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Query Rewriting Optimisation Techniques for Ontology-Based Data Access

The document discusses query rewriting optimization techniques in ontology-based data access (OBDA) systems, proposing several algorithms to enhance efficiency by utilizing mappings and eboxes. It identifies limitations in existing methodologies and presents new hypotheses for improving query rewriting processes. The evaluation of these techniques is demonstrated through tests on various ontologies and their mappings.

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Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING OPTIMISATION TECHNIQUES FOR ONTOLOGY-BASED DATA ACCESS Jos´e Mora L´opez jmora@fi.upm.es Supervisor: Oscar Corcho ocorcho@fi.upm.es Ontology Engineering Group Facultad de Inform´atica, Universidad Polit´ecnica de Madrid Madrid - December 17, 2015 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 1 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING IN OBDA QUERY REWRITING ontology rewritten query Mappings query query translation translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 2 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References OUTLINE 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 3 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 4 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 5 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References USUAL LOGICS IN OBDA axiom1 logic DL-Litecore DL-LiteF DL-LiteR Rapid’s ELHIO Datalog±2 Horn-SHIQ B1 B2, B1 ¬B2 ≥ 2R1 ⊥ R1 R2, R1 ¬R2 B1 ∃R1, ∃R1 B1 B1 ∃R1.B2 ∃R1.B1 B2 B1 B2 B3 {a} B, B {a}, B(a) n-ary predicates trans(R1) B1 ∀R1.B2, B1 ≤ 1R1.B2 1∀i, j, Bi represents a basic concept and Rj represents a role that may be basic or inverted. 2as implemented in Nyaya jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 6 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References SYSTEMS IN THE STATE OF THE ART System Input Output Reference Quonto DL-LiteR UCQ Calvanese et al. (2007) REQUIEM ELHIO Datalog or UCQ P´erez-Urbina et al. (2009) Presto DL-LiteR Datalog Rosati and Almatelli (2010) Rapid DL-LiteR 3 Datalog or UCQ Chortaras et al. (2011) Nyaya Datalog± UCQ Gottlob et al. (2011) Venetis’ DL-LiteR UCQ Venetis et al. (2011) Prexto DL-LiteR and EBox Datalog or UCQ Rosati (2012) Clipper Horn-SHIQ Datalog Eiter et al. (2012) kyrie ELHIO Datalog or UCQ Mora and Corcho (2013) kyrie2 ELHIO and EBox Datalog or UCQ Mora et al. (2014) 3Close to OWL2 QL, B1 ∃R.B2 axioms are supported jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 7 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References GENERAL QUERY REWRITING ALGORITHM ontology convert to internal rep- resentation query reachability test rewrite into Datalog Datalog unfold into UCQ UCQ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 8 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References REQUIEM QUERY REWRITING ALGORITHM query reachability convert to FOL ontology remove functions greedy unfold greedy mode? na¨ıve unfold na¨ıve mode? Datalog optimise UCQ YesNo Yes No jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 9 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References LIMITATIONS IN THE STATE OF THE ART 1. Lack of effectiveness, e.g. rewriting to empty predicates 2. Redundancy, e.g. retrieving the same values multiple times in a query 3. Lack of efficiency, e.g. doing operations that lead to previous points jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 10 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 11 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References OBJECTIVES Improve existing query rewriting techniques by: 1. Using the information provided by mappings 2. Using more efficient query rewriting algorithms 3. Using an EBox to avoid redundant answers 4. Using ELHIO, for the applicability of results both in theory and practice jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 12 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESES AND CONTRIBUTIONS H1. Knowing the presence or absence of a predicate mappings allows reducing the size of the rewritten queries and the time needed to produce them H2. Additional computations can be done during query rewriting so as to obtain a gain in efficiency that compensates for the cost of these computations H3. EBoxes may allow reducing the size of the rewritten queries and the time needed in the query process C1. An algorithm4 that considers the presence of mappings to improve results and process of query rewriting C2. An algorithm including several optimisations in the process of query rewriting C3. An algorithm that uses the knowledge in an EBox to remove redundancy in the rewritten queries and the query rewriting process 4All algorithms were empirically evaluated and formally validated jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 13 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESES AND CONTRIBUTIONS H1. Knowing the presence or absence of a predicate mappings allows reducing the size of the rewritten queries and the time needed to produce them H2. Additional computations can be done during query rewriting so as to obtain a gain in efficiency that compensates for the cost of these computations H3. EBoxes may allow reducing the size of the rewritten queries and the time needed in the query process C1. An algorithm4 that considers the presence of mappings to improve results and process of query rewriting C2. An algorithm including several optimisations in the process of query rewriting C3. An algorithm that uses the knowledge in an EBox to remove redundancy in the rewritten queries and the query rewriting process 4All algorithms were empirically evaluated and formally validated jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 13 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 14 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References OBDA CONTEXT QUERY REWRITING ontology rewritten query Mappings query query translation translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 15 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References OBDA CONTEXT QUERY REWRITING ontology rewritten query Mappings query query translation translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 15 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING ALGORITHM query reachability convert to FOL ontology remove functions greedy unfold greedy mode? na¨ıve unfold na¨ıve mode? Datalog optimise UCQ YesNo Yes No jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 16 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING ALGORITHM query reachability convert to FOL mappings ontology remove functions remove non-mapped predicates greedy unfold greedy mode? na¨ıve unfold na¨ıve mode? Datalog optimise UCQ YesNo Yes No jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 16 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References TEST CASES IN FAO Ontology Concepts Object Properties Datatype Properties Group5 Num. Mapped Coverage Num. Map. Coverage Num. Map. Coverage Total 1 7 5 28.57% 15 14 6.67% 126 100 20.63% 19.59% 2 3 1 66.67% 1 0 100.00% 8 2 75.00% 75.00% 3 6 0 100.00% 25 9 64.00% 30 10 66.67% 68.85% 4 5 0 100.00% 20 8 60.00% 90 32 64.44% 65.22% 5 7 3 57.14% 0 0 0.00% 104 70 32.69% 34.23% 6 5 0 100.00% 20 8 60.00% 90 32 64.44% 65.22% 7 3 1 66.67% 1 0 100.00% 8 2 75.00% 75.00% 8 16 15 6.25% 0 0 0.00% 201 198 1.49% 1.84% 9 16 15 6.25% 0 0 0.00% 201 198 1.49% 1.84% Total 68 40 41.18% 82 39 52.44% 858 644 24.94% 28.27% 5http://purl.org/net/jmora/mappings/fao/analysis jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 17 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References MAPPING-BASED REACHABILITY Principle: predicates that provide no answers are not needed Two working modes: H (highest): first mapped predicate stops, following ones are contained L (lowest): last mapped predicate stops, following ones are empty Example Query: Q(x) ← Student(x) Mapped predicates: GradStudent, PhDStudent Ontology: GradStudent Student PhDStudent GradStudent MasterStudent GradStudent H rewriting Q(x) ← GradStudent(x) L rewriting Q(x) ← GradStudent(x) Q(x) ← PhDStudent(x) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 18 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References MAPPING-BASED REACHABILITY Principle: predicates that provide no answers are not needed Two working modes: H (highest): first mapped predicate stops, following ones are contained L (lowest): last mapped predicate stops, following ones are empty Example Query: Q(x) ← Student(x) Mapped predicates: GradStudent, PhDStudent Ontology: GradStudent Student PhDStudent GradStudent MasterStudent GradStudent H rewriting Q(x) ← GradStudent(x) L rewriting Q(x) ← GradStudent(x) Q(x) ← PhDStudent(x) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 18 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References RESOLUTION WITH MAPPINGS For each non-mapped predicate p 1. Check that all clauses with p in the head have all their body predicates mapped Otherwise: select a different predicate Break cycles: choose one predicate per cycle to exclude from resolution 2. Resolve selecting p 3. Clauses containing p can be removed All clauses containing predicates selected for resolution can be deleted Example: Mapped {PhDStudent, researches} TBox: ∃researches.PhDThesis PhDStudent ∃researches− .PhDStudent PhDThesis Ontology (Datalog): PhDStudent(x) ← researches(x, y), PhDThesis(y) PhDThesis(x) ← researches(y, x), PhDStudent(y) Resolves: PhDStudent(x) ← researches(x, z), researches(z, y), PhDStudent(y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 19 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References RESOLUTION WITH MAPPINGS For each non-mapped predicate p 1. Check that all clauses with p in the head have all their body predicates mapped Otherwise: select a different predicate Break cycles: choose one predicate per cycle to exclude from resolution 2. Resolve selecting p 3. Clauses containing p can be removed All clauses containing predicates selected for resolution can be deleted Example: Mapped {PhDStudent, researches} TBox: ∃researches.PhDThesis PhDStudent ∃researches− .PhDStudent PhDThesis Ontology (Datalog): PhDStudent(x) ← researches(x, y), PhDThesis(y) PhDThesis(x) ← researches(y, x), PhDStudent(y) Resolves: PhDStudent(x) ← researches(x, z), researches(z, y), PhDStudent(y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 19 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION SETUP Ontology: hydrOntology (Vilches-Bl´azquez et al., 2007) 155 concepts expressiveness SHIN(D) several mappings The National Atlas data source, with 32 mappings, 1,100 hydrographical instances The Numerical Cartographic Database (BCN200) with 57 mappings that provide 60,000 toponyms EuroGlobalMap (EGM), 32 mappings that provide 3,500 Spanish hydrographical toponyms Hypothesis: H1. Knowing the presence or absence of a predicate mappings allows reducing the size of the rewritten queries and the time needed to produce them jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 20 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION RESULTS Contextual Information Ontology: HydrOntology Original Datalog size: 686 clauses Ignored statements: 405 Time unit: Milliseconds Working modes: Naive Greedy Full Mappings None BCN200 Atlas EGM Mappings # 0 57 32 32 Prune method A H L H L H L Mode Phase Clauses generated after each phase N Prune 535 323 445 287 415 336 429 Saturation 412 25 50 17 24 19 31 Unfolding 2333 25 46 17 22 19 28 Prune 2333 25 46 17 22 19 28 G Prune 535 323 445 287 415 336 429 Saturation 407 25 49 15 24 19 30 Unfolding 894 25 45 15 22 19 27 Prune 688 25 45 15 22 19 27 F Prune 535 323 445 287 415 336 429 Saturation 407 25 49 15 24 19 30 Unfolding 2245 25 45 15 22 19 27 Prune 911 25 45 15 22 19 27 N Total time 2735 172 625 156 359 250 625 G Total time 2250 172 512 156 360 218 532 F Total time 3719 172 531 156 344 235 531 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 21 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION RESULTS Contextual Information Ontology: HydrOntology Original Datalog size: 686 clauses Ignored statements: 405 Time unit: Milliseconds Working modes: Naive Greedy Full Mappings None BCN200 Atlas EGM Mappings # 0 57 32 32 Prune method A H L H L H L Mode Phase Clauses generated after each phase N Prune 535 323 445 287 415 336 429 Saturation 412 25 50 17 24 19 31 Unfolding 2333 25 46 17 22 19 28 Prune 2333 25 46 17 22 19 28 G Prune 535 323 445 287 415 336 429 Saturation 407 25 49 15 24 19 30 Unfolding 894 25 45 15 22 19 27 Prune 688 25 45 15 22 19 27 F Prune 535 323 445 287 415 336 429 Saturation 407 25 49 15 24 19 30 Unfolding 2245 25 45 15 22 19 27 Prune 911 25 45 15 22 19 27 N Total time 2735 172 625 156 359 250 625 G Total time 2250 172 512 156 360 218 532 F Total time 3719 172 531 156 344 235 531 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 21 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESIS VALIDATED Ontology: hydrOntology (Vilches-Bl´azquez et al., 2007) 155 concepts expressiveness SHIN(D) several mappings The National Atlas data source, with 32 mappings, 1,100 hydrographical instances The Numerical Cartographic Database (BCN200) with 57 mappings that provide 60,000 toponyms EuroGlobalMap (EGM), 32 mappings that provide 3,500 Spanish hydrographical toponyms Hypothesis: H1. Knowing the presence or absence of a predicate mappings allows reducing the size of the rewritten queries and the time needed to produce them jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 22 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESIS VALIDATED Ontology: hydrOntology (Vilches-Bl´azquez et al., 2007) 155 concepts expressiveness SHIN(D) several mappings The National Atlas data source, with 32 mappings, 1,100 hydrographical instances The Numerical Cartographic Database (BCN200) with 57 mappings that provide 60,000 toponyms EuroGlobalMap (EGM), 32 mappings that provide 3,500 Spanish hydrographical toponyms Hypothesis: H1. Knowing the presence or absence of a predicate mappings allows reducing the size of the rewritten queries and the time needed to produce them jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 22 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 23 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References OBDA CONTEXT QUERY REWRITING ontology rewritten query Mappings query query translation translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 24 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References OBDA CONTEXT QUERY REWRITING ontology rewritten query Mappings query query translation preprocess clauses translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 24 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING PROCESS query reachability test remove functions greedy unfold greedy mode? na¨ıve unfold na¨ıve mode? Datalog convert to FOL ontology optimise UCQ YesNo Yes No jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 25 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING PROCESS query reachability test remove functions remove auxiliar predicates reachability test unfold preprocess Datalog UCQ convert to FOL ontology jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 25 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References ONTOLOGY PREPROCESSING Some inferences can be done without the query This saves some time for later processing Entirely in FOL Example: Mapped {PhDStudent, researches} TBox: ∃researches.PhDThesis PhDStudent ∃researches− .PhDStudent PhDThesis Ontology (Datalog): PhDStudent(x) ← researches(x, y), PhDThesis(y) PhDThesis(x) ← researches(y, x), PhDStudent(y) Resolves: PhDStudent(x) ← researches(x, z), researches(z, y), PhDStudent(y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 26 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References ONTOLOGY PREPROCESSING Some inferences can be done without the query This saves some time for later processing Entirely in FOL Example: Mapped {PhDStudent, researches} TBox: ∃researches.PhDThesis PhDStudent ∃researches− .PhDStudent PhDThesis Ontology (Datalog): PhDStudent(x) ← researches(x, y), PhDThesis(y) PhDThesis(x) ← researches(y, x), PhDStudent(y) Resolves: PhDStudent(x) ← researches(x, z), researches(z, y), PhDStudent(y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 26 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References CLAUSE SUBSUMPTION CHECK Checked as soon as a new clause is generated Deleted clauses do not participate in resolution Incremental effect Example: Query: Q(x, y) ← Student(x), takesCourse(x, y), Course(y) Consider big taxonomies for both Student and Course TBox: ∃takesCourse Student ∃takesCourse− Course Rewritten query: Q(x, y) ← takesCourse(x, y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 27 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References CLAUSE SUBSUMPTION CHECK Checked as soon as a new clause is generated Deleted clauses do not participate in resolution Incremental effect Example: Query: Q(x, y) ← Student(x), takesCourse(x, y), Course(y) Consider big taxonomies for both Student and Course TBox: ∃takesCourse Student ∃takesCourse− Course Rewritten query: Q(x, y) ← takesCourse(x, y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 27 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References PRIORITISING SOME INFERENCES Free slection resolution accepts some degree of freedom in order Prioritising resolution with shorter clauses increases likelihood of producing subsuming clauses Increases effect of previous optimisation Example: Query: Q(x, y) ← Student(x), takesCourse(x, y), Course(y) Consider big taxonomies for both Student and Course TBox: ∃takesCourse Student ∃takesCourse− Course Rewritten query: Q(x, y) ← takesCourse(x, y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 28 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References PRIORITISING SOME INFERENCES Free slection resolution accepts some degree of freedom in order Prioritising resolution with shorter clauses increases likelihood of producing subsuming clauses Increases effect of previous optimisation Example: Query: Q(x, y) ← Student(x), takesCourse(x, y), Course(y) Consider big taxonomies for both Student and Course TBox: ∃takesCourse Student ∃takesCourse− Course Rewritten query: Q(x, y) ← takesCourse(x, y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 28 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References CONSTRAINING SEARCHES Different groups of clauses have different properties query clauses length Keeping them separately means restricting the attention (computation) to a group Most operations select a random clause in a group, e.g. prioritising inferences In our examples selection is not random, sets of clauses are preferred selection is not specific, several clauses in the set grouping clauses in categories is effective and efficient jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 29 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References CONSTRAINING SEARCHES Different groups of clauses have different properties query clauses length Keeping them separately means restricting the attention (computation) to a group Most operations select a random clause in a group, e.g. prioritising inferences In our examples selection is not random, sets of clauses are preferred selection is not specific, several clauses in the set grouping clauses in categories is effective and efficient jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 29 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION SETUP Usual assets in query rewriting (ontologies and queries) Adolena, path1, path5, StockExchange, University (LUBM), Vicodi. Added ontologies (marked with “X”) using auxiliar predicates for qualified existentials (Calvanese et al., 2007) Added not so usual ontologies: Galen-Lite, Core Added ontologies in ELHIO and beyond OWL2 QL (marked with “E”) From 5 to 9 queries for each Hypothesis: H2. Additional computations can be done during query rewriting so as to obtain a gain in efficiency that compensates for the cost of these computations jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 30 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION RESULTS FOR DATALOG REQUIEM(F) Presto Rapid Clipper kyrie O q size time size time size time size time size time 1 19 12 4 7 4 3 2 21 2 0 2 47 16 2 9 2 9 49 19 47 3 3 20 9 8 16 8 12 21 24 20 3 4 64 15 3 12 3 3 63 18 64 3 U 5 53 12 8 15 8 12 53 15 53 0 6 20 12 19 6 21 15 16 18 16 9 7 49 25 22 15 22 18 44 18 45 12 8 10 9 13 6 13 9 10 20 10 3 9 29 15 24 12 24 17 19 20 21 7 1 22 6 7 10 7 3 5 27 5 6 2 52 15 2 15 2 15 54 27 52 3 3 24 15 10 28 10 9 25 28 24 3 4 70 15 6 19 6 12 69 22 70 0 UX 5 56 15 11 15 11 15 56 21 56 12 6 24 18 28 15 27 22 20 19 20 3 7 55 28 29 21 27 21 50 28 51 12 8 11 6 14 12 14 13 11 26 11 1 9 32 15 30 21 30 15 22 46 24 4 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 31 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION RESULTS FOR DATALOG REQUIEM(F) Presto Rapid Clipper kyrie O q size time size time size time size time size time 1 19 12 4 7 4 3 2 21 2 0 2 47 16 2 9 2 9 49 19 47 3 3 20 9 8 16 8 12 21 24 20 3 4 64 15 3 12 3 3 63 18 64 3 U 5 53 12 8 15 8 12 53 15 53 0 6 20 12 19 6 21 15 16 18 16 9 7 49 25 22 15 22 18 44 18 45 12 8 10 9 13 6 13 9 10 20 10 3 9 29 15 24 12 24 17 19 20 21 7 1 22 6 7 10 7 3 5 27 5 6 2 52 15 2 15 2 15 54 27 52 3 3 24 15 10 28 10 9 25 28 24 3 4 70 15 6 19 6 12 69 22 70 0 UX 5 56 15 11 15 11 15 56 21 56 12 6 24 18 28 15 27 22 20 19 20 3 7 55 28 29 21 27 21 50 28 51 12 8 11 6 14 12 14 13 11 26 11 1 9 32 15 30 21 30 15 22 46 24 4 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 31 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION RESULTS FOR UCQ REQUIEM(F) Rapid Prexto Nyaya kyrie O q size time size time size time size time size time 1 2 15 2 3 2 9 2 5 2 0 2 1 103 1 15 1 15 1 1 1 34 3 4 212 4 9 4 18 4 34 4 18 4 2 3762 2 12 2 15 2 4 2 50 U 5 10 13034 10 18 10 15 10 33 10 37 6 29 47 29 28 28 12 40 1595 29 28 7 42 797 42 37 70 18 54 670 42 43 8 10 15 10 18 10 6 10 63 10 3 9 960 1893 960 209 960 928 960 75135 960 1107 1 5 15 5 12 5 12 5 22 5 9 2 1 172 1 12 1 16 1 3 1 37 3 12 2062 12 15 12 28 12 55 12 21 4 5 31422 5 15 5 23 5 6 5 47 UX 5 25 91878 25 27 25 23 25 39 25 46 6 323 468 323 106 448 178 348 2685 323 187 7 1456 37212 224 81 280 75 264 852 224 121 8 20 21 20 23 20 15 20 61 20 9 9 4200 30506 4200 739 4200 21181 4200 366673 4200 16875 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 32 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION RESULTS FOR UCQ REQUIEM(F) Rapid Prexto Nyaya kyrie O q size time size time size time size time size time 1 2 15 2 3 2 9 2 5 2 0 2 1 103 1 15 1 15 1 1 1 34 3 4 212 4 9 4 18 4 34 4 18 4 2 3762 2 12 2 15 2 4 2 50 U 5 10 13034 10 18 10 15 10 33 10 37 6 29 47 29 28 28 12 40 1595 29 28 7 42 797 42 37 70 18 54 670 42 43 8 10 15 10 18 10 6 10 63 10 3 9 960 1893 960 209 960 928 960 75135 960 1107 1 5 15 5 12 5 12 5 22 5 9 2 1 172 1 12 1 16 1 3 1 37 3 12 2062 12 15 12 28 12 55 12 21 4 5 31422 5 15 5 23 5 6 5 47 UX 5 25 91878 25 27 25 23 25 39 25 46 6 323 468 323 106 448 178 348 2685 323 187 7 1456 37212 224 81 280 75 264 852 224 121 8 20 21 20 23 20 15 20 61 20 9 9 4200 30506 4200 739 4200 21181 4200 366673 4200 16875 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 32 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESIS VALIDATION Usual assets in query rewriting (ontologies and queries) Adolena, path1, path5, StockExchange, University (LUBM), Vicodi. Added ontologies (marked with “X”) using auxiliar predicates for qualified existentials (Calvanese et al., 2007) Added not so usual ontologies: Galen-Lite, Core Added ontologies in ELHIO and beyond OWL2 QL (marked with “E”) From 5 to 9 queries for each Hypothesis: H2. Additional computations can be done during query rewriting so as to obtain a gain in efficiency that compensates for the cost of these computations jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 33 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESIS VALIDATION Usual assets in query rewriting (ontologies and queries) Adolena, path1, path5, StockExchange, University (LUBM), Vicodi. Added ontologies (marked with “X”) using auxiliar predicates for qualified existentials (Calvanese et al., 2007) Added not so usual ontologies: Galen-Lite, Core Added ontologies in ELHIO and beyond OWL2 QL (marked with “E”) From 5 to 9 queries for each Hypothesis: H2. Additional computations can be done during query rewriting so as to obtain a gain in efficiency that compensates for the cost of these computations jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 33 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 34 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References SOA: EBOX DEFINITION ABoxes may present dependencies in their completeness (Rodr´ıguez-Muro and Calvanese, 2011) An EBox specifies the extensional constraints of the ABox (Rosati, 2012) Example: TBox: {PhDStudent Student} ABox: PhDStudents: Foo, Bar. Students: Bar. EBox: {Student PhDStudent} The set of PhDStudents contains all Students, extensionally jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 35 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References SOA: EBOX DEFINITION ABoxes may present dependencies in their completeness (Rodr´ıguez-Muro and Calvanese, 2011) An EBox specifies the extensional constraints of the ABox (Rosati, 2012) Example: TBox: {PhDStudent Student} ABox: PhDStudents: Foo, Bar. Students: Bar. EBox: {Student PhDStudent} The set of PhDStudents contains all Students, extensionally jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 35 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References SOA: EBOX DEFINITION ABoxes may present dependencies in their completeness (Rodr´ıguez-Muro and Calvanese, 2011) An EBox specifies the extensional constraints of the ABox (Rosati, 2012) Example: TBox: {PhDStudent Student} ABox: PhDStudents: Foo, Bar. Students: Bar. EBox: {Student PhDStudent} The set of PhDStudents contains all Students, extensionally jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 35 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References OBDA CONTEXT ontology preprocess clauses QUERY REWRITING rewritten query Mappings query query translation translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 36 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References OBDA CONTEXT ontology preprocess EBox clauses QUERY REWRITING EBox generation rewritten query Mappings query query translation translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 36 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING PROCESS ontology convert to FOL preprocess reachability test query remove functions remove auxiliar predicates + reachability test unfold Datalog UCQ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 37 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING PROCESS ontology SCC removal EBox convert to Datalog unfold use EBox use EBox convert to FOL preprocess reachability test query remove functions Datalog UCQ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 37 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS Strongly Connected Component (SCC): set of vertexes in a directed graph that can be accessed from each other Two conditions for removal (ensure no intensional values) SCC to be removed cannot be accessible from another Whole SCC needs to be implied by the EBox Analogous in DL and Datalog Vertexes are axioms/clauses Edges are shared predicates Direction: from head to body For the example, clauses: γ: GradStudent Student ϕ: PhDStudent GradStudent imply: γ ϕ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 38 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS Strongly Connected Component (SCC): set of vertexes in a directed graph that can be accessed from each other Two conditions for removal (ensure no intensional values) SCC to be removed cannot be accessible from another Whole SCC needs to be implied by the EBox Analogous in DL and Datalog Vertexes are axioms/clauses Edges are shared predicates Direction: from head to body For the example, clauses: γ: GradStudent Student ϕ: PhDStudent GradStudent imply: γ ϕ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 38 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS Strongly Connected Component (SCC): set of vertexes in a directed graph that can be accessed from each other Two conditions for removal (ensure no intensional values) SCC to be removed cannot be accessible from another Whole SCC needs to be implied by the EBox Analogous in DL and Datalog Vertexes are axioms/clauses Edges are shared predicates Direction: from head to body For the example, clauses: γ: GradStudent Student ϕ: PhDStudent GradStudent imply: γ ϕ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 38 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS Strongly Connected Component (SCC): set of vertexes in a directed graph that can be accessed from each other Two conditions for removal (ensure no intensional values) SCC to be removed cannot be accessible from another Whole SCC needs to be implied by the EBox Analogous in DL and Datalog Vertexes are axioms/clauses Edges are shared predicates Direction: from head to body For the example, clauses: γ: GradStudent Student ϕ: PhDStudent GradStudent imply: γ ϕ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 38 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS: EXAMPLE A BC Q DE FG H I jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 39 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS: EXAMPLE A BC Q DEE FFG HH jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 39 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS: EXAMPLE A BC Q DEE FFGG HH jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 39 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS: EXAMPLE A BC Q D jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 39 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EXTENSIONALLY EMPTY PREDICATES REMOVAL Analogous to the removal of non-mapped predicates Extended to preprocessing Possibility of cycles with function symbols Keep predicates in the heads for reachability Example: Mapped {PhDStudent, researches} Ontology: ∃researches.PhDThesis PhDStudent ∃researches− .PhDStudent PhDThesis Ontology (Datalog): PhDStudent(x) ← researches(x, y), PhDThesis(y) PhDThesis(x) ← researches(y, x), PhDStudent(y) Resolves: PhDStudent(x) ← researches(x, z), researches(z, y), PhDStudent(y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 40 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EXTENSIONALLY EMPTY PREDICATES REMOVAL Analogous to the removal of non-mapped predicates Extended to preprocessing Possibility of cycles with function symbols Keep predicates in the heads for reachability Example: Mapped {PhDStudent, researches} Ontology: ∃researches.PhDThesis PhDStudent ∃researches− .PhDStudent PhDThesis Ontology (Datalog): PhDStudent(x) ← researches(x, y), PhDThesis(y) PhDThesis(x) ← researches(y, x), PhDStudent(y) Resolves: PhDStudent(x) ← researches(x, z), researches(z, y), PhDStudent(y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 40 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EXTENSIONALLY SUBSUMED CLAUSES REMOVAL Analogous to the removal of subsumed clauses More costly: requires resolution with EBox clauses Detected when some clause is generated from a clause and the purely extensional EBox When detected: delete Example: Query: Q(x) ← Student(x) TBox: PhDStudent Student EBox: Student PhDStudent jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 41 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EXTENSIONALLY SUBSUMED CLAUSES REMOVAL Analogous to the removal of subsumed clauses More costly: requires resolution with EBox clauses Detected when some clause is generated from a clause and the purely extensional EBox When detected: delete Example: Query: Q(x) ← Student(x) TBox: PhDStudent Student EBox: Student PhDStudent jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 41 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EXTENSIONAL CONDENSATION OF CLAUSES clause subsumption clause condensation extensional clause subsumption extensional clause condensation Detected when some subsuming clause is generated from a clause and the purely extensional EBox When detected: replace Example: Query: Q(x, y) ← Student(x), takesCourse(x, y), Course(y) Consider big taxonomies for both Student and Course EBox: ∃takesCourse Student ∃takesCourse− Course Rewritten query: Q(x, y) ← takesCourse(x, y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 42 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EXTENSIONAL CONDENSATION OF CLAUSES clause subsumption clause condensation extensional clause subsumption extensional clause condensation Detected when some subsuming clause is generated from a clause and the purely extensional EBox When detected: replace Example: Query: Q(x, y) ← Student(x), takesCourse(x, y), Course(y) Consider big taxonomies for both Student and Course EBox: ∃takesCourse Student ∃takesCourse− Course Rewritten query: Q(x, y) ← takesCourse(x, y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 42 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION SETUP Same queries. Extended previous assets with synthetically-generated EBoxes, according to parameters: SIZE ratio between EBox and TBox sizes (in axioms), e.g. size 2 means that EBox size is twice the TBox size COVER percentage of axioms extracted from the TBox, remaining axioms are generated randomly from the concepts REVERSE among the axioms extracted, percentage of axioms that will have their left hand side and right hand side swapped For the evaluation: Values for parameters have been 0, 0.2, 0.4, 0.6, 0.8 and 1 If a parameter is 0 then following parameters are 0 too Hypothesis: H3. EBoxes may allow reducing the size of the rewritten queries and the time needed in the query process jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 43 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION: OPT. WITH AN EBOX (RESULTS)query Datalog Datalog UCQ UCQ time(ms) size time(ms) size I II III IV I II III IV I II III IV I II III IV 1 0 0 157 235 15 13 14 9 0 0 516 672 15 13 14 9 2 16 16 157 234 10 10 10 10 16 16 500 656 10 10 10 10 3 0 0 125 235 35 30 28 15 31 15 485 813 72 57 54 15 4 0 16 219 188 41 38 22 16 63 94 735 719 185 170 3 42 5 16 16 172 250 8 5 7 1 32 31 609 719 30 9 15 1 6 0 15 234 188 18 14 14 11 0 15 578 641 18 14 14 11 7 0 0 125 172 27 23 23 20 94 125 1359 1359 180 140 140 110 Query independent information EBox I II III IV Preprocess time 109 2047 24266 2859 Preprocess size 222 195 171 111 size ratio 0.0 0.2 0.8 0.8 cover % 0.0 0.8 0.2 0.8 reverse % 0.0 0.0 0.0 0.0 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 44 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION: OPT. WITH AN EBOX (RESULTS)query Datalog Datalog UCQ UCQ time(ms) size time(ms) size I II III IV I II III IV I II III IV I II III IV 1 0 0 157 235 15 13 14 9 0 0 516 672 15 13 14 9 2 16 16 157 234 10 10 10 10 16 16 500 656 10 10 10 10 3 0 0 125 235 35 30 28 15 31 15 485 813 72 57 54 15 4 0 16 219 188 41 38 22 16 63 94 735 719 185 170 3 42 5 16 16 172 250 8 5 7 1 32 31 609 719 30 9 15 1 6 0 15 234 188 18 14 14 11 0 15 578 641 18 14 14 11 7 0 0 125 172 27 23 23 20 94 125 1359 1359 180 140 140 110 Query independent information EBox I II III IV Preprocess time 109 2047 24266 2859 Preprocess size 222 195 171 111 size ratio 0.0 0.2 0.8 0.8 cover % 0.0 0.8 0.2 0.8 reverse % 0.0 0.0 0.0 0.0 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 44 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION, AGGREGATED RESULTS 0 0.2 0.4 0.6 0.8 1 102.3 102.4 102.5 size of the EBox Averagetime(ms)toproducetheDatalogrewriting cover 0 0.2 0.4 0.6 0.8 1 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 45 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION, AGGREGATED RESULTS 0 0.2 0.4 0.6 0.8 1 102.3 102.4 102.5 size of the EBox Averagetime(ms)toproducetheDatalogrewriting cover 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 103 104 size of the EBox Averagetime(ms)toproducetheUCQrewriting cover 0 0.2 0.4 0.6 0.8 1 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 45 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION, AGGREGATED RESULTS 0 0.2 0.4 0.6 0.8 1 102.3 102.4 102.5 size of the EBox Averagetime(ms)toproducetheDatalogrewriting cover 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 103 104 size of the EBox Averagetime(ms)toproducetheUCQrewriting cover 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 101 101.2 101.4 size of the EBox AverageclausesintheDatalogrewriting cover 0 0.2 0.4 0.6 0.8 1 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 45 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References EVALUATION, AGGREGATED RESULTS 0 0.2 0.4 0.6 0.8 1 102.3 102.4 102.5 size of the EBox Averagetime(ms)toproducetheDatalogrewriting cover 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 103 104 size of the EBox Averagetime(ms)toproducetheUCQrewriting cover 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 101 101.2 101.4 size of the EBox AverageclausesintheDatalogrewriting cover 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 101.5 102 102.5 size of the EBox AverageclausesintheUCQrewriting cover 0 0.2 0.4 0.6 0.8 1 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 45 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESIS VALIDATION Same queries. Extended previous assets with synthetically-generated EBoxes, according to parameters: SIZE ratio between EBox and TBox sizes (in axioms), e.g. size 2 means that EBox size is twice the TBox size COVER percentage of axioms extracted from the TBox, remaining axioms are generated randomly from the concepts REVERSE among the axioms extracted, percentage of axioms that will have their left hand side and right hand side swapped For the evaluation: Values for parameters have been 0, 0.2, 0.4, 0.6, 0.8 and 1 If a parameter is 0 then following parameters are 0 too Hypothesis: H3. EBoxes may allow reducing the size of the rewritten queries and the time needed in the query process jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 46 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESIS VALIDATION Same queries. Extended previous assets with synthetically-generated EBoxes, according to parameters: SIZE ratio between EBox and TBox sizes (in axioms), e.g. size 2 means that EBox size is twice the TBox size COVER percentage of axioms extracted from the TBox, remaining axioms are generated randomly from the concepts REVERSE among the axioms extracted, percentage of axioms that will have their left hand side and right hand side swapped For the evaluation: Values for parameters have been 0, 0.2, 0.4, 0.6, 0.8 and 1 If a parameter is 0 then following parameters are 0 too Hypothesis: H3. EBoxes may allow reducing the size of the rewritten queries and the time needed in the query process jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 46 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 47 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References CONCLUSIONS I Small modifications to the query rewriting process from scientific and engineering points of view, with: Big impact on the results Great applicability Strong focus on a pragmatic perspective, maintaining the expressiveness Theoretically sound and validated. Formalisation: 10 propositions, 1 corollary Empirical evaluations that show the obtained results, full data: http://purl.org/net/jmora/mappings/fao/analysis http://purl.org/net/jmora/kyrie/mappings/analysis http://purl.org/net/jmora/kyrie/optimisations/evaluation http://purl.org/net/jmora/kyrie/benchmark http://purl.org/net/jmora/kyrie/queries/analysis http://purl.org/net/jmora/kyrie/extensional/evaluation Open source code: https://bitbucket.org/jmora/kyrie https://bitbucket.org/jmora/kyrie-aux jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 48 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References CONCLUSIONS II Main publications: Jose Mora and Oscar Corcho. Engineering optimisations in query rewriting for OBDA. In Proceedings of the 9th International Conference on Semantic Systems (I-SEMANTICS 13), pages 4148, Graz, Austria, September 2013. Jose Mora and Oscar Corcho. Towards a systematic benchmarking of ontology-based query rewriting systems. In The 12th International Semantic Web Conference (ISWC2013), pages 369384, Sydney, Australia, October 2013. Jose Mora, Riccardo Rosati, and Oscar Corcho. kyrie2: Query Rewriting under Extensional Constraints in ELHIO. In The 13th International Semantic Web Conference (ISWC2014), pages 568583, Riva del Garda, Italy, October 2014. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 49 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References BETTER EVALUATION Some assets used (often reused) for evaluation in papers, e.g. (P´erez-Urbina et al., 2009) Only systematic approach: (Imprialou et al., 2012) Algorithm to generate queries automatically Good for coverage testing (detected several problems) Queries are generated automatically (chase) All have a similar structure No statement about how OBDA systems are actually used Over this state of the art: Usual assets used, added usual assets out of query rewriting Generated some additional assets to evaluate specific features Meta-evaluation: analysed the characteristics of queries and ontologies Having a set of standard benchmarks would ease evaluation, reproducibility, comparison, etc. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 50 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References BETTER EVALUATION Some assets used (often reused) for evaluation in papers, e.g. (P´erez-Urbina et al., 2009) Only systematic approach: (Imprialou et al., 2012) Algorithm to generate queries automatically Good for coverage testing (detected several problems) Queries are generated automatically (chase) All have a similar structure No statement about how OBDA systems are actually used Over this state of the art: Usual assets used, added usual assets out of query rewriting Generated some additional assets to evaluate specific features Meta-evaluation: analysed the characteristics of queries and ontologies Having a set of standard benchmarks would ease evaluation, reproducibility, comparison, etc. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 50 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References BETTER EVALUATION Some assets used (often reused) for evaluation in papers, e.g. (P´erez-Urbina et al., 2009) Only systematic approach: (Imprialou et al., 2012) Algorithm to generate queries automatically Good for coverage testing (detected several problems) Queries are generated automatically (chase) All have a similar structure No statement about how OBDA systems are actually used Over this state of the art: Usual assets used, added usual assets out of query rewriting Generated some additional assets to evaluate specific features Meta-evaluation: analysed the characteristics of queries and ontologies Having a set of standard benchmarks would ease evaluation, reproducibility, comparison, etc. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 50 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References FUTURE LINES Improve expressiveness (SPIN, OWL2 RL, OWL2 DL, ...) efficiency (process but especially results) awareness of context (provenance, cost model, data quality, ...) standardization (SPARQL, ...) applicability: types of data sources (streams, web services, ...) knowledge available, e.g. predefined queries for visualisation benchmarks jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 51 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING OPTIMISATION TECHNIQUES FOR ONTOLOGY-BASED DATA ACCESS Jos´e Mora L´opez jmora@fi.upm.es Supervisor: Oscar Corcho ocorcho@fi.upm.es Ontology Engineering Group Facultad de Inform´atica, Universidad Polit´ecnica de Madrid Madrid - December 17, 2015 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 52 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References FOL ELHIO A(a) A(a), {a} A P(a, b) P(a, b) x≈a ← A(x) A {a} A2(x) ← A1(x) A1 A2 A3(x) ← A1(x) ∧ A2(x) A1 A2 A3 P(x, f(x)) ← A(x) A ∃P P(x, f(x)) ← A1(x),A2(f(x)) ← A1(x) A1 ∃P.A2 P(f(x), x) ← A A ∃P− P(f(x), x) ← A1(x),A2(f(x)) ← A1(x) A1 ∃P− .A2 A(x) ← P(x, y) ∃P A A2(x) ← P(x, y), A1(y) ∃P.A1 A2 A(x) ← P(y, x) ∃P− A A2(x) ← P(y, x), A1(y) ∃P− .A1 A2 S(x, y) ← P(x, y) P S, P− S− S(x, y) ← P(y, x) P S− , P S− jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 53 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References RESEARCH QUESTIONS 1. Can the information about mappings be useful for query rewriting in an OBDA context? 2. Can the information from mappings be used in an efficient way? 3. How can we tune current algorithms for query rewriting in OBDA? 4. How can we measure the appropriateness of the solutions to the query rewriting problem? 5. Can EBoxes be used in query rewriting? 6. What is the impact of the different EBoxes? jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 54 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References ASSUMPTIONS AND LIMITATIONS A1. We can obtain an exhaustive list of the predicates in the ontology that are mapped to some data source for a set of mappings. A2. Predicates that are not mapped cannot be retrieved from the data source. A3. EBoxes can be derived from schemata and data available in data sources. A4. Set semantics: redundant answers have no impact on the correctness. A5. Some predicates in the ontology may not be mapped to the data source. L1. We restrict the input queries to UCQs and output to Datalog. Unfolding of the output to UCQs will be attempted. L2. The mapping-based reduction in time and size for rewriting depends on how many ontology predicates are mapped. L3. The impact of additional computations in query rewriting may be negative in some cases, e.g. those specifically designed for that. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 55 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References ASSUMPTIONS AND LIMITATIONS A1. We can obtain an exhaustive list of the predicates in the ontology that are mapped to some data source for a set of mappings. A2. Predicates that are not mapped cannot be retrieved from the data source. A3. EBoxes can be derived from schemata and data available in data sources. A4. Set semantics: redundant answers have no impact on the correctness. A5. Some predicates in the ontology may not be mapped to the data source. L1. We restrict the input queries to UCQs and output to Datalog. Unfolding of the output to UCQs will be attempted. L2. The mapping-based reduction in time and size for rewriting depends on how many ontology predicates are mapped. L3. The impact of additional computations in query rewriting may be negative in some cases, e.g. those specifically designed for that. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 55 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References FULL LIST OF PUBLICATIONS I 1. Domenico Lembo, Jose Mora, Riccardo Rosati, Domenico Fabio Savo, and Evgenij Thorstensen. Mapping analysis in ontology-based data access: Algorithms and complexity. In ISWC, Bethlehem, Pennsylvania, USA, 2015 2. Ernesto Jimenez-Ruiz, Evgeny Kharlamov, Dmitriy Zheleznyakov, Christoph Pinkel, Martin Skjæveland, Evgenij Thorstensen, Jose Mora, and Ian Horrocks. Bootox: Bootstrapping owl 2 ontologies and r2rml mappings from relational databases. In ISWC, Bethlehem, Pennsylvania, USA, 2015 3. Jose Mora, Riccardo Rosati, and Oscar Corcho. kyrie2: Query rewriting under extensional constraints in ELHIO. In ISWC, Trentino, Italy, 2014 4. Marco Console, Jose Mora, Riccardo Rosati, Valerio Santarelli, and Domenico Fabio Savo. Effective computation of maximal sound approximations of description logic ontologies. In ISWC, Trentino, Italy, 2014 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 56 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References FULL LIST OF PUBLICATIONS II 5. Domenico Lembo, Jose Mora, Riccardo Rosati, Domenico Fabio Savo, and Evgenij Thorstensen. Towards mapping analysis in ontology-based data access. In Web Reason- ing and Rule Systems, Athens, Greece, 2014 6. Jose Mora and Oscar Corcho. Towards a systematic benchmarking of ontology-based query rewriting systems. In ISWC, Sydney, Australia, 2013 7. Jose Mora and Oscar Corcho. Engineering optimisations in query rewriting for OBDA. In I-SEMANTICS ’13, Graz, Austria, 2013 8. Ghislain Atemezing, Oscar Corcho, Daniel Garijo, Jose Mora, Mar´ıa Poveda-Villal´on, Pablo Rozas, Daniel Vila-Suero, and Boris Villaz´on-Terrazas. Transforming meteorological data into linked data. Semantic Web, 2012 9. Jose Mora, Jos´e ´Angel Ramos, and Guadalupe Aguado de Cea. Enhancing the expressiveness of linguistic structures. In E-LKR’12), Castell´on de la Plana, Spain, 2012 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 57 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References FULL LIST OF PUBLICATIONS III 10. B. Villaz´on-Terrazas, D. Vila-Suero, D. Garijo, L. M. Vilches-Bl´azquez, M. Poveda- Villal´on, J. Mora, O. Corcho, and A. G´omez-P´erez. Publishing linked data-there is no one-size-fits-all formula. European Data Forum, 2012 11. Alexander de Le´on, Victor Saquicela, Luis M. Vilches, Boris Villaz´on-Terrazas, Freddy Priyatna, Oscar Corcho, Carlos Buil, Jose Mora, and Jean-Paul Calbimonte. Geographical linked data: a spanish use case. In I-SEMANTICS ’10, Graz, Austria, 2010 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 58 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References REFERENCES I Diego Calvanese, Giuseppe De Giacomo, Domenico Lembo, Maurizio Lenzerini, and Riccardo Rosati. Tractable Reasoning and Efficient Query Answering in Description Logics: The DL-Lite Family. Journal of Automated Reasoning, 39(3):385–429, October 2007. doi: 10.1007/s10817-007-9078-x. Alexandros Chortaras, Despoina Trivela, and Giorgos Stamou. Optimized Query Rewriting for OWL 2 QL. In Automated Deduction – CADE-23, volume 6803, pages 192–206. Springer, 2011. ISBN 978-3-642-22437-9. Thomas Eiter, Magdalena Ortiz, Mantas Simkus, Trung-Kien Tran, and Guohui Xiao. Query rewriting for Horn-SHIQ plus rules. In 26th AAAI Conference on Artificial Intelligence, 2012. Georg Gottlob, Giorgio Orsi, and Andreas Pieris. Ontological Queries: Rewriting and Optimization (Extended Version). arXiv:1112.0343, December 2011. Martha Imprialou, Giorgos Stoilos, and Bernardo Cuenca Grau. Benchmarking ontology-based query rewriting systems. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI, 2012. Jose Mora and Oscar Corcho. Towards a systematic benchmarking of ontology-based query rewriting systems. In The 12th International Semantic Web Conference (ISWC2013), pages 369–384, Sydney, Australia, 2013. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 59 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References REFERENCES II Jose Mora, Riccardo Rosati, and Oscar Corcho. kyrie2: Query Rewriting under Extensional Constraints in ELHIO. In The 13th International Semantic Web Conference (ISWC2014), Trentino, Italy, 2014. H´ector P´erez-Urbina, Ian Horrocks, and Boris Motik. Efficient Query Answering for OWL 2. In Lecture Notes in Computer Science, volume 5823, pages 489–504. Springer, 2009. Mariano Rodr´ıguez-Muro and Diego Calvanese. Dependencies: Making ontology based data access work in practice. Proc. of the 5th Alberto Mendelzon Int. Workshop on Foundations of Data Management, 2011. Riccardo Rosati. Prexto: Query Rewriting under Extensional Constraints in DL-Lite. In The Semantic Web: Research and Applications, volume 7295 of Lecture Notes in Computer Science, pages 360–374. Springer Berlin / Heidelberg, 2012. ISBN 978-3-642-30283-1. Riccardo Rosati and Alessandro Almatelli. Improving Query Answering over DL-Lite Ontologies. In Proceedings of the Twelfth International Conference on the Principles of Knowledge Representation and Reasoning. AAAI Press, 2010. Tassos Venetis, Giorgos Stoilos, and Giorgos Stamou. Query rewriting under query extensions for OWL 2 QL ontologies. In The 7th International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS 2011), page 59, 2011. Luis Manuel Vilches-Bl´azquez, Miguel ´Angel Bernab´e-Poveda, Mari Carmen Su´arez-Figueroa, Asunci´on G´omez-P´erez, and Antonio F. Rodr´ıguez-Pascual. Towntology hydrOntology: Relationship between Urban and Hydrographic Features in the Geographic Information Domain. Ontologies for Urban Development, 61:73–84, 2007. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 60 / 52
Introduction State of the Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING OPTIMISATION TECHNIQUES FOR ONTOLOGY-BASED DATA ACCESS Jos´e Mora L´opez jmora@fi.upm.es Supervisor: Oscar Corcho ocorcho@fi.upm.es Ontology Engineering Group Facultad de Inform´atica, Universidad Polit´ecnica de Madrid Madrid - December 17, 2015 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 61 / 52

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Query Rewriting Optimisation Techniques for Ontology-Based Data Access

  • 1.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING OPTIMISATION TECHNIQUES FOR ONTOLOGY-BASED DATA ACCESS Jos´e Mora L´opez jmora@fi.upm.es Supervisor: Oscar Corcho ocorcho@fi.upm.es Ontology Engineering Group Facultad de Inform´atica, Universidad Polit´ecnica de Madrid Madrid - December 17, 2015 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 1 / 52
  • 2.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING IN OBDA QUERY REWRITING ontology rewritten query Mappings query query translation translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 2 / 52
  • 3.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References OUTLINE 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 3 / 52
  • 4.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 4 / 52
  • 5.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 5 / 52
  • 6.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References USUAL LOGICS IN OBDA axiom1 logic DL-Litecore DL-LiteF DL-LiteR Rapid’s ELHIO Datalog±2 Horn-SHIQ B1 B2, B1 ¬B2 ≥ 2R1 ⊥ R1 R2, R1 ¬R2 B1 ∃R1, ∃R1 B1 B1 ∃R1.B2 ∃R1.B1 B2 B1 B2 B3 {a} B, B {a}, B(a) n-ary predicates trans(R1) B1 ∀R1.B2, B1 ≤ 1R1.B2 1∀i, j, Bi represents a basic concept and Rj represents a role that may be basic or inverted. 2as implemented in Nyaya jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 6 / 52
  • 7.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References SYSTEMS IN THE STATE OF THE ART System Input Output Reference Quonto DL-LiteR UCQ Calvanese et al. (2007) REQUIEM ELHIO Datalog or UCQ P´erez-Urbina et al. (2009) Presto DL-LiteR Datalog Rosati and Almatelli (2010) Rapid DL-LiteR 3 Datalog or UCQ Chortaras et al. (2011) Nyaya Datalog± UCQ Gottlob et al. (2011) Venetis’ DL-LiteR UCQ Venetis et al. (2011) Prexto DL-LiteR and EBox Datalog or UCQ Rosati (2012) Clipper Horn-SHIQ Datalog Eiter et al. (2012) kyrie ELHIO Datalog or UCQ Mora and Corcho (2013) kyrie2 ELHIO and EBox Datalog or UCQ Mora et al. (2014) 3Close to OWL2 QL, B1 ∃R.B2 axioms are supported jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 7 / 52
  • 8.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References GENERAL QUERY REWRITING ALGORITHM ontology convert to internal rep- resentation query reachability test rewrite into Datalog Datalog unfold into UCQ UCQ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 8 / 52
  • 9.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References REQUIEM QUERY REWRITING ALGORITHM query reachability convert to FOL ontology remove functions greedy unfold greedy mode? na¨ıve unfold na¨ıve mode? Datalog optimise UCQ YesNo Yes No jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 9 / 52
  • 10.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References LIMITATIONS IN THE STATE OF THE ART 1. Lack of effectiveness, e.g. rewriting to empty predicates 2. Redundancy, e.g. retrieving the same values multiple times in a query 3. Lack of efficiency, e.g. doing operations that lead to previous points jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 10 / 52
  • 11.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 11 / 52
  • 12.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References OBJECTIVES Improve existing query rewriting techniques by: 1. Using the information provided by mappings 2. Using more efficient query rewriting algorithms 3. Using an EBox to avoid redundant answers 4. Using ELHIO, for the applicability of results both in theory and practice jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 12 / 52
  • 13.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESES AND CONTRIBUTIONS H1. Knowing the presence or absence of a predicate mappings allows reducing the size of the rewritten queries and the time needed to produce them H2. Additional computations can be done during query rewriting so as to obtain a gain in efficiency that compensates for the cost of these computations H3. EBoxes may allow reducing the size of the rewritten queries and the time needed in the query process C1. An algorithm4 that considers the presence of mappings to improve results and process of query rewriting C2. An algorithm including several optimisations in the process of query rewriting C3. An algorithm that uses the knowledge in an EBox to remove redundancy in the rewritten queries and the query rewriting process 4All algorithms were empirically evaluated and formally validated jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 13 / 52
  • 14.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESES AND CONTRIBUTIONS H1. Knowing the presence or absence of a predicate mappings allows reducing the size of the rewritten queries and the time needed to produce them H2. Additional computations can be done during query rewriting so as to obtain a gain in efficiency that compensates for the cost of these computations H3. EBoxes may allow reducing the size of the rewritten queries and the time needed in the query process C1. An algorithm4 that considers the presence of mappings to improve results and process of query rewriting C2. An algorithm including several optimisations in the process of query rewriting C3. An algorithm that uses the knowledge in an EBox to remove redundancy in the rewritten queries and the query rewriting process 4All algorithms were empirically evaluated and formally validated jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 13 / 52
  • 15.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 14 / 52
  • 16.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References OBDA CONTEXT QUERY REWRITING ontology rewritten query Mappings query query translation translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 15 / 52
  • 17.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References OBDA CONTEXT QUERY REWRITING ontology rewritten query Mappings query query translation translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 15 / 52
  • 18.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING ALGORITHM query reachability convert to FOL ontology remove functions greedy unfold greedy mode? na¨ıve unfold na¨ıve mode? Datalog optimise UCQ YesNo Yes No jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 16 / 52
  • 19.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING ALGORITHM query reachability convert to FOL mappings ontology remove functions remove non-mapped predicates greedy unfold greedy mode? na¨ıve unfold na¨ıve mode? Datalog optimise UCQ YesNo Yes No jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 16 / 52
  • 20.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References TEST CASES IN FAO Ontology Concepts Object Properties Datatype Properties Group5 Num. Mapped Coverage Num. Map. Coverage Num. Map. Coverage Total 1 7 5 28.57% 15 14 6.67% 126 100 20.63% 19.59% 2 3 1 66.67% 1 0 100.00% 8 2 75.00% 75.00% 3 6 0 100.00% 25 9 64.00% 30 10 66.67% 68.85% 4 5 0 100.00% 20 8 60.00% 90 32 64.44% 65.22% 5 7 3 57.14% 0 0 0.00% 104 70 32.69% 34.23% 6 5 0 100.00% 20 8 60.00% 90 32 64.44% 65.22% 7 3 1 66.67% 1 0 100.00% 8 2 75.00% 75.00% 8 16 15 6.25% 0 0 0.00% 201 198 1.49% 1.84% 9 16 15 6.25% 0 0 0.00% 201 198 1.49% 1.84% Total 68 40 41.18% 82 39 52.44% 858 644 24.94% 28.27% 5http://purl.org/net/jmora/mappings/fao/analysis jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 17 / 52
  • 21.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References MAPPING-BASED REACHABILITY Principle: predicates that provide no answers are not needed Two working modes: H (highest): first mapped predicate stops, following ones are contained L (lowest): last mapped predicate stops, following ones are empty Example Query: Q(x) ← Student(x) Mapped predicates: GradStudent, PhDStudent Ontology: GradStudent Student PhDStudent GradStudent MasterStudent GradStudent H rewriting Q(x) ← GradStudent(x) L rewriting Q(x) ← GradStudent(x) Q(x) ← PhDStudent(x) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 18 / 52
  • 22.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References MAPPING-BASED REACHABILITY Principle: predicates that provide no answers are not needed Two working modes: H (highest): first mapped predicate stops, following ones are contained L (lowest): last mapped predicate stops, following ones are empty Example Query: Q(x) ← Student(x) Mapped predicates: GradStudent, PhDStudent Ontology: GradStudent Student PhDStudent GradStudent MasterStudent GradStudent H rewriting Q(x) ← GradStudent(x) L rewriting Q(x) ← GradStudent(x) Q(x) ← PhDStudent(x) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 18 / 52
  • 23.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References RESOLUTION WITH MAPPINGS For each non-mapped predicate p 1. Check that all clauses with p in the head have all their body predicates mapped Otherwise: select a different predicate Break cycles: choose one predicate per cycle to exclude from resolution 2. Resolve selecting p 3. Clauses containing p can be removed All clauses containing predicates selected for resolution can be deleted Example: Mapped {PhDStudent, researches} TBox: ∃researches.PhDThesis PhDStudent ∃researches− .PhDStudent PhDThesis Ontology (Datalog): PhDStudent(x) ← researches(x, y), PhDThesis(y) PhDThesis(x) ← researches(y, x), PhDStudent(y) Resolves: PhDStudent(x) ← researches(x, z), researches(z, y), PhDStudent(y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 19 / 52
  • 24.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References RESOLUTION WITH MAPPINGS For each non-mapped predicate p 1. Check that all clauses with p in the head have all their body predicates mapped Otherwise: select a different predicate Break cycles: choose one predicate per cycle to exclude from resolution 2. Resolve selecting p 3. Clauses containing p can be removed All clauses containing predicates selected for resolution can be deleted Example: Mapped {PhDStudent, researches} TBox: ∃researches.PhDThesis PhDStudent ∃researches− .PhDStudent PhDThesis Ontology (Datalog): PhDStudent(x) ← researches(x, y), PhDThesis(y) PhDThesis(x) ← researches(y, x), PhDStudent(y) Resolves: PhDStudent(x) ← researches(x, z), researches(z, y), PhDStudent(y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 19 / 52
  • 25.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION SETUP Ontology: hydrOntology (Vilches-Bl´azquez et al., 2007) 155 concepts expressiveness SHIN(D) several mappings The National Atlas data source, with 32 mappings, 1,100 hydrographical instances The Numerical Cartographic Database (BCN200) with 57 mappings that provide 60,000 toponyms EuroGlobalMap (EGM), 32 mappings that provide 3,500 Spanish hydrographical toponyms Hypothesis: H1. Knowing the presence or absence of a predicate mappings allows reducing the size of the rewritten queries and the time needed to produce them jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 20 / 52
  • 26.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION RESULTS Contextual Information Ontology: HydrOntology Original Datalog size: 686 clauses Ignored statements: 405 Time unit: Milliseconds Working modes: Naive Greedy Full Mappings None BCN200 Atlas EGM Mappings # 0 57 32 32 Prune method A H L H L H L Mode Phase Clauses generated after each phase N Prune 535 323 445 287 415 336 429 Saturation 412 25 50 17 24 19 31 Unfolding 2333 25 46 17 22 19 28 Prune 2333 25 46 17 22 19 28 G Prune 535 323 445 287 415 336 429 Saturation 407 25 49 15 24 19 30 Unfolding 894 25 45 15 22 19 27 Prune 688 25 45 15 22 19 27 F Prune 535 323 445 287 415 336 429 Saturation 407 25 49 15 24 19 30 Unfolding 2245 25 45 15 22 19 27 Prune 911 25 45 15 22 19 27 N Total time 2735 172 625 156 359 250 625 G Total time 2250 172 512 156 360 218 532 F Total time 3719 172 531 156 344 235 531 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 21 / 52
  • 27.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION RESULTS Contextual Information Ontology: HydrOntology Original Datalog size: 686 clauses Ignored statements: 405 Time unit: Milliseconds Working modes: Naive Greedy Full Mappings None BCN200 Atlas EGM Mappings # 0 57 32 32 Prune method A H L H L H L Mode Phase Clauses generated after each phase N Prune 535 323 445 287 415 336 429 Saturation 412 25 50 17 24 19 31 Unfolding 2333 25 46 17 22 19 28 Prune 2333 25 46 17 22 19 28 G Prune 535 323 445 287 415 336 429 Saturation 407 25 49 15 24 19 30 Unfolding 894 25 45 15 22 19 27 Prune 688 25 45 15 22 19 27 F Prune 535 323 445 287 415 336 429 Saturation 407 25 49 15 24 19 30 Unfolding 2245 25 45 15 22 19 27 Prune 911 25 45 15 22 19 27 N Total time 2735 172 625 156 359 250 625 G Total time 2250 172 512 156 360 218 532 F Total time 3719 172 531 156 344 235 531 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 21 / 52
  • 28.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESIS VALIDATED Ontology: hydrOntology (Vilches-Bl´azquez et al., 2007) 155 concepts expressiveness SHIN(D) several mappings The National Atlas data source, with 32 mappings, 1,100 hydrographical instances The Numerical Cartographic Database (BCN200) with 57 mappings that provide 60,000 toponyms EuroGlobalMap (EGM), 32 mappings that provide 3,500 Spanish hydrographical toponyms Hypothesis: H1. Knowing the presence or absence of a predicate mappings allows reducing the size of the rewritten queries and the time needed to produce them jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 22 / 52
  • 29.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESIS VALIDATED Ontology: hydrOntology (Vilches-Bl´azquez et al., 2007) 155 concepts expressiveness SHIN(D) several mappings The National Atlas data source, with 32 mappings, 1,100 hydrographical instances The Numerical Cartographic Database (BCN200) with 57 mappings that provide 60,000 toponyms EuroGlobalMap (EGM), 32 mappings that provide 3,500 Spanish hydrographical toponyms Hypothesis: H1. Knowing the presence or absence of a predicate mappings allows reducing the size of the rewritten queries and the time needed to produce them jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 22 / 52
  • 30.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 23 / 52
  • 31.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References OBDA CONTEXT QUERY REWRITING ontology rewritten query Mappings query query translation translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 24 / 52
  • 32.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References OBDA CONTEXT QUERY REWRITING ontology rewritten query Mappings query query translation preprocess clauses translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 24 / 52
  • 33.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING PROCESS query reachability test remove functions greedy unfold greedy mode? na¨ıve unfold na¨ıve mode? Datalog convert to FOL ontology optimise UCQ YesNo Yes No jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 25 / 52
  • 34.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING PROCESS query reachability test remove functions remove auxiliar predicates reachability test unfold preprocess Datalog UCQ convert to FOL ontology jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 25 / 52
  • 35.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References ONTOLOGY PREPROCESSING Some inferences can be done without the query This saves some time for later processing Entirely in FOL Example: Mapped {PhDStudent, researches} TBox: ∃researches.PhDThesis PhDStudent ∃researches− .PhDStudent PhDThesis Ontology (Datalog): PhDStudent(x) ← researches(x, y), PhDThesis(y) PhDThesis(x) ← researches(y, x), PhDStudent(y) Resolves: PhDStudent(x) ← researches(x, z), researches(z, y), PhDStudent(y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 26 / 52
  • 36.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References ONTOLOGY PREPROCESSING Some inferences can be done without the query This saves some time for later processing Entirely in FOL Example: Mapped {PhDStudent, researches} TBox: ∃researches.PhDThesis PhDStudent ∃researches− .PhDStudent PhDThesis Ontology (Datalog): PhDStudent(x) ← researches(x, y), PhDThesis(y) PhDThesis(x) ← researches(y, x), PhDStudent(y) Resolves: PhDStudent(x) ← researches(x, z), researches(z, y), PhDStudent(y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 26 / 52
  • 37.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References CLAUSE SUBSUMPTION CHECK Checked as soon as a new clause is generated Deleted clauses do not participate in resolution Incremental effect Example: Query: Q(x, y) ← Student(x), takesCourse(x, y), Course(y) Consider big taxonomies for both Student and Course TBox: ∃takesCourse Student ∃takesCourse− Course Rewritten query: Q(x, y) ← takesCourse(x, y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 27 / 52
  • 38.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References CLAUSE SUBSUMPTION CHECK Checked as soon as a new clause is generated Deleted clauses do not participate in resolution Incremental effect Example: Query: Q(x, y) ← Student(x), takesCourse(x, y), Course(y) Consider big taxonomies for both Student and Course TBox: ∃takesCourse Student ∃takesCourse− Course Rewritten query: Q(x, y) ← takesCourse(x, y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 27 / 52
  • 39.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References PRIORITISING SOME INFERENCES Free slection resolution accepts some degree of freedom in order Prioritising resolution with shorter clauses increases likelihood of producing subsuming clauses Increases effect of previous optimisation Example: Query: Q(x, y) ← Student(x), takesCourse(x, y), Course(y) Consider big taxonomies for both Student and Course TBox: ∃takesCourse Student ∃takesCourse− Course Rewritten query: Q(x, y) ← takesCourse(x, y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 28 / 52
  • 40.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References PRIORITISING SOME INFERENCES Free slection resolution accepts some degree of freedom in order Prioritising resolution with shorter clauses increases likelihood of producing subsuming clauses Increases effect of previous optimisation Example: Query: Q(x, y) ← Student(x), takesCourse(x, y), Course(y) Consider big taxonomies for both Student and Course TBox: ∃takesCourse Student ∃takesCourse− Course Rewritten query: Q(x, y) ← takesCourse(x, y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 28 / 52
  • 41.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References CONSTRAINING SEARCHES Different groups of clauses have different properties query clauses length Keeping them separately means restricting the attention (computation) to a group Most operations select a random clause in a group, e.g. prioritising inferences In our examples selection is not random, sets of clauses are preferred selection is not specific, several clauses in the set grouping clauses in categories is effective and efficient jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 29 / 52
  • 42.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References CONSTRAINING SEARCHES Different groups of clauses have different properties query clauses length Keeping them separately means restricting the attention (computation) to a group Most operations select a random clause in a group, e.g. prioritising inferences In our examples selection is not random, sets of clauses are preferred selection is not specific, several clauses in the set grouping clauses in categories is effective and efficient jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 29 / 52
  • 43.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION SETUP Usual assets in query rewriting (ontologies and queries) Adolena, path1, path5, StockExchange, University (LUBM), Vicodi. Added ontologies (marked with “X”) using auxiliar predicates for qualified existentials (Calvanese et al., 2007) Added not so usual ontologies: Galen-Lite, Core Added ontologies in ELHIO and beyond OWL2 QL (marked with “E”) From 5 to 9 queries for each Hypothesis: H2. Additional computations can be done during query rewriting so as to obtain a gain in efficiency that compensates for the cost of these computations jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 30 / 52
  • 44.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION RESULTS FOR DATALOG REQUIEM(F) Presto Rapid Clipper kyrie O q size time size time size time size time size time 1 19 12 4 7 4 3 2 21 2 0 2 47 16 2 9 2 9 49 19 47 3 3 20 9 8 16 8 12 21 24 20 3 4 64 15 3 12 3 3 63 18 64 3 U 5 53 12 8 15 8 12 53 15 53 0 6 20 12 19 6 21 15 16 18 16 9 7 49 25 22 15 22 18 44 18 45 12 8 10 9 13 6 13 9 10 20 10 3 9 29 15 24 12 24 17 19 20 21 7 1 22 6 7 10 7 3 5 27 5 6 2 52 15 2 15 2 15 54 27 52 3 3 24 15 10 28 10 9 25 28 24 3 4 70 15 6 19 6 12 69 22 70 0 UX 5 56 15 11 15 11 15 56 21 56 12 6 24 18 28 15 27 22 20 19 20 3 7 55 28 29 21 27 21 50 28 51 12 8 11 6 14 12 14 13 11 26 11 1 9 32 15 30 21 30 15 22 46 24 4 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 31 / 52
  • 45.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION RESULTS FOR DATALOG REQUIEM(F) Presto Rapid Clipper kyrie O q size time size time size time size time size time 1 19 12 4 7 4 3 2 21 2 0 2 47 16 2 9 2 9 49 19 47 3 3 20 9 8 16 8 12 21 24 20 3 4 64 15 3 12 3 3 63 18 64 3 U 5 53 12 8 15 8 12 53 15 53 0 6 20 12 19 6 21 15 16 18 16 9 7 49 25 22 15 22 18 44 18 45 12 8 10 9 13 6 13 9 10 20 10 3 9 29 15 24 12 24 17 19 20 21 7 1 22 6 7 10 7 3 5 27 5 6 2 52 15 2 15 2 15 54 27 52 3 3 24 15 10 28 10 9 25 28 24 3 4 70 15 6 19 6 12 69 22 70 0 UX 5 56 15 11 15 11 15 56 21 56 12 6 24 18 28 15 27 22 20 19 20 3 7 55 28 29 21 27 21 50 28 51 12 8 11 6 14 12 14 13 11 26 11 1 9 32 15 30 21 30 15 22 46 24 4 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 31 / 52
  • 46.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION RESULTS FOR UCQ REQUIEM(F) Rapid Prexto Nyaya kyrie O q size time size time size time size time size time 1 2 15 2 3 2 9 2 5 2 0 2 1 103 1 15 1 15 1 1 1 34 3 4 212 4 9 4 18 4 34 4 18 4 2 3762 2 12 2 15 2 4 2 50 U 5 10 13034 10 18 10 15 10 33 10 37 6 29 47 29 28 28 12 40 1595 29 28 7 42 797 42 37 70 18 54 670 42 43 8 10 15 10 18 10 6 10 63 10 3 9 960 1893 960 209 960 928 960 75135 960 1107 1 5 15 5 12 5 12 5 22 5 9 2 1 172 1 12 1 16 1 3 1 37 3 12 2062 12 15 12 28 12 55 12 21 4 5 31422 5 15 5 23 5 6 5 47 UX 5 25 91878 25 27 25 23 25 39 25 46 6 323 468 323 106 448 178 348 2685 323 187 7 1456 37212 224 81 280 75 264 852 224 121 8 20 21 20 23 20 15 20 61 20 9 9 4200 30506 4200 739 4200 21181 4200 366673 4200 16875 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 32 / 52
  • 47.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION RESULTS FOR UCQ REQUIEM(F) Rapid Prexto Nyaya kyrie O q size time size time size time size time size time 1 2 15 2 3 2 9 2 5 2 0 2 1 103 1 15 1 15 1 1 1 34 3 4 212 4 9 4 18 4 34 4 18 4 2 3762 2 12 2 15 2 4 2 50 U 5 10 13034 10 18 10 15 10 33 10 37 6 29 47 29 28 28 12 40 1595 29 28 7 42 797 42 37 70 18 54 670 42 43 8 10 15 10 18 10 6 10 63 10 3 9 960 1893 960 209 960 928 960 75135 960 1107 1 5 15 5 12 5 12 5 22 5 9 2 1 172 1 12 1 16 1 3 1 37 3 12 2062 12 15 12 28 12 55 12 21 4 5 31422 5 15 5 23 5 6 5 47 UX 5 25 91878 25 27 25 23 25 39 25 46 6 323 468 323 106 448 178 348 2685 323 187 7 1456 37212 224 81 280 75 264 852 224 121 8 20 21 20 23 20 15 20 61 20 9 9 4200 30506 4200 739 4200 21181 4200 366673 4200 16875 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 32 / 52
  • 48.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESIS VALIDATION Usual assets in query rewriting (ontologies and queries) Adolena, path1, path5, StockExchange, University (LUBM), Vicodi. Added ontologies (marked with “X”) using auxiliar predicates for qualified existentials (Calvanese et al., 2007) Added not so usual ontologies: Galen-Lite, Core Added ontologies in ELHIO and beyond OWL2 QL (marked with “E”) From 5 to 9 queries for each Hypothesis: H2. Additional computations can be done during query rewriting so as to obtain a gain in efficiency that compensates for the cost of these computations jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 33 / 52
  • 49.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESIS VALIDATION Usual assets in query rewriting (ontologies and queries) Adolena, path1, path5, StockExchange, University (LUBM), Vicodi. Added ontologies (marked with “X”) using auxiliar predicates for qualified existentials (Calvanese et al., 2007) Added not so usual ontologies: Galen-Lite, Core Added ontologies in ELHIO and beyond OWL2 QL (marked with “E”) From 5 to 9 queries for each Hypothesis: H2. Additional computations can be done during query rewriting so as to obtain a gain in efficiency that compensates for the cost of these computations jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 33 / 52
  • 50.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 34 / 52
  • 51.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References SOA: EBOX DEFINITION ABoxes may present dependencies in their completeness (Rodr´ıguez-Muro and Calvanese, 2011) An EBox specifies the extensional constraints of the ABox (Rosati, 2012) Example: TBox: {PhDStudent Student} ABox: PhDStudents: Foo, Bar. Students: Bar. EBox: {Student PhDStudent} The set of PhDStudents contains all Students, extensionally jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 35 / 52
  • 52.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References SOA: EBOX DEFINITION ABoxes may present dependencies in their completeness (Rodr´ıguez-Muro and Calvanese, 2011) An EBox specifies the extensional constraints of the ABox (Rosati, 2012) Example: TBox: {PhDStudent Student} ABox: PhDStudents: Foo, Bar. Students: Bar. EBox: {Student PhDStudent} The set of PhDStudents contains all Students, extensionally jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 35 / 52
  • 53.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References SOA: EBOX DEFINITION ABoxes may present dependencies in their completeness (Rodr´ıguez-Muro and Calvanese, 2011) An EBox specifies the extensional constraints of the ABox (Rosati, 2012) Example: TBox: {PhDStudent Student} ABox: PhDStudents: Foo, Bar. Students: Bar. EBox: {Student PhDStudent} The set of PhDStudents contains all Students, extensionally jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 35 / 52
  • 54.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References OBDA CONTEXT ontology preprocess clauses QUERY REWRITING rewritten query Mappings query query translation translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 36 / 52
  • 55.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References OBDA CONTEXT ontology preprocess EBox clauses QUERY REWRITING EBox generation rewritten query Mappings query query translation translated query query execution data source results results translation translated results jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 36 / 52
  • 56.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING PROCESS ontology convert to FOL preprocess reachability test query remove functions remove auxiliar predicates + reachability test unfold Datalog UCQ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 37 / 52
  • 57.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING PROCESS ontology SCC removal EBox convert to Datalog unfold use EBox use EBox convert to FOL preprocess reachability test query remove functions Datalog UCQ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 37 / 52
  • 58.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS Strongly Connected Component (SCC): set of vertexes in a directed graph that can be accessed from each other Two conditions for removal (ensure no intensional values) SCC to be removed cannot be accessible from another Whole SCC needs to be implied by the EBox Analogous in DL and Datalog Vertexes are axioms/clauses Edges are shared predicates Direction: from head to body For the example, clauses: γ: GradStudent Student ϕ: PhDStudent GradStudent imply: γ ϕ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 38 / 52
  • 59.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS Strongly Connected Component (SCC): set of vertexes in a directed graph that can be accessed from each other Two conditions for removal (ensure no intensional values) SCC to be removed cannot be accessible from another Whole SCC needs to be implied by the EBox Analogous in DL and Datalog Vertexes are axioms/clauses Edges are shared predicates Direction: from head to body For the example, clauses: γ: GradStudent Student ϕ: PhDStudent GradStudent imply: γ ϕ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 38 / 52
  • 60.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS Strongly Connected Component (SCC): set of vertexes in a directed graph that can be accessed from each other Two conditions for removal (ensure no intensional values) SCC to be removed cannot be accessible from another Whole SCC needs to be implied by the EBox Analogous in DL and Datalog Vertexes are axioms/clauses Edges are shared predicates Direction: from head to body For the example, clauses: γ: GradStudent Student ϕ: PhDStudent GradStudent imply: γ ϕ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 38 / 52
  • 61.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS Strongly Connected Component (SCC): set of vertexes in a directed graph that can be accessed from each other Two conditions for removal (ensure no intensional values) SCC to be removed cannot be accessible from another Whole SCC needs to be implied by the EBox Analogous in DL and Datalog Vertexes are axioms/clauses Edges are shared predicates Direction: from head to body For the example, clauses: γ: GradStudent Student ϕ: PhDStudent GradStudent imply: γ ϕ jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 38 / 52
  • 62.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS: EXAMPLE A BC Q DE FG H I jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 39 / 52
  • 63.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS: EXAMPLE A BC Q DEE FFG HH jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 39 / 52
  • 64.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS: EXAMPLE A BC Q DEE FFGG HH jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 39 / 52
  • 65.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References REMOVAL OF SCCS: EXAMPLE A BC Q D jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 39 / 52
  • 66.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EXTENSIONALLY EMPTY PREDICATES REMOVAL Analogous to the removal of non-mapped predicates Extended to preprocessing Possibility of cycles with function symbols Keep predicates in the heads for reachability Example: Mapped {PhDStudent, researches} Ontology: ∃researches.PhDThesis PhDStudent ∃researches− .PhDStudent PhDThesis Ontology (Datalog): PhDStudent(x) ← researches(x, y), PhDThesis(y) PhDThesis(x) ← researches(y, x), PhDStudent(y) Resolves: PhDStudent(x) ← researches(x, z), researches(z, y), PhDStudent(y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 40 / 52
  • 67.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EXTENSIONALLY EMPTY PREDICATES REMOVAL Analogous to the removal of non-mapped predicates Extended to preprocessing Possibility of cycles with function symbols Keep predicates in the heads for reachability Example: Mapped {PhDStudent, researches} Ontology: ∃researches.PhDThesis PhDStudent ∃researches− .PhDStudent PhDThesis Ontology (Datalog): PhDStudent(x) ← researches(x, y), PhDThesis(y) PhDThesis(x) ← researches(y, x), PhDStudent(y) Resolves: PhDStudent(x) ← researches(x, z), researches(z, y), PhDStudent(y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 40 / 52
  • 68.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EXTENSIONALLY SUBSUMED CLAUSES REMOVAL Analogous to the removal of subsumed clauses More costly: requires resolution with EBox clauses Detected when some clause is generated from a clause and the purely extensional EBox When detected: delete Example: Query: Q(x) ← Student(x) TBox: PhDStudent Student EBox: Student PhDStudent jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 41 / 52
  • 69.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EXTENSIONALLY SUBSUMED CLAUSES REMOVAL Analogous to the removal of subsumed clauses More costly: requires resolution with EBox clauses Detected when some clause is generated from a clause and the purely extensional EBox When detected: delete Example: Query: Q(x) ← Student(x) TBox: PhDStudent Student EBox: Student PhDStudent jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 41 / 52
  • 70.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EXTENSIONAL CONDENSATION OF CLAUSES clause subsumption clause condensation extensional clause subsumption extensional clause condensation Detected when some subsuming clause is generated from a clause and the purely extensional EBox When detected: replace Example: Query: Q(x, y) ← Student(x), takesCourse(x, y), Course(y) Consider big taxonomies for both Student and Course EBox: ∃takesCourse Student ∃takesCourse− Course Rewritten query: Q(x, y) ← takesCourse(x, y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 42 / 52
  • 71.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EXTENSIONAL CONDENSATION OF CLAUSES clause subsumption clause condensation extensional clause subsumption extensional clause condensation Detected when some subsuming clause is generated from a clause and the purely extensional EBox When detected: replace Example: Query: Q(x, y) ← Student(x), takesCourse(x, y), Course(y) Consider big taxonomies for both Student and Course EBox: ∃takesCourse Student ∃takesCourse− Course Rewritten query: Q(x, y) ← takesCourse(x, y) jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 42 / 52
  • 72.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION SETUP Same queries. Extended previous assets with synthetically-generated EBoxes, according to parameters: SIZE ratio between EBox and TBox sizes (in axioms), e.g. size 2 means that EBox size is twice the TBox size COVER percentage of axioms extracted from the TBox, remaining axioms are generated randomly from the concepts REVERSE among the axioms extracted, percentage of axioms that will have their left hand side and right hand side swapped For the evaluation: Values for parameters have been 0, 0.2, 0.4, 0.6, 0.8 and 1 If a parameter is 0 then following parameters are 0 too Hypothesis: H3. EBoxes may allow reducing the size of the rewritten queries and the time needed in the query process jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 43 / 52
  • 73.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION: OPT. WITH AN EBOX (RESULTS)query Datalog Datalog UCQ UCQ time(ms) size time(ms) size I II III IV I II III IV I II III IV I II III IV 1 0 0 157 235 15 13 14 9 0 0 516 672 15 13 14 9 2 16 16 157 234 10 10 10 10 16 16 500 656 10 10 10 10 3 0 0 125 235 35 30 28 15 31 15 485 813 72 57 54 15 4 0 16 219 188 41 38 22 16 63 94 735 719 185 170 3 42 5 16 16 172 250 8 5 7 1 32 31 609 719 30 9 15 1 6 0 15 234 188 18 14 14 11 0 15 578 641 18 14 14 11 7 0 0 125 172 27 23 23 20 94 125 1359 1359 180 140 140 110 Query independent information EBox I II III IV Preprocess time 109 2047 24266 2859 Preprocess size 222 195 171 111 size ratio 0.0 0.2 0.8 0.8 cover % 0.0 0.8 0.2 0.8 reverse % 0.0 0.0 0.0 0.0 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 44 / 52
  • 74.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION: OPT. WITH AN EBOX (RESULTS)query Datalog Datalog UCQ UCQ time(ms) size time(ms) size I II III IV I II III IV I II III IV I II III IV 1 0 0 157 235 15 13 14 9 0 0 516 672 15 13 14 9 2 16 16 157 234 10 10 10 10 16 16 500 656 10 10 10 10 3 0 0 125 235 35 30 28 15 31 15 485 813 72 57 54 15 4 0 16 219 188 41 38 22 16 63 94 735 719 185 170 3 42 5 16 16 172 250 8 5 7 1 32 31 609 719 30 9 15 1 6 0 15 234 188 18 14 14 11 0 15 578 641 18 14 14 11 7 0 0 125 172 27 23 23 20 94 125 1359 1359 180 140 140 110 Query independent information EBox I II III IV Preprocess time 109 2047 24266 2859 Preprocess size 222 195 171 111 size ratio 0.0 0.2 0.8 0.8 cover % 0.0 0.8 0.2 0.8 reverse % 0.0 0.0 0.0 0.0 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 44 / 52
  • 75.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION, AGGREGATED RESULTS 0 0.2 0.4 0.6 0.8 1 102.3 102.4 102.5 size of the EBox Averagetime(ms)toproducetheDatalogrewriting cover 0 0.2 0.4 0.6 0.8 1 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 45 / 52
  • 76.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION, AGGREGATED RESULTS 0 0.2 0.4 0.6 0.8 1 102.3 102.4 102.5 size of the EBox Averagetime(ms)toproducetheDatalogrewriting cover 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 103 104 size of the EBox Averagetime(ms)toproducetheUCQrewriting cover 0 0.2 0.4 0.6 0.8 1 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 45 / 52
  • 77.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION, AGGREGATED RESULTS 0 0.2 0.4 0.6 0.8 1 102.3 102.4 102.5 size of the EBox Averagetime(ms)toproducetheDatalogrewriting cover 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 103 104 size of the EBox Averagetime(ms)toproducetheUCQrewriting cover 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 101 101.2 101.4 size of the EBox AverageclausesintheDatalogrewriting cover 0 0.2 0.4 0.6 0.8 1 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 45 / 52
  • 78.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References EVALUATION, AGGREGATED RESULTS 0 0.2 0.4 0.6 0.8 1 102.3 102.4 102.5 size of the EBox Averagetime(ms)toproducetheDatalogrewriting cover 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 103 104 size of the EBox Averagetime(ms)toproducetheUCQrewriting cover 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 101 101.2 101.4 size of the EBox AverageclausesintheDatalogrewriting cover 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 101.5 102 102.5 size of the EBox AverageclausesintheUCQrewriting cover 0 0.2 0.4 0.6 0.8 1 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 45 / 52
  • 79.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESIS VALIDATION Same queries. Extended previous assets with synthetically-generated EBoxes, according to parameters: SIZE ratio between EBox and TBox sizes (in axioms), e.g. size 2 means that EBox size is twice the TBox size COVER percentage of axioms extracted from the TBox, remaining axioms are generated randomly from the concepts REVERSE among the axioms extracted, percentage of axioms that will have their left hand side and right hand side swapped For the evaluation: Values for parameters have been 0, 0.2, 0.4, 0.6, 0.8 and 1 If a parameter is 0 then following parameters are 0 too Hypothesis: H3. EBoxes may allow reducing the size of the rewritten queries and the time needed in the query process jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 46 / 52
  • 80.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References HYPOTHESIS VALIDATION Same queries. Extended previous assets with synthetically-generated EBoxes, according to parameters: SIZE ratio between EBox and TBox sizes (in axioms), e.g. size 2 means that EBox size is twice the TBox size COVER percentage of axioms extracted from the TBox, remaining axioms are generated randomly from the concepts REVERSE among the axioms extracted, percentage of axioms that will have their left hand side and right hand side swapped For the evaluation: Values for parameters have been 0, 0.2, 0.4, 0.6, 0.8 and 1 If a parameter is 0 then following parameters are 0 too Hypothesis: H3. EBoxes may allow reducing the size of the rewritten queries and the time needed in the query process jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 46 / 52
  • 81.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References 1 INTRODUCTION 2 STATE OF THE ART 3 OBJECTIVES 4 OPTIMISATIONS IN THE PRESENCE OF MAPPINGS 5 ENGINEERING OPTIMISATIONS 6 EBOX-BASED OPTIMISATIONS 7 CONCLUSIONS AND FUTURE LINES jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 47 / 52
  • 82.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References CONCLUSIONS I Small modifications to the query rewriting process from scientific and engineering points of view, with: Big impact on the results Great applicability Strong focus on a pragmatic perspective, maintaining the expressiveness Theoretically sound and validated. Formalisation: 10 propositions, 1 corollary Empirical evaluations that show the obtained results, full data: http://purl.org/net/jmora/mappings/fao/analysis http://purl.org/net/jmora/kyrie/mappings/analysis http://purl.org/net/jmora/kyrie/optimisations/evaluation http://purl.org/net/jmora/kyrie/benchmark http://purl.org/net/jmora/kyrie/queries/analysis http://purl.org/net/jmora/kyrie/extensional/evaluation Open source code: https://bitbucket.org/jmora/kyrie https://bitbucket.org/jmora/kyrie-aux jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 48 / 52
  • 83.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References CONCLUSIONS II Main publications: Jose Mora and Oscar Corcho. Engineering optimisations in query rewriting for OBDA. In Proceedings of the 9th International Conference on Semantic Systems (I-SEMANTICS 13), pages 4148, Graz, Austria, September 2013. Jose Mora and Oscar Corcho. Towards a systematic benchmarking of ontology-based query rewriting systems. In The 12th International Semantic Web Conference (ISWC2013), pages 369384, Sydney, Australia, October 2013. Jose Mora, Riccardo Rosati, and Oscar Corcho. kyrie2: Query Rewriting under Extensional Constraints in ELHIO. In The 13th International Semantic Web Conference (ISWC2014), pages 568583, Riva del Garda, Italy, October 2014. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 49 / 52
  • 84.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References BETTER EVALUATION Some assets used (often reused) for evaluation in papers, e.g. (P´erez-Urbina et al., 2009) Only systematic approach: (Imprialou et al., 2012) Algorithm to generate queries automatically Good for coverage testing (detected several problems) Queries are generated automatically (chase) All have a similar structure No statement about how OBDA systems are actually used Over this state of the art: Usual assets used, added usual assets out of query rewriting Generated some additional assets to evaluate specific features Meta-evaluation: analysed the characteristics of queries and ontologies Having a set of standard benchmarks would ease evaluation, reproducibility, comparison, etc. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 50 / 52
  • 85.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References BETTER EVALUATION Some assets used (often reused) for evaluation in papers, e.g. (P´erez-Urbina et al., 2009) Only systematic approach: (Imprialou et al., 2012) Algorithm to generate queries automatically Good for coverage testing (detected several problems) Queries are generated automatically (chase) All have a similar structure No statement about how OBDA systems are actually used Over this state of the art: Usual assets used, added usual assets out of query rewriting Generated some additional assets to evaluate specific features Meta-evaluation: analysed the characteristics of queries and ontologies Having a set of standard benchmarks would ease evaluation, reproducibility, comparison, etc. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 50 / 52
  • 86.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References BETTER EVALUATION Some assets used (often reused) for evaluation in papers, e.g. (P´erez-Urbina et al., 2009) Only systematic approach: (Imprialou et al., 2012) Algorithm to generate queries automatically Good for coverage testing (detected several problems) Queries are generated automatically (chase) All have a similar structure No statement about how OBDA systems are actually used Over this state of the art: Usual assets used, added usual assets out of query rewriting Generated some additional assets to evaluate specific features Meta-evaluation: analysed the characteristics of queries and ontologies Having a set of standard benchmarks would ease evaluation, reproducibility, comparison, etc. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 50 / 52
  • 87.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References FUTURE LINES Improve expressiveness (SPIN, OWL2 RL, OWL2 DL, ...) efficiency (process but especially results) awareness of context (provenance, cost model, data quality, ...) standardization (SPARQL, ...) applicability: types of data sources (streams, web services, ...) knowledge available, e.g. predefined queries for visualisation benchmarks jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 51 / 52
  • 88.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING OPTIMISATION TECHNIQUES FOR ONTOLOGY-BASED DATA ACCESS Jos´e Mora L´opez jmora@fi.upm.es Supervisor: Oscar Corcho ocorcho@fi.upm.es Ontology Engineering Group Facultad de Inform´atica, Universidad Polit´ecnica de Madrid Madrid - December 17, 2015 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 52 / 52
  • 89.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References FOL ELHIO A(a) A(a), {a} A P(a, b) P(a, b) x≈a ← A(x) A {a} A2(x) ← A1(x) A1 A2 A3(x) ← A1(x) ∧ A2(x) A1 A2 A3 P(x, f(x)) ← A(x) A ∃P P(x, f(x)) ← A1(x),A2(f(x)) ← A1(x) A1 ∃P.A2 P(f(x), x) ← A A ∃P− P(f(x), x) ← A1(x),A2(f(x)) ← A1(x) A1 ∃P− .A2 A(x) ← P(x, y) ∃P A A2(x) ← P(x, y), A1(y) ∃P.A1 A2 A(x) ← P(y, x) ∃P− A A2(x) ← P(y, x), A1(y) ∃P− .A1 A2 S(x, y) ← P(x, y) P S, P− S− S(x, y) ← P(y, x) P S− , P S− jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 53 / 52
  • 90.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References RESEARCH QUESTIONS 1. Can the information about mappings be useful for query rewriting in an OBDA context? 2. Can the information from mappings be used in an efficient way? 3. How can we tune current algorithms for query rewriting in OBDA? 4. How can we measure the appropriateness of the solutions to the query rewriting problem? 5. Can EBoxes be used in query rewriting? 6. What is the impact of the different EBoxes? jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 54 / 52
  • 91.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References ASSUMPTIONS AND LIMITATIONS A1. We can obtain an exhaustive list of the predicates in the ontology that are mapped to some data source for a set of mappings. A2. Predicates that are not mapped cannot be retrieved from the data source. A3. EBoxes can be derived from schemata and data available in data sources. A4. Set semantics: redundant answers have no impact on the correctness. A5. Some predicates in the ontology may not be mapped to the data source. L1. We restrict the input queries to UCQs and output to Datalog. Unfolding of the output to UCQs will be attempted. L2. The mapping-based reduction in time and size for rewriting depends on how many ontology predicates are mapped. L3. The impact of additional computations in query rewriting may be negative in some cases, e.g. those specifically designed for that. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 55 / 52
  • 92.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References ASSUMPTIONS AND LIMITATIONS A1. We can obtain an exhaustive list of the predicates in the ontology that are mapped to some data source for a set of mappings. A2. Predicates that are not mapped cannot be retrieved from the data source. A3. EBoxes can be derived from schemata and data available in data sources. A4. Set semantics: redundant answers have no impact on the correctness. A5. Some predicates in the ontology may not be mapped to the data source. L1. We restrict the input queries to UCQs and output to Datalog. Unfolding of the output to UCQs will be attempted. L2. The mapping-based reduction in time and size for rewriting depends on how many ontology predicates are mapped. L3. The impact of additional computations in query rewriting may be negative in some cases, e.g. those specifically designed for that. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 55 / 52
  • 93.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References FULL LIST OF PUBLICATIONS I 1. Domenico Lembo, Jose Mora, Riccardo Rosati, Domenico Fabio Savo, and Evgenij Thorstensen. Mapping analysis in ontology-based data access: Algorithms and complexity. In ISWC, Bethlehem, Pennsylvania, USA, 2015 2. Ernesto Jimenez-Ruiz, Evgeny Kharlamov, Dmitriy Zheleznyakov, Christoph Pinkel, Martin Skjæveland, Evgenij Thorstensen, Jose Mora, and Ian Horrocks. Bootox: Bootstrapping owl 2 ontologies and r2rml mappings from relational databases. In ISWC, Bethlehem, Pennsylvania, USA, 2015 3. Jose Mora, Riccardo Rosati, and Oscar Corcho. kyrie2: Query rewriting under extensional constraints in ELHIO. In ISWC, Trentino, Italy, 2014 4. Marco Console, Jose Mora, Riccardo Rosati, Valerio Santarelli, and Domenico Fabio Savo. Effective computation of maximal sound approximations of description logic ontologies. In ISWC, Trentino, Italy, 2014 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 56 / 52
  • 94.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References FULL LIST OF PUBLICATIONS II 5. Domenico Lembo, Jose Mora, Riccardo Rosati, Domenico Fabio Savo, and Evgenij Thorstensen. Towards mapping analysis in ontology-based data access. In Web Reason- ing and Rule Systems, Athens, Greece, 2014 6. Jose Mora and Oscar Corcho. Towards a systematic benchmarking of ontology-based query rewriting systems. In ISWC, Sydney, Australia, 2013 7. Jose Mora and Oscar Corcho. Engineering optimisations in query rewriting for OBDA. In I-SEMANTICS ’13, Graz, Austria, 2013 8. Ghislain Atemezing, Oscar Corcho, Daniel Garijo, Jose Mora, Mar´ıa Poveda-Villal´on, Pablo Rozas, Daniel Vila-Suero, and Boris Villaz´on-Terrazas. Transforming meteorological data into linked data. Semantic Web, 2012 9. Jose Mora, Jos´e ´Angel Ramos, and Guadalupe Aguado de Cea. Enhancing the expressiveness of linguistic structures. In E-LKR’12), Castell´on de la Plana, Spain, 2012 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 57 / 52
  • 95.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References FULL LIST OF PUBLICATIONS III 10. B. Villaz´on-Terrazas, D. Vila-Suero, D. Garijo, L. M. Vilches-Bl´azquez, M. Poveda- Villal´on, J. Mora, O. Corcho, and A. G´omez-P´erez. Publishing linked data-there is no one-size-fits-all formula. European Data Forum, 2012 11. Alexander de Le´on, Victor Saquicela, Luis M. Vilches, Boris Villaz´on-Terrazas, Freddy Priyatna, Oscar Corcho, Carlos Buil, Jose Mora, and Jean-Paul Calbimonte. Geographical linked data: a spanish use case. In I-SEMANTICS ’10, Graz, Austria, 2010 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 58 / 52
  • 96.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References REFERENCES I Diego Calvanese, Giuseppe De Giacomo, Domenico Lembo, Maurizio Lenzerini, and Riccardo Rosati. Tractable Reasoning and Efficient Query Answering in Description Logics: The DL-Lite Family. Journal of Automated Reasoning, 39(3):385–429, October 2007. doi: 10.1007/s10817-007-9078-x. Alexandros Chortaras, Despoina Trivela, and Giorgos Stamou. Optimized Query Rewriting for OWL 2 QL. In Automated Deduction – CADE-23, volume 6803, pages 192–206. Springer, 2011. ISBN 978-3-642-22437-9. Thomas Eiter, Magdalena Ortiz, Mantas Simkus, Trung-Kien Tran, and Guohui Xiao. Query rewriting for Horn-SHIQ plus rules. In 26th AAAI Conference on Artificial Intelligence, 2012. Georg Gottlob, Giorgio Orsi, and Andreas Pieris. Ontological Queries: Rewriting and Optimization (Extended Version). arXiv:1112.0343, December 2011. Martha Imprialou, Giorgos Stoilos, and Bernardo Cuenca Grau. Benchmarking ontology-based query rewriting systems. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI, 2012. Jose Mora and Oscar Corcho. Towards a systematic benchmarking of ontology-based query rewriting systems. In The 12th International Semantic Web Conference (ISWC2013), pages 369–384, Sydney, Australia, 2013. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 59 / 52
  • 97.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References REFERENCES II Jose Mora, Riccardo Rosati, and Oscar Corcho. kyrie2: Query Rewriting under Extensional Constraints in ELHIO. In The 13th International Semantic Web Conference (ISWC2014), Trentino, Italy, 2014. H´ector P´erez-Urbina, Ian Horrocks, and Boris Motik. Efficient Query Answering for OWL 2. In Lecture Notes in Computer Science, volume 5823, pages 489–504. Springer, 2009. Mariano Rodr´ıguez-Muro and Diego Calvanese. Dependencies: Making ontology based data access work in practice. Proc. of the 5th Alberto Mendelzon Int. Workshop on Foundations of Data Management, 2011. Riccardo Rosati. Prexto: Query Rewriting under Extensional Constraints in DL-Lite. In The Semantic Web: Research and Applications, volume 7295 of Lecture Notes in Computer Science, pages 360–374. Springer Berlin / Heidelberg, 2012. ISBN 978-3-642-30283-1. Riccardo Rosati and Alessandro Almatelli. Improving Query Answering over DL-Lite Ontologies. In Proceedings of the Twelfth International Conference on the Principles of Knowledge Representation and Reasoning. AAAI Press, 2010. Tassos Venetis, Giorgos Stoilos, and Giorgos Stamou. Query rewriting under query extensions for OWL 2 QL ontologies. In The 7th International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS 2011), page 59, 2011. Luis Manuel Vilches-Bl´azquez, Miguel ´Angel Bernab´e-Poveda, Mari Carmen Su´arez-Figueroa, Asunci´on G´omez-P´erez, and Antonio F. Rodr´ıguez-Pascual. Towntology hydrOntology: Relationship between Urban and Hydrographic Features in the Geographic Information Domain. Ontologies for Urban Development, 61:73–84, 2007. jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 60 / 52
  • 98.
    Introduction State ofthe Art Objectives Mappings Engineering EBox Conclusions References QUERY REWRITING OPTIMISATION TECHNIQUES FOR ONTOLOGY-BASED DATA ACCESS Jos´e Mora L´opez jmora@fi.upm.es Supervisor: Oscar Corcho ocorcho@fi.upm.es Ontology Engineering Group Facultad de Inform´atica, Universidad Polit´ecnica de Madrid Madrid - December 17, 2015 jmora@fi.upm.es Optimising OBDA query rewriting Madrid - December 17, 2015 61 / 52