A PROV Encoding for Provenance Analysis Using Deductive Rules

PROV is a specification, promoted by the World Wide Web consortium, for recording the provenance of web resources. It includes a schema, consistency constraints and inference rules on the schema, and a language for recording provenance facts. In this paper we describe a implementation of PROV that is based on the DLV Datalog engine. We argue that the deductive databases paradigm, which underpins the Datalog model, is a natural choice for expressing at the same time (i) the intensional features of the provenance model, namely its consistency constraints and inference rules, (ii) its extensional features, i.e., sets of provenance facts (called a provenance graph), and (iii) declarative recursive queries on the graph. The deductive and constraint solving capability of DLV can be used to validate a graph against the constraints, and to derive new provenance facts. We provide an encoding of the PROV rules as Datalog rules and constraints, and illustrate the use of deductive capabilities both for queries and for constraint validation, namely to detect inconsistencies in the graphs. The DLV code along with a parser to map the PROV assertion language to Datalog syntax, are publicly available.