Integrating Relational Databases with the Semantic Web: A Reflection

1. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Integrating Relational Databases with the Semantic Web: A Reflection Juan F. Sequeda Joint work with Daniel P. Miranker (UT Austin) and Marcelo Arenas (PUC Chile) Thanks to: Oscar Corcho, Aibo Tian, Mayank Kejriwal, Hamid Tirmizi 13th Reasoning Web Summer School (RW 2017) – July 7 to 11, 2017 – London, UK

3. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Take away message of this talk • Reflect on 10 years of (our) research on Integrating Relational Database with the Semantic Web – DISCLAIMER: This is NOT a Survey – W3C Relational Database to RDF Standards (Science vs Engineering) • Provide answer to the research question: • Thesis: How and to what extent can Relational Databases be integrated with the Semantic Web? Much of the existing Relational Database infrastructure can be reused to support the Semantic Web

4. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Data Logic RDBMS Semantic Web Workshop on Logic and Data Bases, Toulouse 1977 Gallaire, Nicolas & Minker SQL99 Recursion KL-‐ONE Description Logic RDF OWL Views Triggers Semantic Networks Japanese 5th Generation Project MCC Austin, TX Today1970s Relational Algebra Workshops on Expert Systems Deductive Databases KRDB 1980s 1990s 2000s Let’s put History in Today’s Context 4

5. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com What is the relationship between Relational Model Table Definition ConstraintsS Q L Relational Databases RDF RDFS OWL S P A R Q L TIME Triggers Rules Semantic Web Sequeda et al. SQL Databases are a Moving Target. W3C Workshop on RDF Access on RDB. 2007 Progra mmer type 2 “Bob” name ITEmployee subClassOf SELECT ?s ?n { ?s type ITEmployee. ?s name ?n } Literal name

6. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Once upon a time … • D2R (Map,Q,Server), Virtuoso RDF Views, SquirrelRDF, R2D2, Relational.OWL, DB2OWL, R2O, Triplify, Dartgrid, RDBToOnto, METAmorphoses,… https://www.w3.org/2007/03/RdfRDB/

7. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com F2F Meeting ISWC 2008 March 2008 February 2009 1. Recommendation to standardize a mapping language 2. RDB2RDF Survey (2) http://www.w3.org/2005/Incubator/rdb2rdf/RDB2RDF_SurveyReport.pdf (1) http://www.w3.org/2005/Incubator/rdb2rdf/XGR-‐rdb2rdf-‐20090126/ October 2008

9. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com 0 50 100 150 200 250 Sep-‐09 Oct-‐09 Nov-‐09 Dec-‐09 Jan-‐10 Feb-‐10 Mar-‐10 Apr-‐10 May-‐10 Jun-‐10 Jul-‐10 Aug-‐10 Sep-‐10 Oct-‐10 Nov-‐10 Dec-‐10 Jan-‐11 Feb-‐11 Mar-‐11 Apr-‐11 May-‐11 Jun-‐11 Jul-‐11 Aug-‐11 Sep-‐11 Oct-‐11 Nov-‐11 Dec-‐11 Jan-‐12 Feb-‐12 Mar-‐12 Apr-‐12 May-‐12 Jun-‐12 Jul-‐12 Aug-‐12 Sep-‐12 Oct-‐12 First F2F @Semtech 2010 FPWD R2RML WD R2RML + DM Rec R2RML + DM Candidate Rec R2RML + DM Proposed Rec R2RML + DM FPWD DM WD R2RML+DM WD R2RML+DM Photo from cygri http://www.flickr.com/photos/cygri/4719458268/

11. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Outline • 9:00 – 10:30 – Intro – Relational Database à Semantic Web: Data Mapping • 10:30 – 11:00 – Coffee Break • 11:00 – 12:30 – Semantic Web à Relational Database: Data Access – Conclusion 11

13. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com RDF W3C Direct Mapping Overview Relational Database Direct Mapping Engine Input: Database (Schema and Data) Primary Keys Foreign Keys Output RDF graph

14. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com orderid date total currency status 1234 2017-‐07-‐07 100 USD 1 Order LineItem LineItem/lineid=6789 Order#orderid 6789 Input • Relational Schema • Primary Keys PK and Foreign Keys FK over R • Relational Data Output • RDF graphDirect Mapping W3C Direct Mapping 14 lineid price quantity product orderid 6789 30 2 Shoes Foo 1234 6790 20 2 Tshirt Bar 1234 <LineItem/lineid=6790> <Order/orderid=1234> LineItem#ref-‐orderid LineItem#ref-‐orderid 1234 2017-‐07-‐07 100 USD 1 30 2 Shoes Foo 1234 6790 20 2Tshirt Bar 1234 Order#date Order#total Order#currency Order#status Lineitem#lineid Lineitem#price Lineitem#quantity Lineitem#product Lineitem#orderid Lineitem#lineid Lineitem#price Lineitem#quantity Lineitem#product

15. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com What do we need to automatically generate? • Generate Identifiers – IRI – Blank Nodes • Generate Triples – Table – Literal – Reference

16. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Generating Identifiers • Identifier for rows, tables, columns and foreign keys • If a table has a primary key, – then the row identifier will be an IRI, – otherwise a blank node • The identifiers for table, columns and foreign keys are IRIs • IRIs are generated by appending to a given base IRI • All strings are percent encoded

17. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Row Node 1) <http://www.ex.com/Person/ID=1> Base IRI “Table Name”/“PK attr”=“PK value” 2) <http://www.ex.com/Person/ID=1;SID=123> Base IRI “Table Name”/“PK attr”=“PK value” 3) Fresh Blank Node

18. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com More IRI 1) <http://www.ex.com/Person> Base IRI “Table Name” 2) <http://www.ex.com/Person#NAME> Base IRI “Table Name”#“Attribute” 3) <http://www.ex.com/Person#ref-‐CID> Base IRI “Table Name”#ref-‐“Attribute”

20. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com <http://www.ex.com/Person/ID=1> <http://www.ex.com/Person#NAME> “Alice” . Literal Triples 20 ID (pk) NAME AGE 1 Alice 25 2 Bob NULL Person

21. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com ID (pk) NAME AGE CID (fk) 1 Alice 25 100 2 Bob NULL 200 Person CID (pk) TITLE 100 Austin 200 Madrid City Reference Triples 21 <http://www.ex.com/Person/ID=1> <http://www.ex.com/Person#ref-‐CID> <http://www.ex.com/City/CID=100>.

22. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Direct Mapping Result 22 ID NAME AGE CID 1 Alice 25 100 2 Bob NULL 100 Person CID NAME 100 Austin 200 Madrid City <Person/ID=1> <City/CID=100> Alice 25 Austin <Person/ID=2> Alice <City/CID=200> Madrid <Person#NAME> <Person#AGE> <Person#NAME> <Person#NAME> <Person#NAME> <Person#ref-‐CID> <Person#ref-‐CID>

23. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Summary of W3C Direct Mapping • Default and Automatic Mapping • URIs are automatically generated – <table> – <table#attribute> – <table#ref-‐attribute> – <Table/pkAttr=pkValue> • RDF represents the same relational schema • RDF can be transformed by SPARQL CONSTRUCT – RDF represents the structure and ontology of mapping author’s choice 23

24. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Issues with the W3C Direct Mapping 1. Mapping is only from Relational data to RDF data. – The relational schema is not taken in account. Hence no relational schema to OWL 2. Semantics is not defined for NULL values – “The direct mapping does not generate triples for NULL values. Note that it is not known how to relate the behavior of the obtained RDF graph with the standard SQL semantics of the NULL values of the source RDB.” 24

25. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Research Problem with Direct Mapping • How can a relational database schema and data, including nulls, be automatically mapped to RDF and OWL? • How can we assure correctness of mapping? – Information Preservation: no information is lost – Query Preservation: no queries are lost – Monotonicity: inserts does not affect – Semantics Preservation: constraints are not lost 25 Hypothesis: Relational Databases can be automatically mapped to RDF and OWL under a correct mapping

26. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com orderid date total currency status 1234 2017-‐07-‐07 100 USD 1 Order LineItem LineItem/lineid=6789 Order#orderid 6789 Input • Relational Schema R • Set Σ of Primary Keys PK and Foreign Keys FK over R • Instance I of R Output • RDF graph • OWL Ontology as RDF graph Direct Mapping Direct Mapping as Ontology Overview 26 lineid price quantity product orderid 6789 30 2 Shoes Foo 1234 6790 20 2 Tshirt Bar 1234 <LineItem/lineid=6790> <Order/orderid=1234> LineItem#ref-‐orderid 1234 2017-‐07-‐07 100 USD 1 30 2 Shoes Foo 1234 6790 20 2Tshirt Bar 1234 Order#date Order#total Order#currency Order#status Lineitem#lineid Lineitem#price Lineitem#quantity Lineitem#product Lineitem#orderid Lineitem#lineid Lineitem#price Lineitem#quantity Lineitem#product <Order> <LineItem> LineItem#ref-‐orderid owl:Class rdf:type rdf:type We need to be careful about two issues • Binary Relations • NULLs

27. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com NULLs • What should we do with NULLs? – Generate a Blank Node – Don’t generate a triple 27 How do we reconstruct the NULL? lineid product comment 6789 Shoes Foo “…” 6790 Tshirt Bar NULL LineItem/lineid=6789 “…” _:a LineItem/lineid=6789 “…” pr:title

28. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Direct Mapping Input: A relational schema R a set of Σ of primary keys and foreign keys and a database instance I of this schema Output: An RDF Graph 28 Definition: A direct mapping M is a total function from the set of all (R, Σ, I) to the set of all RDF graphs

29. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com I R, Σ Predicates to store (R, Σ, I) Predicates to Store Ontology O Datalog Rules to generate O from R, Σ Datalog Rules to generate RDF from O and I Datalog Rules to generate OWL from O OWL RDF Direct Mapping RDB to RDF and OWL 29 Rel(r) Attr(a, r) PKn(a1, … , an, r) Value(v, a, t, r) … Class(X) ← Rel(X), ¬IsBinRel(X) Triple(U,"rdf:type","owl:Class") ← Class(R), ClassIRI(R, U) Triple(s, p, o) ← … On Directly Mapping Relational Databases to RDF and OWL Sequeda, Arenas, Miranker. WWW 2012 Class(C) DtP(p, C) ObjP(p, S, T) …

30. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Input: Relational Schema • Rel(r) : – Rel(Order) • Attr(a, r) : – Attr(total, Order) • PKn(a1, … , an, r) : – PK1(orderid, Order) • FKn(a1, … , an, r, b1, … , bn, s) : – FK1(orderid, LineItem, orderid, Order) 30 orderid date total currency status 1234 2017-‐07-‐07 100 USD 1 Order LineItem lineid price quantity product orderid 6789 30 2 Shoes Foo 1234 6790 20 2 Tshirt Bar 1234

32. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Input: Relational Schema • Value(v, a, t, r) – Value( 1234, orderid, t1, Order) 32 orderid date total currency status 1234 2017-‐07-‐07 100 USD 1 Order

33. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Input: Relational Schema • Value(v, a, t, r) – Value( 1234, orderid, t1, Order) – Value( 2017-‐07-‐07, date, t1, Order) 33 orderid date total currency status 1234 2017-‐07-‐07 100 USD 1 Order

34. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Input: Relational Schema • Value(v, a, t, r) – Value( 1234, orderid, t1, Order) – Value( 2017-‐07-‐07, date, t1, Order) – Value( 100, total, t1, Order) 34 orderid date total currency status 1234 2017-‐07-‐07 100 USD 1 Order

35. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Mapping to OWL 35 Triple(http://ex.org/Order, rdf:type, owl:Class) Triple(U,"rdf:type","owl:Class") ← Class(R), ClassIRI(R, U) ClassIRI(R, X) ← Class(R), Concat2(base, R, X) Class(X) ← Rel(X), ¬IsBinRel(X) IsBinRel(X) ← BinRel(X, A, B, S, C, T, D) BinRel(R, A, B, S, C, T, D) ← PK2(A, B, R), ¬ThreeAttr(R), FK1(A,R,C,S),R ≠ S, FK1(B,R,D,T),R ≠ T, ¬TwoFK(A, R), ¬TwoFK (B, R), ¬OneFK(A, B, R), ¬FKTo(R)

36. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Generating IRIs for Tuples 36 Generate IRIs for the tuples of the relations having a primary key: Generate blank nodes for the tuples of the relations not having a primary key Generate an identifier X of a tuple T of a relation R, which is an IRI if R has a primary key or a blank node.

37. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Mapping to RDF: Table Triples 37 Table triples: for each relation, store the tuples that belongs to it Triple(http://ex.org/Order#orderid=1234 , rdf:type, http://ex.org/Order )

38. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Mapping to RDF: Literal Triples 38 Literal triples: for each tuple, store the values in each of its attributes Triple(http://ex.org/person#ssn=123 , http://ex.org/person#name , “Juan”) Generate for every tuple t in a relation R and for every attribute A of R, a triple storing the value of t in A, which is called a literal triple.

39. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Mapping to RDF: Reference Triples 39 Reference triples: store the references generated by the FKs Triple(http://ex.org/student#id=3 , http://ex.org/student,person#ssn,ssn , http://ex.org/person#ssn=123 ) Construct reference triples for object properties that are generated from binary relations Construct reference triples for object properties that are generated from foreign keys

40. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Information Preservation 40 I R, Σ DM(R, Σ, I) DM-‐ (DM(R, Σ, I)) Proof: Provide a computable mapping DM-‐ Theorem: The Direct Mapping DM is information preserving

42. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Relational Algebra tuples vs. SPARQL mappings 42 ssn name age 789 Daniel NULL person t.ssn = 789 t.name = Daniel t.age = NULL Then, tr(t) = μ : • Domain of μ is {?ssn, ?name} • μ(?ssn) = 789 • μ(?name) = Daniel

43. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Query Preservation 43 I R, Σ DM(R, Σ, I) eval(Q*, DM(R, Σ, I))tr(eval(Q, I)) = Proof: By induction on the structure of Q Bottom-‐up algorithm for translating Q into Q* Theorem: The Direct Mapping is query preserving

44. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Monotonicity 44 DM(R, Σ, I1) DM(R, Σ, I2) I1 ⊆ I2 DM(R, Σ, I1) ⊆ DM(R, Σ, I2) I1 R, Σ I2 R, Σ Proof: All negative atoms in the Datalog rules refer to the schema, where the schema is fixed Theorem: The Direct Mapping DM is Monotone

45. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Semantics Preservation 45 DM(R, Σ, I) DM(R, Σ, I) I R, Σ I R, Σ I satisfies Σ I does not satisfies Σ Consistent under OWL semantics Not consistent under OWL semantics ssn name 123 Juan 123 Marcelo person ssn is the PK #ssn=123 Juan Marcelo DM(R, Σ, I) 12 3 person#ssn I does not satisfy Σ however DM(R, Σ, I) is consistent under OWL semantics Proposition: The direct mapping DM is not semantics preserving. Theorem: No monotone direct mapping is semantics preserving

46. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Extending DM for Semantics Preservation • Family of Datalog rules to determine violation – Primary Keys – Foreign Keys – Create artificial triple that will generate contradiction • Non-‐monotone direct mapping • Information Preserving • Query Preserving • Semantics Preserving 46

47. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Reflection 1 • We studied how Relational Databases can be automatically and correctly mapped to the Semantic Web – HOW: Defined a Direct Mapping using Datalog rules – EXTENT: Information and Query Preserving. Monotonicity is an obstacle for Semantics Preservation Recall the Hypothesis: Relational Databases can be automatically mapped to RDF and OWL under a correct mapping Information Preserving, Query Preserving and Monotone or Information, Query and Semantics Preserving

48. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com RDF W3C R2RML Relational Database R2RML Mapping Engine OWL Ontologies (e.g FOAF, etc) R2RML File Input Database (schema and data) Target Ontologies Mappings between the Database and Target Ontologies in R2RML Output RDF graph Direct Mapping helps to “bootstrap”

49. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Direct Mapping as R2RML 49 ID NAME AGE CID 1 Alice 25 100 2 Bob NULL 100 Person CID NAME 100 Austin 200 Madrid City <Person/ID=1> <City/CID=100> Alice 25 Austin <Person/ID=2> Alice <City/CID=200> Madrid <Person#NAME> <Person#AGE> <Person#NAME> <Person#NAME> <Person#NAME> <Person#ref-‐CID> <Person#ref-‐CID> How can this be represented as R2RML?

50. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/ID={ID}"; rr:class <http://www.ex.com/Person> ]; rr:predicateObjectMap [ rr:predicate <http://www.ex.com/Person#NAME> ; rr:objectMap [rr:column ”NAME" ] ]. Direct Mapping as R2RML

51. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/ID={ID}"; rr:class <http://www.ex.com/Person> ]; rr:predicateObjectMap [ rr:predicate <http://www.ex.com/Person#NAME> ; rr:objectMap [rr:column ”NAME" ] ]. Direct Mapping as R2RML 51 Logical Table: What is being mapped? SubjectMap: How to generate the Subject? PredicateObjectMap: How to generate the Predicate and Object?

52. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/ID={ID}"; rr:class <http://www.ex.com/Person> ]; rr:predicateObjectMap [ rr:predicate <http://www.ex.com/Person#NAME> ; rr:objectMap [rr:column ”NAME" ] ] . Logical Table 52 What is being mapped?

53. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/ID={ID}"; rr:class <http://www.ex.com/Person> ]; rr:predicateObjectMap [ rr:predicate <http://www.ex.com/Person#NAME> ; rr:objectMap [rr:column ”NAME" ] ] . Subject URI Template 53 Subject URI <Subject URI> rdf:type <Class URI>

54. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/ID={ID}"; rr:class <http://www.ex.com/Person> ]; rr:predicateObjectMap [ rr:predicate <http://www.ex.com/Person#NAME> ; rr:objectMap [rr:column ”NAME" ] ] . Predicate URI Constant 54 Predicate URI

55. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/ID={ID}"; rr:class <http://www.ex.com/Person> ]; rr:predicateObjectMap [ rr:predicate <http://www.ex.com/Person#NAME> ; rr:objectMap [rr:column ”NAME" ] ] . Object Column Value 55 Object Literal

56. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com <http://www.ex.com/Person/ID=1> <http://www.ex.com/Person#NAME> <http://www.ex.com/Person/1> foaf:name “Ugly” vs “Cool” URIs 56 foaf:Person <http://www.ex.com/Person>

57. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicate foaf:name; rr:objectMap [rr:column ”NAME" ] ] . Customization 57 Customized Subject URI Customized Class Customized Property

58. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com What if … 58 ID NAME GENDER 1 Alice F 2 Bob M Person <Person/1> Alice foaf:name <Woman> rdf:type SELECT ID, NAME FROM Person WHERE GENDER = "F" R2RML View

59. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:sqlQuery “””SELECT ID, NAME FROM Person WHERE gender = “F” “””]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class <http://www.ex.com/Woman> ]; rr:predicateObjectMap [ rr:predicate foaf:name; rr:objectMap [rr:column ”NAME" ] ] . R2RML View 59 Query instead of table

60. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Quick Overview of R2RML • Manual and Customizable Language • Learning Curve • Direct Mapping bootstraps R2RML • RDF represents the structure and ontology of mapping author’s choice 60

61. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com W3C R2RML Details • Logical Tables: What is being mapped • Term Maps: How to create RDF terms • How to create Triples from a table • How to create Triples between two tables • Languages • Datatypes

63. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com sid name pid 1 Juan 100 2 Martin 200 pid name 100 Dan 200 Marcelo Student Professor ex:Student1 rdf:type ex:Student . ex:Student2 rdf:type ex:Student . ex:Professor100 rdf:type ex:Professor . ex:Professor200 rdf:type ex:Professor . ex:Student1 foaf:name “Juan”. … R2RML Mapping R2RML Mapping

64. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com R2RML Mapping • A R2RML Mapping M consists of a finite set TM TripleMaps. • Each TM ∈TM consists of a tuple (LT, SM, POM) – LT: LogicalTable – SM: SubjectMap – POM: PredicateObjectMap • Each POM∈POM consists of a pair (PM, OM)* – PM: PredicateMap – OM: ObjectMap * For simplicity

65. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com R2RML Mapping • An R2RML Mapping is represented as an RDF Graph itself. • Associated RDFS schema – http://www.w3.org/ns/r2rml • Turtle is the recommended syntax

66. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com LogicalTable • Tabular data mapped to RDF – rr:logicalTable 1. Existing Relational table or view – rr:tableName 2. R2RML (SQL) View – rr:sqlQuery

67. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicate foaf:name; rr:objectMap [rr:column ”NAME" ] ] . 67

68. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:sqlQuery “””SELECT ID, NAME FROM Person WHERE gender = “F” “””]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class <http://www.ex.com/Woman> ]; rr:predicateObjectMap [ rr:predicate foaf:name; rr:objectMap [rr:column ”NAME" ] ] .

70. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com How to create RDF terms that define RDF Triples? • RDF term is either an IRI, a blank node, or a literal • Answer 1. Constant Value 2. Value in the database a. Raw Value in a Column b. Column Value applied to a template

71. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com TermMap • A TermMap is a function that generates an RDF Term from a logical table row. • RDF Term is either a IRI, or a Blank Node, or a Literal Logical Table Row TermMap IRI Bnode Literal RDF Term

72. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com TermMap • A TermMap must be exactly on of the following – Constant-‐valued TermMap – Column-‐valued TermMap – Template-‐valued TermMap • If TermMaps are used to create S, P, O, then – 3 ways to create a subject – 3 ways to create a predicate – 3 ways to create an object

73. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Stemplate Ptemplate Otemplate Oconstant Ocolumn PConstant Otemplate Oconstant Ocolumn Pcolumn Otemplate Oconstant Ocolumn Sconstant Ptemplate Otemplate Oconstant Ocolumn PConstant Otemplate Oconstant Ocolumn Pcolumn Otemplate Oconstant Ocolumn Scolumn Ptemplate Otemplate Oconstant Ocolumn PConstant Otemplate Oconstant Ocolumn Pcolumn Otemplate Oconstant Ocolumn How many ways to create a Triple?

74. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Constant-‐valued TermMap • A TermMap that ignores the logical table row and always generates the same RDF term • rr:constant • Commonly used to generate constant IRIs as the predicate

75. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicateMap [rr:constant foaf:name ] rr:objectMap [rr:column ”NAME" ] ] . 75

76. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Column-‐valued TermMap • A TermMap that maps a column value of a column name in a logical table row • rr:column • Commonly used to generate Literals as the object

78. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Template-‐valued TermMap • A TermMap that maps the column values of a set of column names to a string template. • A string template is a format that can be used to build strings from multiple components. • rr:template • Commonly used to generate IRIs as the subject or concatenate different attributes

80. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Commonly used… • … but any of these TermMaps can be used to create any RDF Term (s,p,o). Recall: – 3 ways to create a subject – 3 ways to create a predicate – 3 ways to create an object • Template-‐valued TermMap are commonly used to create an IRI for a subject, but can be used to create Literal for an object. • How to specify the term (IRI or Literal in this case)?

81. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com TermType • Specify the type of a term that a TermMap should generate • Force what the RDF term should be • Three types of TermType: – rr:IRI – rr:BlankNode – rr:Literal

82. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicateMap [rr:constant foaf:name ] rr:objectMap [ rr:template ”{FIRST_NAME} {LAST_NAME}”; rr:termType rr:Literal; ] ] . 82

83. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template ”person{ID}"; rr:termType rr:BlankNode; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicateMap [rr:constant foaf:name ] rr:objectMap [rr:column ”NAME" ] ] . 83

84. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com TermType (cont…) • Can only be applied to Template and Column valued TermMap • Applying to Constant-‐valued TermMap has no effect – i.e If the constant is an IRI, the term type is automatically an IRI

85. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com TermType Rules • If the Term Map is for a 1. Subject à TermType = IRI or Blank Node 2. Predicate à TermType = IRI 3. Object à TermType = IRI or Blank Node or Literal

86. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com TermType is Optional • If a TermType is not specified then – Default = IRI – Unless it’s for an object being defined by a Column-‐based TermMap or has a language tag or specified datatype, then the TermType is a Literal • That’s why if there is a template in an ObjectMap, it will always generate an IRI, unless a TermType to Literal is specified.

87. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com rr:predicateObjectMap [ rr:predicateMap [rr:constant foaf:name ] rr:objectMap [ rr:template ”{FIRST_NAME} {LAST_NAME}”; rr:termType rr:Literal; ] ] 87 rr:predicateObjectMap [ rr:predicateMap [rr:constant ex:role ] rr:objectMap [ rr:template ”http://ex.com/role/{role}” ] ] rr:predicateObjectMap [ rr:predicateMap [rr:constant foaf:name ] rr:objectMap [ rr:template ”{FIRST_NAME} {LAST_NAME}” ] ]

91. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com SubjectMap • SubjectMap is a TermMap • rr:subjectMap • Specifies what the subject of a triple should be • 3 ways to create a subject – Template-‐valued Term Map – Column-‐valued Term Map – Constant-‐valued Term Map • Has to be an IRI or Blank Node

92. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com SubjectMap • SubjectMaps are usually Template-‐valued TermMap • Use-‐case for Column-‐valued TermMap – Use a column value to create a blank node – URI exist as a column value • Use-‐case for Constant-‐valued TermMap – For all tuples: <CompanyABC> <consistsOf> <Dep{id}>

94. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicate foaf:name; rr:objectMap [rr:column ”NAME" ] ] . 94 Optional

95. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com PredicateObjectMap • A function that creates one or more predicate-‐ object pairs for each logical table row. • rr:predicateObjectMap • It is used in conjunction with a SubjectMap to generate RDF triples in a TriplesMap. • A predicate-‐object pair consists of – One or more PredicateMaps – One or more ObjectMaps or ReferencingObjectMaps

96. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicateMap [rr:constant foaf:name]; rr:objectMap [rr:column ”NAME" ] ] . 96

97. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com PredicateMap • PredicateMap is a TermMap • rr:predicateMap • Specifies what the predicate of a RDF triple should be • 3 ways to create a predicate – Template-‐valued Term Map – Column-‐valued Term Map – Constant-‐valued Term Map • Has to be an IRI

98. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com PredicateMap • PredicateMaps are usually Constant-‐valued TermMap • Use-‐case for Column-‐valued TermMap – … • Use-‐case for Template-‐valued TermMap – …

99. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicateMap [rr:constant foaf:name]; rr:objectMap [rr:column ”NAME" ] ] . 99

100. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicate foaf:name; rr:objectMap [rr:column ”NAME" ] ] . 10 0 Shortcut!

101. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Constant Shortcut Properties • ?x rr:predicate ?y • ?x rr:predicateMap [ rr:constant ?y ] • ?x rr:subject ?y • ?x rr:subjectMap [ rr:constant ?y ] • ?x rr:object ?y • ?x rr:objectMap [ rr:constant ?y ]

102. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com ObjectMap • ObjectMap is a TermMap • rr:objectMap • Specifies what the object of a triple should be • 3 ways to create a predicate – Template-‐valued Term Map – Column-‐valued Term Map – Constant-‐valued Term Map • Has to be an IRI or Literal or Blank Node

103. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com ObjectMap • ObjectMaps are usually Column-‐valued TermMap • Use-‐case for Template-‐valued TermMap – Concatenate values – Create IRIs • Use-‐case for Constant-‐valued TermMap – All rows in a table share a role

104. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”Person”]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicateMap [rr:constant foaf:name]; rr:objectMap [rr:column ”NAME" ] ] . 10 4

106. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com sid name pid 1 Juan 100 2 Martin 200 Student @prefix ex: <http://example.com/ns/>. ex:Student1 rdf:type ex:Student . ex:Student2 rdf:type ex:Student . TripleMap Example 1 • We now have sufficient elements to create a mapping that will generate – A Subject IRI – rdf:Type triple(s)

107. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Example 1 @prefix rr: <http://www.w3.org/ns/r2rml#>. @prefix ex: <http://example.com/ns/>. <#TriplesMap1> rr:logicalTable [ rr:tableName ”Student”]; rr:subjectMap [ rr:template "http://example.com/ns/{sid}"; rr:class ex:Student; ]. Logical Table is a Table Name SubjectMap is a Template-‐valued TermMap And it has one Class IRI sid name pid 1 Juan 100 2 Martin 200 Student @prefix ex: <http://example.com/ns/>. ex:Student1 rdf:type ex:Student . ex:Student2 rdf:type ex:Student . TripleMap ρ1 : Student(s, x, y) ∧ p = rdf:type∧ o = ex:Student → Triple(s, p, o) Predicate ObjectQuery over R and Subject

108. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Class RDB2RDF Rule • Given a relational schema R such, a class RDB2RDF-‐rule ρ over R is a first-‐order formula of the form: ∀s∀p∀o∀x̄ α(s,x ̄) ∧ p = type ∧ o = c → triple(s,p,o) where α(s,x ̄) is a query over R and c ∈ D and D is a a countably infinite domain of constants 10 8 ρ1 : Student(s, x, y) ∧ p = rdf:type∧ o = ex:Student → Triple(s, p, o)

109. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com sid name pid 1 Juan 100 2 Martin 200 Student @prefix ex: <http://example.com/ns/>. ex:Student1 rdf:type ex:Student . ex:Student1 ex:name “Juan” . ex:Student2 rdf:type ex:Student . ex:Student2 ex:name “Martin” . TripleMap Example 2

110. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Example 2 @prefix rr: <http://www.w3.org/ns/r2rml#>. @prefix ex: <http://example.com/ns/>. <#TriplesMap1> rr:logicalTable [ rr:tableName ”Student”]; rr:subjectMap [ rr:template "http://example.com/ns/{sid}"; rr:class ex:Student; ]; rr:predicateObjectMap [ rr:predicate ex:name; rr:objectMap [ rr:column “name”]; ]. Logical Table is a Table Name SubjectMap is a Template-‐valued TermMap And it has one Class IRI PredicateObjectMap PredicateMap which is a Constant-‐valued TermMap ObjectMap which is a Column-‐valued TermMap ρ2 : Student(s, o, y) ∧ p = ex:name → Triple(s, p, o)

111. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Predicate RDB2RDF Rule • Given a relational schema R such, a class RDB2RDF-‐rule ρ over R is a first-‐order formula of the form: ∀s∀p∀o∀x̄ β(s, o, x ̄) ∧ p = c → triple(s, p, o) where β(s, o, x ̄) is a query over R and c ∈ D and D is a a countably infinite domain of constants 11 1 ρ2 : Student(s, o, y) ∧ p = ex:name → Triple(s, p, o)

112. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com RDB2RDF Mapping • An RDB2RDF mapping M over R is a finite set of class or predicate RDB2RDF rules over R 11 2 M = {ρ1, ρ2} ρ1 : Student(s, x, y) ∧ p = rdf:type∧ o = ex:Student → Triple(s, p, o) ρ2 : Student(s, o, y) ∧ p = ex:name → Triple(s, p, o) @prefix rr: <http://www.w3.org/ns/r2rml#>. @prefix ex: <http://example.com/ns/>. <#TriplesMap1> rr:logicalTable [ rr:tableName ”Student”]; rr:subjectMap [ rr:template "http://example.com/ns/{sid}"; rr:class ex:Student; ]; rr:predicateObjectMap [ rr:predicate ex:name; rr:objectMap [ rr:column “name”]; ].

113. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com sid name pid 1 Juan 100 2 Martin 200 Student @prefix ex: <http://example.com/ns/>. ex:Student1 rdf:type ex:Student . ex:Student1 ex:comment “Juan is a Student” . ex:Student2 rdf:type ex:Student . ex:Student2 ex:comment “Martin is a Student” . TripleMap Example 3

114. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Example 3 @prefix rr: <http://www.w3.org/ns/r2rml#>. @prefix ex: <http://example.com/ns/>. <#TriplesMap1> rr:logicalTable [ rr:tableName ”Student”]; rr:subjectMap [ rr:template "http://example.com/ns/{sid}"; rr:class ex:Student; ]; rr:predicateObjectMap [ rr:predicate ex:comment; rr:objectMap [ rr:template “{name} is a Student”; rr:termType rr:Literal; ]; ]. Logical Table is a Table Name SubjectMap is a Template-‐valued TermMap And it has one Class IRI PredicateObjectMap PredicateMap which is a Constant-‐valued TermMap ObjectMap which is a Template-‐valued TermMap TermType

115. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com sid name pid 1 Juan 100 2 Martin 200 Student @prefix ex: <http://example.com/ns/>. ex:Student1 rdf:type ex:Student . ex:Student1 ex:webpage <http://ex.com/Juan>. ex:Student2 rdf:type ex:Student . ex:Student2 ex:webpage <http://ex.com/Martin>. TripleMap Example 4

116. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Example 4 @prefix rr: <http://www.w3.org/ns/r2rml#>. @prefix ex: <http://example.com/ns/>. <#TriplesMap1> rr:logicalTable [ rr:tableName ”Student”]; rr:subjectMap [ rr:template "http://example.com/ns/{sid}"; rr:class ex:Student; ]; rr:predicateObjectMap [ rr:predicate ex:webpage; rr:objectMap [ rr:template “http://ex.com/{name}”; ]; ]. Logical Table is a Table Name SubjectMap is a Template-‐valued TermMap And it has one Class IRI PredicateObjectMap PredicateMap which is a Constant-‐valued TermMap ObjectMap which is a Template-‐valued TermMap Note that there is not TermType

117. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com sid name pid 1 Juan 100 2 Martin 200 Student @prefix ex: <http://example.com/ns/>. ex:Student1 rdf:type ex:Student . ex:Student1 ex:studentType ex:GradStudent. ex:Student2 rdf:type ex:Student . ex:Student2 ex:studentType ex:GradStudent. TripleMap Example 5

118. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Example 6 @prefix rr: <http://www.w3.org/ns/r2rml#>. @prefix ex: <http://example.com/ns/>. <#TriplesMap1> rr:logicalTable [ rr:tableName ”Student”]; rr:subjectMap [ rr:template "http://example.com/ns/{sid}"; rr:class ex:Student; ]; rr:predicateObjectMap [ rr:predicate ex:studentType; rr:object ex:GradStudent ; ]. Logical Table is a Table Name SubjectMap is a Template-‐valued TermMap And it has one Class IRI PredicateObjectMap PredicateMap which is a Constant-‐valued TermMap ObjectMap which is a Constant-‐valued TermMap

119. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com RefObjectMap • A RefObjectMap (Referencing ObjectMap) allows using the subject of another TriplesMap as the object generated by a ObjectMap. • rr:objectMap • A RefObjectMap defined by – Exactly one ParentTripleMap, which must be a TripleMap – May have one or more JoinConditions

120. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName”Person" ]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicate foaf:based_near ; rr:objectMap [ rr:parentTripelMap <TripleMap2>; rr:joinCondition [ rr:child “CID”; rr:parent “CID”; ] ] ] . <TriplesMap2> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”City" ]; rr:subjectMap [ rr:template "http://ex.com/City/{CID}"; rr:class ex:City ]; rr:predicateObjectMap [ rr:predicate foaf:name; rr:objectMap [ rr:column ”TITLE" ] ] . 12 0 RefObjectMap

121. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com ParentTripleMap • The referencing TripleMap • rr:parentTriplesMap <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName”Person" ]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicate foaf:based_near ; rr:objectMap [ rr:parentTripelMap <TripleMap2>; rr:joinCondition [ rr:child “CID”; rr:parent “CID”; ] ] ] . Parent TriplesMap

122. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com JoinCondition • Join between child and parent attributes • rr:joinCondition <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName”Person" ]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicate foaf:based_near ; rr:objectMap [ rr:parentTripelMap <TripleMap2>; rr:joinCondition [ rr:child “CID”; rr:parent “CID”; ] ] ] . JoinCondition

123. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName”Person" ]; rr:subjectMap [ rr:template "http://www.ex.com/Person/{ID}"; rr:class foaf:Person ]; rr:predicateObjectMap [ rr:predicate foaf:based_near ; rr:objectMap [ rr:parentTripelMap <TripleMap2>; rr:joinCondition [ rr:child “CID”; rr:parent “CID”; ] ] ] . <TriplesMap2> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”City" ]; rr:subjectMap [ rr:template "http://ex.com/City/{CID}"; rr:class ex:City ]; rr:predicateObjectMap [ rr:predicate foaf:name; rr:objectMap [ rr:column ”TITLE" ] ] . 12 3 RefObjectMap Parent TriplesMap JoinCondition

124. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com JoinCondition • Child Column which must be the column name that exists in the logical table of the TriplesMap that contains the RefObjectMap • Parent Column which must be the column name that exists in the logical table of the RefObjectMap’sParent TriplesMap. <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName”Person" ]; ... rr:predicateObjectMap [ rr:predicate foaf:based_near ; rr:objectMap [ rr:parentTripelMap <TripleMap2>; rr:joinCondition [ rr:child “CID”; rr:parent “CID”;] ] ] . <TriplesMap2> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”City" ]; ... .

125. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com JoinCondition • Child Query – The Child Query of a RefObjectMap is the LogicalTable of the TriplesMap containing the RefObjectMap • Parent Query – The ParentQuery of a RefObjectMap is the LogicalTable of the Parent TriplesMap • If the ChildQuery and ParentQuery are not identical, then a JoinCondition must exist <TriplesMap1> a rr:TriplesMap; rr:logicalTable [ rr:tableName”Person" ]; ... rr:predicateObjectMap [ rr:predicate foaf:based_near ; rr:objectMap [ rr:parentTripelMap <TripleMap2>; rr:joinCondition [ rr:child “CID”; rr:parent “CID”;] ] ] . <TriplesMap2> a rr:TriplesMap; rr:logicalTable [ rr:tableName ”City" ]; ... .

127. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com sid name pid 1 Juan 100 2 Martin 200 pid name 100 Dan 200 Marcelo Student Professor ex:Student1 rdf:type ex:Student . ex:Student2 rdf:type ex:Student . ex:Professor100 rdf:type ex:Professor . ex:Professor200 rdf:type ex:Professor . ex:Student1 ex:hasAdvisor ex:Professor100 . ex:Student2 ex:hasAdvisor ex:Professor200 R2RML Mapping Example 7 ρ1 : Student(s, x, o) ∧ Professor(o, z) ∧ p = ex:hasAdvisor → Triple(s, p, o) ρ2 : Student(s, x, y) ∧ p = rdf:type∧ o = ex:Student → Triple(s, p, o) ρ3 : Professor(s, x) ∧ p = rdf:type∧ o = ex:Professor → Triple(s, p, o)

128. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix rr: <http://www.w3.org/ns/r2rml#>. @prefix ex: <http://example.com/ns/>. <#TriplesMap1> rr:logicalTable [ rr:tableName ”Student”]; rr:subjectMap [ rr:template "http://example.com/ns/{sid}"; rr:class ex:Student; ]; rr:predicateObjectMap [ rr:predicate ex:hasAdvisor; rr:objectMap [ rr:parentTriplesMap <#TriplesMap2>; rr:joinCondition [ rr:child “pid”; rr:parent “pid”; ] ] ]. <#TriplesMap2> rr:logicalTable [ rr:tableName ”Professor”]; rr:subjectMap [ rr:template "http://example.com/ns/{pid}"; rr:class ex:Professor; ]. RefObjectMap Parent TriplesMap JoinCondition

130. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Languages • TermMap with a TermType of rr:Literal may have a language tag • rr:language <#TriplesMap1> rr:logicalTable [ rr:tableName ”Student”]; rr:subjectMap [ rr:template "http://example.com/ns/{sid}"; rr:class ex:Student; ]; rr:predicateObjectMap [ rr:predicate ex:comment; rr:objectMap [ rr:column “comment”; rr:language “en”; ]; ].

131. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com sid name comment 1 Juan Excellent Student 2 Martin Wonderful student Student @prefix ex: <http://example.com/ns/>. ex:Student1 rdf:type ex:Student . ex:Student1 ex:comment “Excellent Student”@en . ex:Student2 rdf:type ex:Student . ex:Student2 ex:comment “Wonderful Student”@en .

132. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Issue with Languages • What happens if language value is in the data? ID COUNTRY_ID LABEL LANG 1 1 United States en 2 1 Estados Unidos es 3 2 England en 4 2 Inglaterra es

133. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com @prefix ex: <http://example.com/ns/>. ex:country1 rdfs:label “United States”@en . ex:country1 rdfs:label “Estados Unidos”@es . ex:country2 rdfs:label “England”@en . ex:country2 rdfs:label “Inglaterra”@es . ID COUNTRY_ID LABEL LANG 1 1 United States en 2 1 Estados Unidos es 3 2 England en 4 2 Inglaterra es ?

134. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Issue with Languages • Mapping for each language <#TripleMap_Countries_EN> a rr:TriplesMap; rr:logicalTable [ rr:sqlQuery """SELECT COUNTRY_ID, LABEL FROM COUNTRY WHERE LANG = ’en'""" ]; rr:subjectMap [ rr:template "http://example.com/country{COUNTRY_ID}" ]; rr:predicateObjectMap [ rr:predicate rdfs:label; rr:objectMap [ rr:column “LABEL”; rr:language “en”; ]; ].

135. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Datatypes • TermMap with a TermType of rr:Literal • TermMap does not have rr:language <#TriplesMap1> rr:logicalTable [ rr:tableName ”Student”]; rr:subjectMap [ rr:template "http://example.com/ns/{sid}"; rr:class ex:Student; ]; rr:predicateObjectMap [ rr:predicate ex:startDate; rr:objectMap [ rr:column “start_date”; rr:datatype xsd:date; ]; ].

136. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Summary of Terminology • R2RML Mapping • Logical Table • Input Database • R2RML View • TriplesMap • Logical Table Row • TermMap • TermType • SubjectMap • PredicateObjectMap • PredicateMap • ObjectMap • Constant-‐valued TermMap • Column-‐valued TermMap • Template-‐valued TermMap • RefObjectMap • JoinConditions • ChildQuery • ParentQuery • Language • Datatype

141. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com “Comparing the overall performance […] of the fastest rewriter with the fastest relational database shows an overhead for query rewriting of 106%. This is an indicator that there is still room for improving the rewriting algorithms” Larger numbers are better 100M Triple Dataset [Bizer and Schultz. Berlin SPARQL Benchmark 2009]

142. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Current rdb2rdf systems are not capable of providing the query execution performance required [...] it is likely that with more work on query translation, suitable mechanisms for translating queries could be developed. These mechanisms should focus on exploiting the underlying database system’s capabilities to optimize queries and process large quantities of structure data [Gray et al. 2009]

144. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Why was this happening if … ISWC 2008 Hypothesis: Existing commercial relational databases already subsume algorithms and optimizations needed to support effective SPARQL execution on relationally stored data

145. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Compile Time 1. Translate SQL Schema to OWL and Mapping 2. Define RDF Triples, as a View Run Time 3. SPARQL to SQL translation 4. SQL Optimizer creates relational query plan 14 5 Ultrawrap1: SPARQL to SQL under Direct Mapping Ultrawrap: SPARQL execution on relational data Sequeda & Miranker. J. WebSem 2013 US Patent 8719252, 9396283

146. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Creating Tripleview • For every ontology element (Class, Object Property and Datatype property), create a SQL SELECT query that outputs triples SELECT 'Product’+ptID as s, ‘label’ as p, label as o FROM Product WHERE label IS NOT NULL S P O Product1 label ACME Inc Product2 label Foo Bars ptID label prID 1 ACME Inc 4 2 Foo Bars 5 Product 14 6

147. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Creating Tripleview SELECT ‘Product’+ptID as s, prID as s_id, ‘label’ as p, label as o, NULL as o_id FROM Product WHERE label IS NOT NULL S S_id P O O_id Product1 1 label ACME Inc NULL Product2 2 label Foo Bars NULL ptID label prID 1 ACME Inc 4 2 Foo Bars 5 Product 14 7

148. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Creating Tripleview (…) • Create TripleViews (SQL View), which are unions of the SQL SELECT query that have the same datatype CREATE VIEW Tripleview_varchar AS SELECT ‘Product’+ptID as s, ptID as s_id, ‘label’ as p, label as o, NULL as o_id FROM Product UNION ALL SELECT ‘Producer’+prID as s, prID as s_id, ‘title’ as p, title as o, NULL as o_id FROM Producer UNION ALL … S S_id P O O_id Product1 1 label ACME Inc NULL Product2 2 label Foo Bars NULL Producer4 4 title Foo NULL Producer5 5 Ttitle Bars NULL 14 8

149. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com SPARQL and SQL • Translating a SPARQL query to a semantically equivalent SQL query SELECT ?label ?pnum1 WHERE{ ?x label ?label. ?x pnum1 ?pnum1. } à SELECT label, pnum1 FROM product SQL on Tripleview SELECT t1.o AS label, t2.o AS pnum1 FROM tripleview_varchar t1, tripleview_int t2 WHERE t1.p = 'label' AND t2.p = 'pnum1' AND t1.s_id = t2.s_id What is the Query Plan? 14 9

150. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Tripleview_varchar t1 Product π Product+’id’ AS s , ‘label’ AS p, label AS o σlabel ≠ NULL Producer π Producer+’id’ AS s , ‘title’ AS p, title AS o σtitle ≠ NULL U Tripleview_int t2 Product π Product+’id’ AS s , ‘pnum1’ AS p, pnum1 AS o σpnum1 ≠ NULL Product π Product+’id’ AS s , ‘pnum2’ AS p, pnum2 AS o σpnum2 ≠ NULL U π t1.o AS label, t2.o AS pnum1 σp = ‘label’ σp = ‘pnum1’ CONTRADICTION CONTRADICTION 15 0

151. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Detection of Unsatisfiable Conditions • Determine that the query result will be empty if the existence of another answer would violate some integrity constraint in the database. • This would imply that the answer to the query is null and therefore the database does not need to be accessed Chakravarthy, Grant and Minker. (1990) Logic-‐Based Approach to Semantic Query Optimization. 15 1

152. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Product π Product+’id’ AS s , ‘label’ AS p, label AS o σlabel ≠ NULL Product π Product+’id’ AS s , ‘pnum1’ AS p, pnum1 AS o σpnum1 ≠ NULL π t1.o AS label, t2.o AS pnum1 Join on the same table? à REDUNDANT 15 2

153. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Self Join Elimination • If attributes from the same table are projected separately and then joined, then the join can be dropped SELECT label, pnum1 FROM product WHERE id = 1 SELECT p1.label, p2.pnum1 FROM product p1, product p2 WHERE p1.id = 1 and p1.id = p2.id SELECT p1.id FROM product p1, product p2 WHERE p1.pnum1 >100 and p2.pnum2 < 500 and p1.id = p2.id SELECT id FROM product WHERE pnum1 > 100 and pnum2 < 500 Self Join Elimination of Projection Self Join Elimination of Selection 15 3

155. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Evaluation • Used Two Benchmarks that stores data in relational databases, provides SPARQL queries and their semantically equivalent SQL queries Detection of Unsatisfiable Conditions Self Join Elimination MYSQL MSSQL ORACLE DB2 ✖ ✔ ✖ ✖ ✖ ✔ ✔ ✔ 15 5

158. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Reflection 2 • We studied how SQL systems can be used to effectively evaluate SPARQL queries – HOW: Defined architecture based on SQL Views which allows RDBMS to do the optimization and Identified two important optimizations that already exist in commercial RDBMS. – EXTENT: SPARQL 1.0 (relational core) Recall the Hypothesis: Existing commercial relational databases already subsume algorithms and optimizations needed to support effective SPARQL execution on relationally stored data

160. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com UltrawrapOBDA: Ontology-‐Based Data Access • Given – a source relational database D – a target OWL ontology O, and – a mapping from the source database to the target ontology M • Goal: Answer SPARQL queries in terms of the target ontology using mappings and the database Hypothesis: We can effect optimizations for OBDA by push processing into the RDBMS, thus acting as a reasoner.

161. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com 16 1 ID Name Age Job 1 Alice 40 CTO 2 Bob 41 Java 3 John 42 SysAd Employee Executive IT Employee Programmer SysAdmin EMP subClassOf subClassOf subClassOf subClassOf EMP(s, y, z, ”CTO”) à Triple(s, type, ”CTO”) EMP(s, y, z, ”Java”) à Triple(s, type, ”Programmer”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”SysAdmin”) CTO subClassOf

163. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com State of the Art: Materialization SELECT ?x WHERE { ?x type ITEmployee} Triple(2,type,Programmer) Triple(2,type,ITEmployee) Triple(3,type,SysAdmin) Triple(3,type, ITEmployee) OWL Ontology Mapping Relational Database Materialization ?x =2 ?x =3 SPARQL Query Ans Programmer ⊑ ITEmployee SysAdmin ⊑ ITEmployee RDF Database EMP(s, y, z, ”Java”) à Triple(s, type, ”Programmer”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”SysAdmin”) EMP(ID,NAME,AGE,JOB) EMP(1,Alice,40,CTO) EMP(2,Bob,41,Java) EMP(3,John,42,SysAd)

164. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com State of the Art: Query Rewriting Rewriting Unfolding Evaluation SPARQL Query OWL Ontology Qo Qsql Mapping Ans Programmer ⊑ ITEmployee SysAdmin ⊑ ITEmployee SELECT ?x WHERE { ?x type ITEmployee} SELECT ?x WHERE {?x type ITEmployee UNION ?x type Programmer UNION ?x type SysAdmin} EMP(ID,NAME,AGE,JOB) EMP(1,Alice,40,CTO) EMP(2,Bob,41,Java) EMP(3,John,42,SysAd) ?x =2 ?x =3SELECT SID FROM EMP WHERE JOB = ‘Java’ UNION SELECT SID FROM EMP WHERE JOB = ‘SysAd’ Database EMP(s, y, z, ”Java”) à Triple(s, type, ”Programmer”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”SysAdmin”)

165. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com 16 5 RDB2RDF Mapping SQL Optimizer: Views & Recursion Inheritance & Transitivity Hybrid: Backward – Forward Chaining RDBMS OWL Ontology

166. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Hybrid Approach: UltrawrapOBDA OWL Ontology Mapping Programmer ⊑ ITEmployee SysAdmin ⊑ ITEmployee EMP(s, y, z, ”Java”) à Triple(s, type, ”Programmer”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”SysAdmin”) Compiler EMP(ID,NAME,AGE,JOB) EMP(1,Alice,40,CTO) EMP(2,Bob,41,Java) EMP(3,John,42,SysAd) Saturated Mapping EMP(s, y, z, ”Java”) à Triple(s, type, ”Programmer”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”SysAdmin”) EMP(s, y, z, ”Java”) à Triple(s, type, ”ITEmployee”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”ITEmployee”) OBDA: query rewriting or materialization? In practice, both! Sequeda, Arenas, Miranker. ISWC 2014 (Best Paper)

168. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Generating Saturated Mappings Programmer subClassOf ITEmployee X type Programmer X type ITEmployee Programmer subClassOf ITEmployee EMP(s, x1, “Java”) ∧ p = type ∧ o = Programmer→ Triple(s, p, o) EMP(s, x1, “Java”) ∧ p = type ∧ o = ITEmployee→ Triple(s, p, o) ρ : EMP(s, y, z, ‘Java’) ∧ p = type ∧ o = Programmer→ Triple(s, p, o) Predicate ObjectQuery over R and Subject

169. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Transitivity and SQL Recursion 16 9 WITH MANAGER (X, Y) AS( SELECT ID, MAN FROM EMP UNION ALL SELECT EMP.ID, MANAGER.Y FROM EMP, MANAGER WHERE EMP.MAN= MANAGER.X ) SELECT X, Y FROM MANAGER

170. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com OWL 2 QL OWL 2 RL OWL 2 EL OWL 2 DL EL QL RL subClass X X X subProp X X X domain X X X range X X X eqClass X X X eqProp X X X inverseProp X X symProp X X transProp X X 17 0 Check paper for all 9 Inference rules

171. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Saturated Mappings 17 1 OWL Ontology Qo Mapping Programmer ⊑ ITEmployee SysAdmin ⊑ ITEmployee EMP(s, y, z, ”Java”) à Triple(s, type, ”Programmer”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”SysAdmin”) Compiler EMP(ID,NAME,AGE,JOB) EMP(1,Alice,40,CTO) EMP(2,Bob,41,Java) EMP(3,John,42,SysAd) Saturated Mapping EMP(s, y, z, ”Java”) à Triple(s, type, ”Programmer”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”SysAdmin”) EMP(s, y, z, ”Java”) à Triple(s, type, ”ITEmployee”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”ITEmployee”) Saturated Mappings are similar to T-‐Mapping per Rodriguez-‐Muro et. al. (AMW2011, ISWC2013) Saturation is performed by exhaustively applying the inference rules We present a linear-‐time algorithm Check paper

172. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Represent Saturated Mappings as SQL Views 17 2 OWL Ontology Qo Mapping Programmer ⊑ ITEmployee SysAdmin ⊑ ITEmployee EMP(s, y, z, ”Java”) à Triple(s, type, ”Programmer”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”SysAdmin”) Compiler EMP(ID,NAME,AGE,JOB) EMP(1,Alice,40,CTO) EMP(2,Bob,41,Java) EMP(3,John,42,SysAd) Saturated Mapping EMP(s, y, z, ”Java”) à Triple(s, type, ”Programmer”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”SysAdmin”) EMP(s, y, z, ”Java”) à Triple(s, type, ”ITEmployee”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”ITEmployee”) CREATE VIEW ITEmployeeView AS SELECT ID as S, “type” as P, “ITEmployee” as O FROM EMP where JOB = ‘Java’ UNION …

173. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com From Saturated Mappings to SQL Views Mapping Triplequery Tripleview EMP(s, x1, “Java”) ∧ p = type ∧ o = ITEmployee → Triple(s, p, o) EMP(s, x1, “SysAd”) ∧ p = type ∧ o = ITEmployee → Triple(s, p, o) SELECT ID as S, “type” as P, “ITEmployee” as O FROM EMP where JOB = ‘Java’ SELECT ID as S, “type” as P, “ITEmployee” as O FROM EMP where JOB = ‘SysAd’ CREATE VIEW ITEmployeeView AS SELECT ID as S, “type” as P, “ITEmployee” as O FROM EMP where JOB = ‘Java’ UNION SELECT ID as S, “type” as P, “ITEmployee” as O FROM EMP where JOB = ‘SysAd’ S P O … … …

174. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com All of this is offline OWL Ontology Qo Mapping Programmer ⊑ ITEmployee SysAdmin ⊑ ITEmployee EMP(s, y, z, ”Java”) à Triple(s, type, ”Programmer”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”SysAdmin”) Compiler EMP(ID,NAME,AGE,JOB) EMP(1,Alice,40,CTO) EMP(2,Bob,41,Java) EMP(3,John,42,SysAd) Saturated Mapping EMP(s, y, z, ”Java”) à Triple(s, type, ”Programmer”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”SysAdmin”) EMP(s, y, z, ”Java”) à Triple(s, type, ”ITEmployee”) EMP(s, y, z, ”SysAd”) à Triple(s, type, ”ITEmployee”) CREATE VIEW ITEmployeeView AS SELECT ID as S, “type” as P, “ITEmployee” as O FROM EMP where JOB = ‘Java’ UNION …

175. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Runtime: SPARQL Execution 17 5 EMP(ID,NAME,AGE,JOB) EMP(1,Alice,40,CTO) EMP(2,Bob,41,Java) EMP(3,John,42,SysAd) SELECT ?x WHERE { ?x type ITEmployee} s = 2 s = 3 SPARQL Query Ans CREATE VIEW ITEmployeeView AS SELECT ID as S, “type” as P, “ITEmployee” as O FROM EMP where JOB = ‘Java’ UNION … SELECT s FROM Tripleview WHERE p = type and o = ITEmployee SQL Query on Views S P O … … … Theorem: Given a RDB2RDF Mapping M, every SPARQL query is SQL-‐rewritable under M Proof: by induction on the structure of SPARQL

176. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Query Optimization: Materialize Views? 17 6 Best query response time Worst query response time Less Space Consumed Most Space Consumed Materialize Everything Materialize Nothing Harinarayan et al. Implementing Data Cubes Efficiently. SIGMOD96 ….. Mami & Bellahsene. A Survey of View Selection Methods. SIGMOD Record 2012 Hybrid Approach

177. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Cost Model Best query response time Worst query response time Less Space Consumed Most Space Consumed Materialize All Materialize Nothing Materialize views representing mappings to leaf classes Query cost = n x NR x S(A,R) Space cost = NR + (NR x d) Query cost = n x NR Space cost = NR Query cost = n x NR x S(A,R) Space cost = 2NR Hypothesis: If a RDBMS rewrites queries in terms of materialized views, then … Check paper for details n is the number of leaf classes underneath the class that is being queried NR is the number of tuples of the relation R in the mapping S(A, R) is the selectivity of the attribute A of the relation R in the mapping d is the depth of the ontology

178. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Texas Benchmark id …. TWO FIVE TEN TWENTY FIFTY HUNDRED 1 1 1 1 1 1 … … … … … … 2 5 10 20 50 100 1 100… d = 2 1 100… d = 5… … Database … Ontologies Goal: Understand the behavior when querying for instances of a class depending on the depth of the ontology and the selectivity

180. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com BSBM Extension for Transitivity SELECT ?x WHERE { ?x typeAncestor ProductType7 } SELECT ?product ?x WHERE { ?x typeAncestor ProductType7 . ?product hasType ?x. } SELECT ?product ?x WHERE { ?x typeAncestor ProductType7 . ?product hasType ?x. ?product label ?label . ?product numProp ?num.} Product ProductType hasType typeAncestor Literal Literal label numProp Ontology Simple Query Join Query More Join Query

182. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Reflection 3 • We studied how SQL systems can be used as reasoners for SPARQL queries in terms of Ontologies – HOW: Incorporate semantics of ontologies in Saturating Mappings and take advantages of query rewriting using materialized views and recursion which exist in RDBMS – EXTENT: OWL-‐ SQL Recall the Hypothesis: We can effect optimizations for OBDA by push processing into the RDBMS, thus acting as a reasoner.

184. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com past à present à FUTURE • Federated Semantic Data Management – “Semantify” data by mapping to ontologies • Business view of heterogeneous data – Federation (NoETL) in order to avoid centralization (ETL) – Dagstuhl seminar on this topic (June 2017) • http://www.dagstuhl.de/17262 • “Start” of commercial interest – Startups: Capsenta, … – Industries: Pharma, Finance, … – EU Project: Optique

187. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com It’s a Semantic Problem! What is an Order? When a user clicks “Order” on the website When the customer has received the product When it comes out of the billing system and the CC has been charged Billing Shipping E-‐Commerce 18 7

189. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com IT Biz Total net sales of all Orders today Data Architect SELECT .. FROM … csv csv csv MS Access T=1 T=2T=3 XLS Did the Biz User communicate the correct message to IT? Did IT understand correctly what the Biz User wanted? Did IT deliver the correct/precise results? Reports XLS XLS Status Quo 1 18 9

190. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Enterprise Data Warehouse IT Biz Reports Time and $ Total net sales of all Orders today ETL ETL ETL Total net sales of all Orders today with FX Status Quo 2 Data Architect 19 0

191. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Integrating Data using Graphs and Semantics 19 1 HIVE Impala, etc Oracle SQL Server Postgres Unstructured Semi-‐ Structured Mappings Enterprise Knowledge Graph Search ReportsAPI Dashboard

192. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Semantic Technology is not easy Who creates this? Using what tools? Funny Note: I found my presentation from 2007 where I asked this same question

195. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Mappings and Ontologies from Questions 19 5 A Pay-‐As-‐You-‐Go Methodology for Ontology-‐Based Data Access Sequeda & Miranker. IEEE Internet Computing 2017

196. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Real World Example SELECT o.orderid, o.orderdate, o.ordertotal - ot.finaltax - CASE WHEN o.currencyid in (‘USD’, ‘CAD’) THEN o.shippingcost ELSE o.shippingcost - ot.shippingtax END AS netsales, o.currencyid FROM order o, ordertax ot WHERE o.orderid = ordertax.orderid AND o.statusid NOT IN (4, 5)

197. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Reflection 4 • We are studying how non-‐semantic web (and non-‐technical) users can integrate data using semantic web technologies – HOW: We need better tools – EXTENT: I don’t know

199. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com HOW and to what EXTENT can RDB be integrated with the SW? 1. RDB can be automatically directly mapped to RDF and OWL – Monotone, Information and Query Preserving – Monotone is obstacle for Semantics Preserving 2. RDB can evaluate and optimize SPARQL 1.0 queries – Two important optimizations 3. RDB can act as a reasoner for Ontologies with inheritance and transitivity – Saturated mappings, query rewriting using mat views and recursion

200. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com Tipping Point Relational Database Semantic Web • Semantics • “Graphy” Queries • Data Integration • Flexible • Metadata • Provenance • Graph Visualizations OWL 2 QL OWL 2 RL OWL 2 EL OWL 2 DL OWL SQL

201. Smart Data for Smarter Business | © 2016 Capsenta | capsenta.com HOW and to what EXTENT can RDB be integrated with the SW? 20 1 Juan Sequeda, Ph.D Co-‐Founder – Capsenta juan@capsenta.com @juansequeda Sequeda J. Integrating Relational Databases with the Semantic Web. IOS Press. 2016 http://www.iospress.nl/book/integrating-‐relational-‐databases-‐with-‐the-‐semantic-‐web/ We are always looking for smart people THANK YOU! RDB can be automatically directly mapped to RDF and OWL and preserve information and queries RDB can evaluate and optimize SPARQL 1.0 queries RDB can act as a reasoner for Ontologies with inheritance and transitivity

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