Veracity Estimation for Entity-Oriented Search with Knowledge Graphs

In this paper, we discuss the potential costs that emerge from using a Knowledge Graph (KG) in entity-oriented search without considering its data veracity. We argue for the need for KG veracity analysis to gain insights and propose a scalable assessment framework. Previous assessments focused on relevance, assuming correct KGs, and overlooking the potential risks of misinformation. Our approach strategically allocates annotation resources, optimizing utility and revealing the significant impact of veracity on entity search and card generation. Contributions include a fresh perspective on entity-oriented search extending beyond the conventional focus on relevance, a scalable assessment framework, exploratory experiments highlighting the impact of veracity on ranking and user experience, as well as outlining associated challenges and opportunities.