Semantic impact graphs for information valuation

Information valuation has typically been carried out implicitly in question-answering and document retrieval systems. We argue that explicit information valuation is needed to move away from the system and process-centric nature of implicit valuation which has also hindered the theoretical study of information value under a unified and explicit framework. In this paper we present a graphical-based model for explicit information valuation. Our model caters to the subjective nature of information quality by measuring the impact a candidate piece of information may have on a knowledge base representing the recipient's world view. Our model is capable of evaluating information semantically at the statement level and is in effect basing information-valuation on information-understanding. However, information value can be computed and predicted using our causal graph model without requiring full logical inference typically needed for information-understanding.