On the Chase for All Provenance Paths with Existential Rules

Abstract

Abstract. In this paper we focus on the problem of how lineage for existential rules knowledge bases. Given a knowledge base and an atomic ground query, we want to output all minimal provenance paths of the query (i.e. the sequence of rule applications that generates an atom from a given set of facts). Obtaining all minimal provenance paths of a query using forward chaining can be challenging due to the simplifications done during the rule applications of different chase mechanisms. We build upon the notion of Graph of Atoms Dependency (GAD) and use it to solve the problem of provenance path loss in the context of forward chaining with existential rules. We study the properties of this structure and investigate how different chase mechanisms impact its construction.

DAMN: Defeasible Reasoning Tool for Multi-Agent Reasoning

“…Different tools for defeasible reasoning have been proposed in the literature, most notably, ASPIC+ 2 (Prakken 2010), DeLP 3 (García and Simari 2004), DEFT (Hecham, Croitoru, and Bisquert 2017), ELDR (Hecham, Bisquert, and Croitoru 2018), Flora-2 (Wan, Kifer, and Grosof 2015), and SPINdle (Lam 2012). However, each tool allows for a different set of defeasible reasoning features and none of them provides support for multi-agent collaboration or visualization.…”

An argumentative approach for handling inconsistency in prioritized Datalog ± ontologies

“…In Algorithm 1 below, we find all consistent subsets of F (line 2), which is done by the function AllConsistentSubset. Next, all possible arguments are generated: R-consistent subsets of F are used as the supports and the conclusions are deduced from them (This can be done by using the function Chase [26]) (line 3-6). We next define an undercut attack to express conflicting information.…”

“…By using Algorithm 1, we can obtain a set of arguments.A 1 = ({DancesWOP(d 3 )}, {DancesWOP(d 3 ))}, A 2 = ({DancesWP(d 3 )}, {DancesWP(d 3 ))}, A 3 = ({Tdances(d 1 )}, {Tdances(d 1 ))}, Algorithm 1: GenerateArguments Input: a KB K = (F, R, C) Output: The set of arguments SetofArgs 1 SetofArgs ← ∅; 2 Subsets ← AllConsistentSubset(F , R, C);// get all consistent subsets of F 3 for each E ∈ Subsets do ← Chase(R, conc); // saturate facts by using forward chaining (i.e. Skolem chase)[26] ← Argument(E, temp);7 SetofArgs ← TempArg; 8 return SetofArgs.A 4 = ({Tdances(d 1 )}, {DancesWP(d 1 ))}, A 5 = ({HasProps(d 1 , f l)}, {HasProps(d 1 , f l))}, A 6 = ({HasProps(d 1 , f l)}, {DancesWP(d 1 ))},A 7 = ({Mdances(d 1 )}, {Mdances(d 1 ))}, A 8 = ({Mdances(d 1 )}, {DancesWOP(d 1 ))}, A 9 = ({Mdances(d 2 )}, {Mdances(d 2 ))}, A 10 = ({Mdances(d 2 )}, {DancesWOP(d 2 )}), A 11 = ({HasProps(d 2 , hk)}, {HasProps(d 2 , hk)}), A 12 = ({HasProps(d 2 , hk)}, {DancesWP(d 2 )}).…”

Rule Applicability on RDF Triplestore Schemas

“…To the best of the authors' knowledge, our approach is the first to determine the applicability of inference rules to types of RDF triplestores specified by their schema, and to expand their schema with the potential consequences of such rules. Unlike related work on provenance paths for query inferences [14], we do not explicitly model the dependencies between different rules. Instead, we compute their combined potential set of inferences by expanding the original schema on a ruleby-rule basis, through multiple iterations, following the basic principles of the chase algorithm.…”