework that integrates human–AI collaboration, knowledge graphs, and user-centered design across technological, social, and legal dimensions. The framework encompasses automated information processing workflows, a wiki-style digital library, and demand-driven interactive interfaces. Pilot implementations demonstrate its effectiveness in consolidating fragmented knowledge resources and establishing a viable collaborative paradigm for sparsely networked domains. Nevertheless, institutional and sociocultural barriers remain significant and require further intervention to fully realize the framework’s potential.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Engineering
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Humans and AI: Intelligent User Interfaces

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
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

Abstract

Knowledge infrastructures are defined as robust networks of people, artifacts, and institutions that generate, share and maintain specific knowledge. Yet, many domains are fragmented and far from robustly networked, such as science communication or aerospace engineering. While FAIR (Findable, Accessible, Interoperable, Reusable) data management tools exist, their adoption in these domains is limited. Several challenges inhibit this adoption, from complex heterogeneous data formats to lack of structured support to outright incentives against collaboration or legal barriers. This doctoral work outlines how to foster underdeveloped knowledge infrastructures with the usecases of science communication and aerospace engineering. By analyzing these problems and identifying available solutions, toolsupported workflows towards collaborative infrastructure can be implemented and evaluated. These include human-in-theloop artificial intelligence (AI)-supported workflows for information extraction and processing, wiki-and knowledge-graphbased digital libraries, and stakeholder-requirement-driven interfaces. While these developed tools for workflow automation and knowledge representation show promise, significant challenges remain. Future work will have to go beyond technical problemsolving and address the societal and legal barriers to unlock the particular domains. Beyond that, advocates of emerging knowledge infrastructures in any domain are welcome to apply the findings of this work to foster the networking of available knowledge.

Problem

• knowledge infrastructures
• science communication
• aerospace engineering
• FAIR data
• collaboration barriers

Innovation

• knowledge infrastructures
• human-in-the-loop AI
• FAIR data
• knowledge graphs
• collaborative workflows