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Updated: Sep 18, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Life Detection Knowledge Base: A Community Tool for Knowledge Management and Representation
Andrew Pohorille1, Graham Lau2, Stanislaw Gliniewicz3
1Exobiology Branch, NASA Ames Research Center, Moffett Field, California, USA.
None:
The Life Detection Knowledge Base (LDKB; https://lifedetectionforum.com/ldkb) is a community-owned web resource that is designed to facilitate the infusion of astrobiology knowledge and expertise into the conceptualization and design of life detection missions. The aim of the LDKB is to gather and organize diverse knowledge from a range of fields into a common reference frame to support mission science risk assessment, specifically in terms of the potential for false positive and false negative results when pursuing a particular observation strategy. Within the LDKB, knowledge sourced from the primary scientific literature is organized according to (1) a taxonomic classification scheme in which potential biosignatures are defined at a uniform level of granularity that corresponds to observable physical or chemical quantities, qualities, or states; (2) a set of four standard assessment criteria, uniformly applied to each potential biosignature, that target the factors that contribute to false positive and false negative potential; and (3) a discourse format that utilizes customizable, user-defined "arguments" to represent the essential aspects of relevant scientific literature in terms of their specific bearing on one of the four assessment criteria, and thereby on false positive and false negative potential. By mapping available and newly emerging knowledge into this standardized framework, we can identify areas where the current state of knowledge supports a well-informed science risk assessment as well as critical knowledge gaps where focused research could help flesh out and mature promising life detection approaches.
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