Arenas, Marcelo

Gottlob, Georg

Pieris, Andreas

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     Edinburgh Research Explorer                                      A Datalog-Based Language for Querying RDF GraphsCitation for published version:Arenas, M, Gottlob, G & Pieris, A 2016, A Datalog-Based Language for Querying RDF Graphs. inProceedings of the 10th Alberto Mendelzon International Workshop on Foundations of Data Management,Panama City, Panama, May 8-10, 2016. CEUR Workshop Proceedings, CEUR Workshop Proceedings(CEUR-WS.org), 10th Alberto Mendelzon International Workshop on Foundations of Data Management,Panama City, Panama, 6/06/16.Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Publisher's PDF, also known as Version of recordPublished In:Proceedings of the 10th Alberto Mendelzon International Workshop on Foundations of Data Management,Panama City, Panama, May 8-10, 2016General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 05. Apr. 2019A Datalog-Based Language for Querying RDF Graphs?Marcelo Arenas1, Georg Gottlob2, and Andreas Pieris31 Pontificia Universidad Cato´lica de Chile marenas@ing.puc.cl2 Department of Computer Science, University of Oxford georg.gottlob@cs.ox.ac.uk3 Institute of Information Systems, Vienna University of Technologypieris@dbai.tuwien.ac.atRDF is the W3C recommendation data model to represent information about WorldWide Web resources, while SPARQL is the standard language for querying RDF data,since its standardization in 2008. One of the distinctive features of Semantic Web data isthe existence of vocabularies with predefined semantics: the RDF Schema (RDFS) andthe Web Ontology Language (OWL), which can be used to derive logical conclusionsfrom RDF graphs; hence, an RDF query language equipped with reasoning capabilitiesto deal with these vocabularies is desirable. In addition, navigational capabilities arevital for data models with an explicit graph structure such as RDF [1, 3, 9, 15], while re-cursive definitions are a key feature for graph query languages [5, 14]. Having an RDFquery language available that combines the above key functionalities is of paramountimportance for the development of the Semantic Web. This has been recognized by theW3C, which led to the release of SPARQL 1.1 in 2013 [10, 12], that is, an extendedversion of the 2008 language with reasoning capabilities to deal with RDFS and OWLvocabularies, and a mechanism to express navigation patterns through regular expres-sions. However, there are still useful queries that cannot be expressed in SPARQL 1.1,due to the lack of general recursion [14].To the best of our knowledge, the only language that supports the above features,focussing on the profile OWL 2 QL of OWL 2, while its query evaluation problem istractable in data complexity, is the recently introduced rule-based language TriQ-Lite,the lite version of the highly expressive triple query (TriQ) language [2]. This languageis based on Datalog∃,¬s,⊥, that is, Datalog extended with existential quantification inrule-heads, stratified negation, and negative constraints with the falsum (⊥) in rule-heads. Unfortunately, TriQ-Lite suffers from a serious drawback, which may revoke itsadvantage as an expressive RDF query language, namely it is not a plain language. Aquery language is called plain if it allows the user to write a query as a single programin a simple non-composite syntax. An example of a plain query language is Datalog,where the user simply needs to define a single Datalog program that captures the in-tended query. The property of plainness provides conceptual simplicity, which is con-sidered to be a key condition for a query language to be useful in practice. AlthoughTriQ-Lite is based on an extension of Datalog, the way its syntax and semantics are de-fined significantly deviates from the standard way of defining Datalog-like languages,and thus does not inherit the plainness of Datalog. In fact, TriQ-Lite is a compositelanguage, where the user is forced to split the query in several modules Π1, . . . ,Πnso that each Πi can be expressed by the fragment of Datalog∃,¬s,⊥ that is underlyingTriQ-Lite, while each pair (Πi, Πi+1) is bridged via a set Qi of conjunctive queries.? This short paper is based on the recent works [2, 11].From the above discussion, we conclude that an RDF query language that fulfillscertain desiderata, which in turn guarantee its applicability in real Semantic Web appli-cations, is currently missing. These desiderata are the following:1. Plainness: simple syntax and semantics, with the aim of simplifying the definitionof queries;2. Reasoning Capabilities: express every SPARQL query under the entailment regimefor OWL 2 QL;3. Recursive Definitions: general form of recursion must be supported, and ideallyDatalog must be incorporated;4. Efficiency: query evaluation must be data tractable, and feasible by the use of stan-dard database technology.At this point, we would like to expose an additional (conceptual) shortcoming ofSPARQL 1.1, which must be taken into account during the designing of an RDF querylanguage. Under the OWL 2 direct semantics entailment regime, the evaluation of abasic graph pattern over an RDF graph adopts the so-called active domain semantics,i.e., it uses the notion of entailment in OWL 2 QL, but allowing variables and blanknodes to take only values from the RDF graph. As discussed in [2], this forces theuser to encode part of the reasoning in the actual query, which undoubtedly leads tounnatural and complex queries. This is illustrated in the following example:Example 1. Consider the OWL 2 QL ontologyO which states that Tom is a person andeach person has a father, and letG be the RDF graph that representsO. Assume that wewant to retrieve the elements of G that have a father. One may be tempted to claim thatthis query can be expressed via the graph pattern P = (?X, father, B), where B is ablank node. However, the answer to P over G is empty since there are no elements a, bin G such that the triple (a, father, b) is implied by the ontology. To obtain the expectedanswer, we have to consider the graph pattern (?X, rdf:type,∃father), which means thatwe are forced to implicitly encode the fact that the triple (Tom, rdf:type,∃father) isinferred by the ontology.Notably, TriQ-Lite provides the definition of the more natural entailment regimewithout the active domain restriction. This is certainly an additional desideratum:5. Reasoning-Query Decoupling: the entailment regime without the active domainrestriction must be definable in order to decouple the reasoning from the query.In this work, we focus on TriQ-Lite, which is a language in evolution, and we in-vestigate how it can be transformed into a plain language without sacrificing any ofthe other desiderata. The outcome of our study is TriQ-Lite 1.0, the new version ofTriQ-Lite, which is based on Datalog∃,¬sg,⊥; we use the superscript ¬sg (instead of ¬s)since, for our purposes, it suffices to focus on negation that, apart from being stratified,is also grounded, i.e., it can be used with predicates that can only store constants. Theproposed formalism is part of Datalog±, that is, a family of logical KR languages [7].Although several interesting Datalog± languages can be found in the literature (see,e.g., [4, 6, 8, 13, 16]), none of them fulfills all desiderata. Even though Datalog± lan-guages inherit the plainness of Datalog, either they are not expressive enough for sat-isfying desiderata 2, 3 and 5, or very expressive and thus intractable. Hence, our keyDesideratum How it is AchievedPlainness Standard Datalog-like languageReasoning capabilities ∃-quantification and ⊥ in rule-headsRecursive definitions Incorporate full Datalog¬sEfficiency Reduction to UCQ evaluationReasoning-Query Expressive joins in rule-bodies, anddecoupling ∃-quantification in rule-headsTable 1. TriQ-Lite 1.0.challenge was to define a Datalog± language that achieves the right balance betweenexpressivity and complexity.Contribution. We propose a new syntactic paradigm, which is underlyingTriQ-Lite 1.0,called wardedness, that can be informally described as follows: all the dangerous body-variables, i.e., variables that may be bound by the program to non-constant values, andat the same time are propagated to the rule-head, occur in exactly one body-atom, calledward, that can interact with the rest of the rule-body only via harmless join variables,i.e., variables that are bound by the program to database constants. For example, theprogram Π , where the dangerous variables are the variables marked by tilde,triple(X˜, U, Y˜ ), inv(U, V )→ triple(Y˜ , V, X˜)type(X˜, Y ), rest(Y, U)→ ∃Z triple(X˜, U, Z)triple(X˜, U, Y ), rest(Z,U)→ type(X˜, Z)is warded; for each rule, all the tilde variables occur in a single body-atom a, while allthe other variables of a are harmless.Our technical results can be summarized as follows (for details see [11]):- We introduce TriQ-Lite 1.0, which is based on warded Datalog∃,¬sg,⊥, and showthat it fulfills all desiderata; the technical reasons are given in Table 1.- We show that for the reasoning-query decoupling, apart from expressive joins amongbody-variables that are bound to non-constant values (this exposes one of the mainlimitations of the language underlying TriQ-Lite), it is vital to allow for existen-tially quantified variables in rule-heads.- We provide a formal justification for the necessity of introducing a new Datalog-based formalism. We establish, via a model-theoretic argument, that existing tractableformalisms are not able to encode the OWL 2 direct semantics entailment regime,and at the same time ensure the reasoning-query decoupling.- Finally, we substantiate the design choices made in the definition of our formalism.In fact, we show that very mild extensions lead to EXPTIME-hardness.Let us say that warded Datalog∃,¬sg,⊥, the formalism underlying TriQ-Lite 1.0, iswell-suited as a general-purpose KR language, since it extends Datalog with featuresthat allow us to express OWL 2 QL and OWL 2 RL ontologies.Acknowledgements. Arenas is funded by the Millenium Nucleus Center for SemanticWeb Research under grant NC120004. Gottlob is supported by the EPSRC ProgrammeGrant EP/M025268/ “VADA: Value Added Data Systems – Principles and Architec-ture”. Pieris is supported by the Austrian Science Fund (FWF), projects P25207-N23and Y698, and Vienna Science and Technology Fund (WWTF), project ICT12-015.References1. 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Calı`, A., Gottlob, G., Pieris, A.: Towards more expressive ontology languages: The queryanswering problem. Artif. Intell. 193, 87–128 (2012)9. Fionda, V., Gutierrez, C., Pirro`, G.: Semantic navigation on the web of data: specification ofroutes, web fragments and actions. In: WWW. pp. 281–290 (2012)10. Glimm, B., Ogbuji, C.: SPARQL 1.1 entailment regimes (2013), w3C Recommendation 21March 2013, http://www.w3.org/TR/sparql11-entailment/11. Gottlob, G., Pieris, A.: Beyond SPARQL under OWL 2 QL entailment regime: Rules to therescue. In: IJCAI. pp. 2999–3007 (2015)12. Harris, S., Seaborne, A.: SPARQL 1.1 query language (2013), w3C Recommendation 21March 2013, http://www.w3.org/TR/sparql11-query/13. Leone, N., Manna, M., Terracina, G., Veltri, P.: Efficiently computable Datalog∃ programs.In: KR (2012)14. Libkin, L., Reutter, J.L., Vrgoc, D.: Trial for RDF: adapting graph query languages for RDFdata. In: PODS. pp. 201–212 (2013)15. 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A Datalog-Based Language for Querying RDF Graphs

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