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Knowledge graphs (KG) that model the relationships between entities aslabeled edges (or facts) in a graph are mostly constructed using a suite ofautomated extractors, thereby inherently leading to uncertainty in theextracted facts. Modeling the uncertainty as probabilistic confidence scoresresults in a probabilistic knowledge graph. Graph queries over suchprobabilistic KGs require answer computation along with the computation ofthose result probabilities, aka, probabilistic inference. We propose a system,HAPPI (How Provenance of Probabilistic Inference), to handle such queryprocessing. Complying with the standard provenance semiring model, we propose anovel commutative semiring to symbolically compute the probability of theresult of a query. These provenance-polynomiallike symbolic expressions encodefine-grained information about the probability computation process. We leveragethis encoding to efficiently compute as well as maintain the probability ofresults as the underlying KG changes. Focusing on a popular class ofconjunctive basic graph pattern queries on the KG, we compare the performanceof HAPPI against a possible-world model of computation and a knowledgecompilation tool over two large datasets. We also propose an adaptive systemthat leverages the strengths of both HAPPI and compilation based techniques.Since existing systems for probabilistic databases mostly focus on querycomputation, they default to re-computation when facts in the KG are updated.HAPPI, on the other hand, does not just perform probabilistic inference andmaintain their provenance, but also provides a mechanism to incrementallymaintain them as the KG changes. We extend this maintainability as part of ourproposed adaptive system.

Computing and Maintaining Provenance of Query Result Probabilities in Uncertain Knowledge Graphs