1. Semantic Technology in Oracle Database

2. Data Interoperability Challenges Data locked into schemas, formats, software systems Semantic technology seen as a possible solution Specialty RDF data management engines are isolated from the data to be integrated In addition there are high training costs, systems admin costs, management costs. Tightly coupling semantics (RDF/OWL) functionality to the data storage infrastructure will facilitate data integration using semantics RDF/OWL Triples Business Data Semantic Apps Business Apps Enterprise Data Server RDF Data Server

3. Adding advanced RDF services to Oracle Database Database features and queries can be enhanced using semantics Hybrid queries between enterprise data and semantic data possible Databases are part of infrastructure in several categories of applications that use semantics for data integration Biosurveillance, Social Networks, Telcos, Utilities, Text, Life Sciences, GeoSpatial All database benefits become available for semantic applications Scalability: Manage datasets 10X larger than specialized RDF/OWL stores (billions of triples), no scalability boundaries Billions of nodes, large graphs, parallel loading, query, indexing Security, transaction control, availability, backup and recovery, lifecycle management, etc. Can combine multiple datatypes (geospatial, sensor, etc. with semantic data)

4. Oracle 10g RDF Approach Provide an open and persisted RDF data model and analysis platform for semantic applications RDF Data Model with inferencing (RDFS and user- defined rules) Inferencing based on forward-chaining Perform SQL-based access to triples and inferred data Combine SQL query of business with RDF graphs and ontologies Support large graphs (billion+ triples) Easily extensible by 3 rd party tools/apps

5. Use Case: Knowledge Mining Solutions Information Extraction Categorization, Feature/term Extraction Web Resources News, Email, RSS Content Mgmt. Systems Processed Document Collection RDF/OWL Knowledge Mining & Analysis Text Indexing using Oracle Text Non-Obvious Relationship Discovery Pattern Discovery Text Mining Faceted Search Analyst Browsing, Presentation, Reporting, Visualization, Query SQL/SPARQL Query Explore Domain Specific Knowledge Base OWL Ontologies Ontology Engineering Modeling Process

6. Geospatial Semantic Search Schemas: Persisted RDF/OWL data Persisted spatial data Persisted business data Persisted text data GeoSemantic Processes Text Extraction Semantic Modeling Rules/Policy Mgmt. Geospatial Analysis Map Visualization Semantic Search RDF Models Spatial Data Oracle 10g RDBMS Business Data Text Data

7. Semantic Solutions on the Web Deploying on a SOA Infrastructure Simple Features GeoRaster Topology Networks Spatial Data Mining Geocoding Routing Versioning DBMS Rules J2EE Container SOAP Web sevices Orchestration & Workflow Security Policy based resource mgmt Workload scaling Portal Wireless & Sensor Core Software Infrastructure Semantic- Enabled tools Applications & Services Business Logic Entity Extraction Visualization Ontology Modeling Faceted Search Link/Graph Analysis Advanced Inference Metadata Repository Entity Categorization Relationship analysis National Security Financial Risk Analysis Regulatory Compliance Life Sciences Drug Discovery Health Science BioSurveillance Manufacturing Configuration Management

8. Semantic Technology Stack Standards based

9. Based on Standards Our implementation entirely based on W3C standards (RDF, RDFS, OWL) SPARQL support is planned We are members of: W3C DAWG (WG responsible for SPARQL) W3C SWEO Interest group W3C HCLS Interest group W3C Multimedia Semantics Incubator group Soon to be formed W3C OWL 1.1 Working group

10. Technical Features Database storage model for data represented in RDF SQL-based query of RDF data Combining RDF queries with relational queries Native inferencing engine to infer new relationships from RDF data

11. Technical Overview RDF/OWL data and ontologies Enterprise (Relational) data Query RDF/OWL data and ontologies Combining relational queries with RDF/OWL queries INFER STORE QUERY RDF/S User def. rules Batch- Load Incr. Load and DML

12. Storage: Highlights Stores triples Set of triples form an RDF/OWL graph (model) Optimized storage structure: repeated values stored only once (uses normalization) Scales to very large datasets No limits to amount of data that can be stored Current users: 600Million+ triples (UTH) Can handle multiple lexical forms of the same value Ex: “0010”^^xsd:decimal and “010”^^xsd:decimal Maintains fidelity (user-specified lexical form) Supports long literal values JohnOracle :employeeOf

13. Semantic Data Storage ID (number)TRIPLE (sdo_rdf_triple_s) ……… Model Triple (SDO_RDF_T RIPLE_S) ….. Internal Semantic Store Application table 1 Application table 2 Application table links to model in internal semantic store Optional columns for related enterprise data

14. Query RDF Data SPARQL-like graph pattern embedded in SQL query Matches RDF/OWL graph patterns with patterns in stored data Returns a table of results Can use SQL operators/functions to process results Avoids staging when combined with queries on relational data Scales: millisecond query times for large data sets (10M+ triples) SELECT … FROM …, TABLE ( SDO_RDF_MATCH invocation ) t, … WHERE … SDO_RDF_MATCH( '(?x rdf:type :Person)', -- pattern: all persons SDO_RDF_Models('family'), -- RDF data models SDO_RDF_Rulebases(‘RDFS'), -- rulebases SDO_RDF_Aliases(…) -- aliases null -- no filter condition )

15. Query Example: Family Data select x, y, name from TABLE(SDO_RDF_MATCH( ‘(:Tom :hasParent ?x) (?x :hasFather ?y) (?y :name ?name)', SDO_RDF_Models('family'),..,..,..)); Returns the name of Tom’s grandfather :Jack :Tom :Janice:John :Suzie :Matt “ John D ” XYNAME MattJohn“John D”

16. Combining RDF Queries with Relational Queries Find salary and hiredate of Tom’s grandfather(s) SELECT emp.name, emp.salary, emp.hiredate FROM emp, TABLE(SDO_RDF_MATCH( ‘(:Tom :hasParent ?y) (?y :hasFather ?x) (?x :name ?name)’, SDO_RDF_Models(‘family'), …)) t WHERE emp.name=t.name;

17. Inference: Overview Native inferencing in the database for RDF, RDFS User-defined rules Rules are stored in rulebases in the database RDF graph is entailed (new triples are inferred) by applying rules in rulebase/s to model/s Inferencing is based on forward chaining: new triples are inferred and stored ahead of query time Minimizes on-the-fly computation and results in fast query times

18. Inferencing RDFS Example: A rdf:type B, B rdfs:subClassOf C => A rdf:type C Ex: Matt rdf:type Father, Father rdfs:subClassOf Parent => Matt rdf:type Parent User-defined Rules Example: A :hasParent B, B :hasParent C => A :hasGrandParent C Ex: Tom :hasParent Matt, Matt :hasParent John => Tom :hasGrandParent John

19. Query Example: Family Data select y, name from TABLE(SDO_RDF_MATCH( ‘(:Tom :hasGrandParent ?y) (?y :name ?name)’ (?y rdf:type :Male), SEM_Models('family'), SEM_Rulebases(‘family_rb),..,..)); Returns the name of Tom’s grandfather YNAME John‘John D’ :Jack :Tom :Janice:John :Suzie :Matt “ JohnD ” Male

20. Data Integration in the Life Sciences “Find all pieces of information associated with a specific target” Data integration of multiple datasets Across multiple representation formats, granularity of representation, and access mechanisms Across In-house and public sets (Gene Ontology, UniProt, NCI thesaurus, etc.). Standardized and machine-understandable data format with an open data access model is necessary to enable integration Data-warehousing approach represents all data to be integrated in RDF/OWL Semantic metadata layer approach links metadata from various sources and maps data access tool to relevant source Ability to combine RDF/OWL queries with relational queries is a big benefit Lilly and Pfizer are using semantic technology to solve data integration problems

21. Use Case: SenseLab Overview Courtesy, SenseLab, Yale University

22. Relational to Ontological Mapping Drug Neuron Pathological Agent Receptor Channel inhibits Agent Neuronal Property Pathological Change involves inhibits Compartment has is_located_in Courtesy, SenseLab, Yale University

23. Semantic Technology Plans for the Next Release

24. Safe Harbor Statement & Confidentiality The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

25. Plans for the Next Release Fast bulk-load RDF/OWL data into the database Several times faster than 10.2.0.2 batch load Infer new triples with native OWL inferencing Faster query of RDF/OWL data and ontologies Ontology-Assisted Query of relational data

26. Overview RDF/OWL data and ontologies Enterprise (Relational) data Query RDF/OWL data and ontologies INFER STORE Ontology-Assisted Query of Enterprise Data QUERY RDF/S User-def. Batch- Load OWL subsets Bulk- Load Incr. DML

27. Technical Overview Summary Semantic Technology support in the database Store RDF/OWL data and ontologies Infer new RDF/OWL triples via native inferencing Query RDF/OWL data and ontologies Ontology-Assisted Query of relational data