AEE: Automated Epistemological Evaluation System

This paper introduces the Automated Epistemological Evaluation (AEE) system, a groundbreaking framework that revolutionizes how we extract and evaluate knowledge from textual data. In an era of information overload and misinformation, AEE addresses the critical challenge of determining what information can be trusted and why.

Unlike conventional knowledge extraction systems that focus solely on identifying facts, AEE incorporates sophisticated epistemological mechanisms to assess the reliability, plausibility, and coherence of extracted propositions. The system's innovative "Era" architecture integrates:

• Linguistic sensitivity that captures certainty/uncertainty cues in source texts
• Plausibility validation against established knowledge patterns
• Source reliability assessment based on contradiction analysis
• Bias detection to identify source monoculture and circular reasoning
• Transparent explanation generation to provide insight into confidence scores

Our evaluation demonstrates AEE's effectiveness in building a coherent knowledge base from diverse and potentially contradictory sources while maintaining epistemological integrity. This work establishes a foundation for more reliable AI systems that can reason about the trustworthiness of information—a capability increasingly crucial in domains from healthcare and scientific research to media analysis and education.

Keywords: knowledge extraction, epistemology, NLP, bias detection, plausibility assessment, source reliability, automated reasoning, knowledge representation

Data Availability: The AEE system implementation and test datasets are available in the supplementary materials.

Code Repository: NextGenC/AEE · Hugging Face