=Paper=
{{Paper
|id=Vol-2373/paper-37
|storemode=property
|title=Querying Attributed DL-Lite Ontologies Using Provenance Semirings (Extended Abstract)
|pdfUrl=https://ceur-ws.org/Vol-2373/paper-37.pdf
|volume=Vol-2373
|authors=Camille Bourgaux,Ana Ozaki
|dblpUrl=https://dblp.org/rec/conf/dlog/BourgauxO19
}}
==Querying Attributed DL-Lite Ontologies Using Provenance Semirings (Extended Abstract)==
https://ceur-ws.org/Vol-2373/paper-37.pdf
Querying Attributed DL-Lite Ontologies Using
Provenance Semirings (Extended Abstract)
Camille Bourgaux and Ana Ozaki
DI ENS, CNRS, ENS, PSL University & Inria, France
KRDB Research Centre, Free University of Bozen-Bolzano, Italy
Knowledge graphs often enrich data with contextual information (such as temporal
validity or provenance of an assertion) in the format of annotations, which can be seen as
attribute-value pairs. Recently, attributed description logics have been proposed to bridge
the gap between the data format present in knowledge graphs and classical description
logics [4, 3, 5]. The mentioned works analyse the complexity of the satisfiability problem
for various attributed description logics. This extended abstract gives an overview of
our work [1], published at AAAI 2019, on query answering in attributed description
logics using provenance semirings. After investigating satisfiability and query answering
in attributed DL-LiteR , we turn our attention to provenance information, one of the
most common types of annotation in knowledge graphs. Indeed, since knowledge graphs
often integrate data from multiple sources, one may not be only interested in obtaining
query results but also in establishing levels of trust, or determining authorship, among
others [7]. To deal with provenance information, we propose a new semantics based
on provenance semirings, as first introduced in database theory [2] (and also recently
studied in an ontology-based data access setting [6]).
Attributed DL-LiteR Attributed DLs are defined over the usual DL signature with
countable sets of concept names NC , role names NR , and individual names NI . We
consider an additional set NU of set variables and a set NV of object variables. Annotation
sets are defined as finite binary relations, understood as sets of attribute-value pairs.
Attributes and values refer to domain elements and are syntactically denoted by individual
names. To describe annotation sets, we use a set S of specifiers, which can be either set
variables; closed specifiers [a1 : v1 , . . . , an : vn ]; or open specifiers ba1 : v1 , . . . , an : vn c,
where ai ∈ NI and vi is either an individual name in NI , an object variable in NV , or an
expression of the form X.a, with X a set variable in NU and a an individual name in NI .
We use X.a to refer to the (finite, possibly empty) set of all values of attribute a in an
annotation set X. Intuitively, closed specifiers define specific annotation sets whereas
open specifiers merely provide lower bounds [3]. A ground specifier is a closed or open
specifier that only contains individual names. A DL-LiteR @ role (resp. concept) assertion
is an expression R(a, b)@S (resp. A(a)@S), with R ∈ NR (resp. A ∈ NC ), a, b ∈ NI ,
and S ∈ S a ground closed specifier. DL-LiteR @ role and concept inclusions are of the
form X : S (P v Q) and X : S (B v C) respectively, where X ∈ NU , S ∈ S is a
closed or open specifier, and P, Q and B, C are respectively role and concept expressions
defined by the following syntax, where A ∈ NC , R ∈ NR and S ∈ S:
P ::= R@S | R− @S, Q ::= P | ¬P, B ::= A@S | ∃P, C ::= B | ¬B.
We require that all variables are safe, which intuitively means that if they appear on the
right side of an inclusion then they should also be associated to the concept on the left.
Example 1. In DL-LiteR @ , we can express that those who are married (role spouse) to
someone are married (concept Married), and that this fact is associated with the same
sources from which the information has been extracted (attribute src): ∃spouse@X v
Married@bsrc : X.srcc. The assertion spouse(gabor, ryan)@[src : s1 , src : s2 ] states that
Gabor is married to Ryan and it is annotated with the sources of this information.
We similarly define attributed conjunctive queries by associating concept and role names
with specifiers. The formal definition of the semantics is given in [1].
Example 2. The query ∃x Married(gabor)@byear : xc ∧ Married(taylor)@byear : xc
holds if Gabor and Taylor were married (to someone) on the same year.
We establish complexity results for the satisfiability and query entailment problems.
Theorem 3. In DL-LiteR @ , satisfiability and query entailment are PS PACE -complete.
When the ontology does not contain variables, query entailment is NP-complete.
We now define a new semantics for dealing with provenance, which is based on semirings.
Provenance semirings-based semantics We study query entailment in attributed DL-
Lite using a provenance semiring-based semantics. We represent provenance with the
positive algebra provenance semiring for NI , defined as the commutative semiring of
polynomials with variables in NI and coefficients from N, with operations defined as
usual: K = (N[NI ], +, ×, 0, 1) [2]. We denote by NP the set of polynomials of K and
by NS the subset of NP containing the sums of the commutative monoid (N[NI ], +, 0).
We allow to use provenance polynomials from NP as values in a specifier associated
to the whole query and provenance sums from NS as values in specifiers occurring in
the ontology or associated with query atoms. Intuitively, + indicates alternative use of
the data while × indicates join use of the data. For instance, if the result of a query
over an ontology can be obtained from source s3 together with any of s1 , s2 then the
provenance polynomial is: (s1 + s2 ) × s3 . The main idea is that now we allow the whole
query to be annotated with a provenance polynomial and such query is entailed if the
polynomial represents the alternative and join uses of the data. More specifically, we
introduce semiring attributed queries, which are attributed queries where we associate a
specifier to the whole query [1].
Example 4. The query (Married(a)∧Married(b))@bsrc: s1 ×(s2 +s3 ), cls: pub×cnfc is
entailed by the assertions Married(a)@[src: s1 , cls: pub], Married(b)@[src: s2 , cls: cnf],
and Married(b)@[src: s3 , cls: cnf]. The fact that a and b are both married is obtained by
combining s1 with s2 or s3 , and by having access to public and confidential information.
Sums may also appear in concept inclusions, e.g., X : bsrc : s1 + s2 c(∃spouse@X v
Married@X), which requires that the fact that someone has a spouse has to be associated
both with s1 and with s2 to conclude that this person is married.
We establish complexity results for this variant of attributed DL-Lite, which we call
DL-LiteR @,K . The salient results of our investigation are the E XP T IME -hardness for the
satisfiability problem, and the NP-completeness of query entailment for a restricted class
of ontologies, called simple, that allows for inclusions among atomic concepts or roles
of the form E1 @S v E2 @T where S and T are ground specifiers.
Theorem 5. In DL-LiteR @,K , satisfiability is E XP T IME -hard and in 2E XP T IME . When
the ontology is simple, query entailment is NP-complete.
References
1. Camille Bourgaux and Ana Ozaki. Querying attributed DL-Lite ontologies using provenance
semirings. In Proceedings of AAAI, 2019.
2. Todd J. Green, Gregory Karvounarakis, and Val Tannen. Provenance semirings. In Proceedings
of PODS, 2007.
3. Markus Krötzsch, Maximilian Marx, Ana Ozaki, and Veronika Thost. Attributed description
logics: Ontologies for knowledge graphs. In Proceedings of ISWC, 2017.
4. Markus Krötzsch, Maximilian Marx, Ana Ozaki, and Veronika Thost. Attributed description
logics: Reasoning on knowledge graphs. In Proceedings of IJCAI, 2018.
5. Ana Ozaki, Markus Krötzsch, and Sebastian Rudolph. Happy ever after: Temporally attributed
description logics. In Proceedings of DL, 2018.
6. Ana Ozaki and Rafael Peñaloza. Provenance in ontology-based data access. In Proceedings of
DL, 2018.
7. Pierre Senellart. Provenance and probabilities in relational databases. SIGMOD Record,
46(4):5–15, 2017.