Knowledge engineering at scale: which avenues are actively explored to automate and accelerate the creation of a knowledge base for expert and AI systems?

Introduction / Context:
Expert systems and modern AI applications rely on structured knowledge. Building and maintaining knowledge bases by hand is costly and error-prone. Consequently, research and practice explore automation and tooling to scale acquisition, curation, and discovery, thereby reducing human bottlenecks and improving coverage and freshness.

Given Data / Assumptions:

• Automation can come from algorithms, better interfaces, or data-driven discovery.
• We interpret “simpler tools” as improved tooling that lowers the effort for domain experts.
• “Discovery of new concepts” refers to automated induction from data or text.

Concept / Approach:
Automatic knowledge acquisition includes techniques like information extraction, ontology learning, and semi-supervised curation. Simpler tools empower subject-matter experts to encode rules or annotate data without deep technical training (wizards, templates, low-code). Discovery of new concepts leverages machine learning to identify entities, relations, and rules from corpora and structured datasets, feeding back into the knowledge base with human validation loops.

Step-by-Step Solution:

Verification / Alternative check:
Industrial knowledge graphs, medical expert systems, and enterprise ontologies employ pipelines that combine automated extraction, user-friendly authoring, and discovery techniques, validating the multi-pronged approach.

Why Other Options Are Wrong:

• Each single option is true but incomplete on its own.
• None is false because these avenues are all used in practice.

Common Pitfalls:
Assuming automation eliminates experts; in reality, human-in-the-loop validation remains crucial to ensure precision and trust.

Final Answer:
All of the above