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Knowledge graphs (KGs) have increasingly become the backbone of many critical knowledge-centric applications. Most large-scale KGs used in practice are automatically constructed based on an ensemble of extraction techniques applied over diverse data sources. Therefore, it is important to establish the provenance of results for a query to determine how these were computed. Provenance is shown to be useful for assigning confidence scores to the results, for debugging the KG generation itself, and for providing answer explanations. In many such applications, certain queries are registered as standing queries since their answers are needed often. However, KGs keep continuously changing due to reasons such as changes in the source data, improvements to the extraction techniques, refinement/enrichment of information, and so on. This brings us to the issue of efficiently maintaining the provenance polynomials of complex graph pattern queries for dynamic and large KGs instead of having to recompute them from scratch each time the KG is updated. Addressing these issues, we present HUKA which uses provenance polynomials for tracking the derivation of query results over knowledge graphs by encoding the edges involved in generating the answer. More importantly, HUKA also maintains these provenance polynomials in the face of updates---insertions as well as deletions of facts---to the underlying KG. Experimental results over large real-world KGs such as YAGO and DBpedia with various benchmark SPARQL query workloads reveals that HUKA can be almost 50 times faster than existing systems for provenance computation on dynamic KGs.
Knowledge graphs (KG) that model the relationships between entities as labeled edges (or facts) in a graph are mostly constructed using a suite of automated extractors, thereby inherently leading to uncertainty in the extracted facts. Modeling the un
Why and why-not provenance have been studied extensively in recent years. However, why-not provenance, and to a lesser degree why provenance, can be very large resulting in severe scalability and usability challenges. In this paper, we introduce a no
Explaining why an answer is (or is not) returned by a query is important for many applications including auditing, debugging data and queries, and answering hypothetical questions about data. In this work, we present the first practical approach for
As users become confronted with a deluge of provenance data, dedicated techniques are required to make sense of this kind of information. We present Aggregation by Provenance Types, a provenance graph analysis that is capable of generating provenance
Explaining why an answer is in the result of a query or why it is missing from the result is important for many applications including auditing, debugging data and queries, and answering hypothetical questions about data. Both types of questions, i.e