1 / 33

SQL Databases are a Moving Target

SQL Databases are a Moving Target. Juan F. Sequeda – jsequeda@cs.utexas.edu Syed Hamid Tirmizi – hamid@cs.utexas.edu Daniel P. Miranker – miranker@cs.utexas.edu Department of Computer Sciences The University of Texas at Austin. My favorite slide. SEMANTIC WEB. DATABASES.

bernadine-wolf
Télécharger la présentation

SQL Databases are a Moving Target

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. SQL Databases are a Moving Target Juan F. Sequeda – jsequeda@cs.utexas.edu Syed Hamid Tirmizi – hamid@cs.utexas.edu Daniel P. Miranker – miranker@cs.utexas.edu Department of Computer Sciences The University of Texas at Austin

  2. My favorite slide SEMANTIC WEB DATABASES

  3. The Semantic Web Vision Semantic Query Engine User Layer: Users and agents interact with this layer and query the ontologies Ontology Layer: defines the semantic representation of RDB. Inference Local ontology Local ontology Local ontology DB DB DB Database Layer: Data that needs to accessed semantically

  4. So what should be done? • Make it easy for existing databases to generate Semantic Web content. Research Problem(s): Generate Ontologies from Database Content • (many systems already make RDF from database data) • Mapping database restrictions to OWL • People don’t live past 120 years old • Mining database content for additional domain knowledge • Professors earn more than teaching assistants

  5. Semantic Query Engine Local ontology Local ontology Local ontology DB DB DB Finally: Can the Semantic Web Work? • We see a big problem: • Who builds this? • Who generates thesemappings?

  6. The Semantic Web Vision Semantic WebQuery Engines Local ontology generate Schema/ Metadata RDF DB Table Content SQL-query translate

  7. Our Position • SQL has semantics • SQL has been evolving  it is a moving target! • SQL DDL can be used to generate local ontologies • A Greatest Common Denominator like OWL-DL can aid with data integration

  8. Why are SQL Databases Moving Targets? • In the beginning we had… Relational ModelStudent(Juan, 22) • SQL86-89 came out with Table DefinitionsCREATE TABLE employee ( name VARCHAR(100), age INTEGER) • SQL92 added data integrity ConstraintsCHECK, PRIMARY KEY, FOREIGN KEY, UNIQUE • SQL99 added Triggers

  9. SQL DDL to Ontologies • Requirements • To create the ontology automatically, we need to compare the technologies • We need to identify constructs with similar semantics • Analysis • Our analysis leads to a layer cake for SQL with corresponding layers in the Semantic Web stack • Implementation • We express our transformations as FOL and use SQL BNF as a guideline for completeness.

  10. Relational Model to RDF • Relational Model • Employee(name, age) • T1: Employee (Juan, 21) • RDF <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:ex="http://www.example.com/#"> <rdf:Description rdf:about="http://www.example.com/employee"> <ex:name>Juan</ex:name> <ex:age>21</ex:age> </rdf:Description> </rdf:RDF>

  11. Table Definition to RDFS • Table Definition CREATE TABLE employee ( name VARCHAR(100), age INTEGER) • RDFS

  12. Table Definition to RDFS <?xml version="1.0"?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"> <rdfs:Class rdf:ID="employee"> <rdfs:comment>Employee</rdfs:comment> <rdfs:subClassOf rdf:resource="http://www.w3.org/1999/02/22-rdf-syntax- ns#Resource"/> </rdfs:Class> <rdf:Property rdf:ID="name"> <rdfs:comment>Name of Employee</rdfs:comment> <rdfs:domain rdf:resource="#employee"/> <rdfs:range rdf:resource="http://www.w3.org/1999/02/22-rdf-syntax-ns#Literal"/> </rdf:Property> <rdf:Property rdf:ID="age"> <rdfs:comment>Age of Employee</rdfs:comment> <rdfs:domain rdf:resource="#employee"/> <rdfs:range rdf:resource="http://www.w3.org/1999/02/22-rdf-syntax-ns#Literal"/> </rdf:Property> </rdf:RDF> Table Name Attributes Data type

  13. SQL to RDFS • Tables  rdfs:Class • Columns  rdf:Property • Table  rdfs:Domain • Datatype  rdfs:Range • Foreign Keys  rdf:Property • Table  rdfs:Domain • Referencing Table  rdfs:Range

  14. SQL to RDFS

  15. SQL to OWL CREATE TABLE employee( employee_id INTEGER PRIMARY KEY, employee_ssn VARCHAR(11) UNIQUE, employee_name VARCHAR(100) NOT NULL, employee_salary INTEGER NOT NULL, employee_type CHAR(8) CHECK ( employee_type IN ('TEMP', 'FULLTIME', 'CONTRACT')) dept INTEGER FOREIGN KEY (dept) REFERENCES department (dept_id)) CREATE TABLE department( dept_id INTEGER PRIMARY KEY, dept_name VARCHAR(100) NOT NULL, manager INTEGER FOREIGN KEY (manager) REFERENCES employee (employee_id))

  16. SQL to OWL <owl:Class rdf:ID="Department"/> <owl:ObjectProperty rdf:ID="dept"> <rdfs:domain rdf:resource="#Employee"/> <rdfs:range rdf:resource="#Department"/> </owl:ObjectProperty> <owl:DatatypeProperty rdf:ID="age"> <rdf:type rdf:resource="&owl;FunctionalProperty"/> <rdfs:domain rdf:resource="#Employee"/> <rdfs:range rdf:resource="&xsd;int"/> </owl:DatatypeProperty> Table Name Foreign Key Attribute Data type

  17. SQL to OWL <owl:DatatypeProperty rdf:ID="employee_type"> <rdfs:domain rdf:resource="#Employee"/> <rdfs:range> <owl:DataRange> <owl:oneOf> <rdf:List> <rdf:first rdf:datatype="&xsd;string">Temp</rdf:first> <rdf:rest> <rdf:List> <rdf:first rdf:datatype="&xsd;string">Fulltime</rdf:first> <rdf:rest> <rdf:List> <rdf:first rdf:datatype="&xsd;string">Contract</rdf:first> <rdf:rest rdf:resource="&rdf;nil"/> </rdf:List> </rdf:rest> </rdf:List> </rdf:rest> </rdf:List> </owl:oneOf> </owl:DataRange> </rdfs:range> </owl:DatatypeProperty> Enumerated Check Constraint

  18. SQL to OWL

  19. Rules: SQL to OWL • Binary Relation: a relation that only has two foreign keys (single or composite) referencing two relations. • Class: A relation that is not a binary relation is a class. • Object Property • A binary relation is an object property • A foreign key that references another relation is an object property, whose domain is the current relation and range is the relation that the foreign key references

  20. Rules: SQL to OWL • Data type Property: If an attribute is not an object property, then it is a data type property. Its domain is the current relation and the range is the data type.

  21. SQL to Rules • Triggers are business rules and it can not be expressed in OWL • Should think about how this could be mapped to the rule layer of the Semantic Web And the final layer cake…

  22. SQL Layer Cake

  23. Greatest Common Denominator for Data Integration • All ontologies generated by SQL should have similar semantic power to facilitate better data integration • Use of different technologies may cause problems in data integration • The constructs we use has OWL-DL as GCD • OWL-DL is what SW people like the most. • Reasoning and Inference • Decidability • Computational Completeness • Therefore OWL-DL should be the target for SQL to Semantic Web transformations.

  24. Summary • SQL has semantics • SQL has been evolving  it is a moving target! • SQL DDL can be used to generate local ontologies • A Greatest Common Denominator like OWL-DL can aid with data integration

  25. Thank You

  26. Other Slides…

  27. Discussion Topics • Hierarchy and Inheritance • SQL DDL doesn’t have it • How do database people model it • Not clear how to get the semantics • CHECK Constraint • Has more semantics that OWL can handle • CHECK (value >0 AND value < 360) • OWL Constraints • allValuesFromsomeValuesFrom • URI

  28. Discussion Topics: Hierarchy and Inheritance • Hierarchy and Inheritance • Integrate information that is spread across several relations (vertical partitioning) and can be either • Integrated in one concept • Inheritance But how do you decide! • Key Equality and Data Inclusion: two relations that share the same primary key and the child relation’s primary key is also a foreign key SoftwareProject Project PK PK, FK

  29. Discussion Topics: Hierarchy and Inheritance • How do you represent Hierarchy and Inheritance in database? • Hierarchy: Hand - Finger Hand Finger ? PK FK • Inheritance: Person - Student Person Student PK PK PK, FK

  30. Discussion Topics: someValuesFrom vs allValuesFrom A(id, x) B(id, y) C(A_id, B_id) • Can we say that A and B are classes? Yes • Can we say that C represents an object property? Yes • Can we say that B can only be connected to A using the object property? ¿? • Can we say anything about the cardinality of this relation? ¿?

  31. Discussion Topics: CHECK Constraint • Embodies semantics and rules at the same time • Enumerated CHECK constraint  owl:one of • But what about: • CHECK (value >0 AND value < 360)

  32. Translation BNF: SQL to OWL <sql-owl statement> ::= <class defintion> <property table>| <object property definition> <class definition> ::= CREATE TABLE <table name> <object property definition> :: = <object definition> <object property table> <object definition> ::= CREATE TABLE <table name> <object property table> ::= <left paren> <object property references> <object property references> <right paren> <property table>::= <left paren> <property> [ { <comma> <property}...] <property> ::= <object property> |<datatype property>

More Related