Abstract
In this paper we present a method for enhancing Question Answering (QA) systems by iteratively improving Knowledge Graphs (KGs) with a focus on maintaining monotonicity in the enhancement process. We introduce a mathematical framework employing functions τ and ϕ, where τ transforms text T into a KG K, and ϕ generates an answer from T for a given question. We propose that augmenting K with domain-specific information, denoted as ΔK, leads to a more accurate approximation of the expected answer, adhering to the principle that each enhancement either maintains or improves answer quality. This concept is formalized as ϕ− 1(ϕ(T) ∪ ΔK) yielding better results than ϕ− 1(ϕ(T)). The paper elaborates on this process with practical examples, demonstrating how KG enhancements, under the constraints of monotonicity, lead to successive improvements in the Question Answering (QA) system.