Soft Reasoning on Uncertain Knowledge Graphs

The further possibilities in data management (Wang et al., 2022; uncertain nature of knowledge is widely observed Ren et al., 2023). in the real world, but does not align seamlessly with the first-order logic underpinning existing Uncertain knowledge is widely observed from the daily studies. To bridge this gap, we study the setting events (Zhang et al., 2020) to the interaction of biological of soft queries on uncertain knowledge, which systems (Szklarczyk et al., 2023). Besides, uncertainty is is motivated by the establishment of soft constraint also particularly pervasive in KGs because KGs are constructed programming. We further propose an MLbased by information extraction models that could introduce approach with both forward inference and errors (Angeli et al., 2015; Ponte & Croft, 2017) backward calibration to answer soft queries on and from large corpses that could be noisy (Carlson et al., large-scale, incomplete, and uncertain knowledge 2010). To represent the uncertain knowledge, confidence graphs. Theoretical discussions present that our values p are associated with triples in many well-established methods share the same complexity as state-ofthe-art KGs (Carlson et al., 2010; Speer et al., 2017; Szklarczyk inference algorithms for first-order queries.