Enterprise Solutions for the Semantic Web Ralph Hodgson, TopQuadrant Susie Stephens, Oracle

1. Enterprise Solutions for the Semantic Web Ralph Hodgson, TopQuadrant Susie Stephens, Oracle SICOP 4th Semantic Interoperability fore-Government Conference,February 9-10, 2006,Mitre, McLean, VA

2. Agenda • Introduction • Semantic Web Technology Overview • Architecture of the Oracle RDF Data Model • Life Sciences Use Cases and Demos • Wrap Up

3. is Us

4. You Knowledge/Experience Adoption ? Advocacy Enthusiasm Curiosity Skepticism Commitment to Semantic Technology

5. Adoption of Semantic Technology Knowledge/Experience Current State Adoption Confidence in ability to implement and scale Advocacy 2005 Positive experiences of the power of RDF/OWL Enthusiasm 2003 People are now asking “How” questions as opposed to “Why” and “What”. Curiosity 2002 Skepticism Increase in attendance at trainings and more evidence of coverage at conferences Commitment to ST

6. Applications are getting smarter

7. What is Semantic Technology? “Semantic technology (software) allows the meaning of and associations between information to be known and processed at execution time. For a semantic technology to be truly at work within a system, there must be a knowledge model of some part of the world (an active ontology) that is used by one or more applications at execution time.” -- TopQuadrant

8. Static Dynamic Transactional Semantic Encoding + XML J2EE, .NET, … + RDBMS JSP, ASP, Java, … HTML CGI, Perl, ... + RDF, OWL ? Generated by applications based on fixed schemas, used by applications and people Generated by applications based on models, used by applications, devices and people Generated applying specific templates, used by people Hand crafted by people for people Creation A catalog becomes a transaction platform Set of mind = “interact” Advertisement, Information, 1 large newspaper Set of mind = “browse” A newspaper becomes a catalog Set of mind = “retrieve/update” Platforms connect Set of mind = “interoperate” Paradigm Marketing Sales Service Integration • Advisors • Personal Agents • IP Apps • Cognitive Engines • Search • Content Mgmt • Web Application Servers • Portals • Process Integration • Web Services Killer Apps • Browser 1995 2000 2005 Evolution of the WEB

9. ... The Semantic Continuum Very Rich Metadata: OWL Richer Metadata: RDF/S Simple Metadata: XML Computer interpreted Human interpreted Interpretation Continuum KNOWLEDGE DATA • Very structured • Logical • Relatively unstructured • Random • Info retrieval • Web search • Text summarization • Content extraction • Topic maps • Reasoning services • Ontology Induction Automatically acquire concepts; evolve ontologies into domain theories; link to institution repositories (e.g., MII) Automatically span domain theories and institution repositories; inter-operate with fully interpreting computer Store and connect patterns via conceptual model (i.e,. an ontology); link to docs to aid retrieval Find and correlate patterns in raw docs; display matches only Display raw documents; All interpretation done by humans Moving to the right depends on increasing automated semantic interpretation Adapted from: Leo Obrst, “Ontologies and the Semantic Web: An Overview” Mitre, June 2005

10. The Semantic Stack

11. The Quadrants of Meaning Modal Logics Semantic Executable Models Semantic Descriptions Thesaurus FOL OWL-DLP DL Formal CG Topic Maps Taxonomy OWL-DL OWL-Lite RDFS Rules Terminology Management MDA UML Syntactical Consensus XML Textual Descriptions PDF Code ER Informal HTML Human Machine

12. OWL CD CD Reasoning RDFS A CD Classes Is-a RDF A B Relationships hasTrack XML Structures The Semantic Stack - Demystified + Proof + Trust Rules

13. Mapping Capability Cases Product Design Assistant Semantic Web Services Composer Expert Locator Ontology Driven Information Retriever Formal Context-Aware Retriever Semantic Data Integrator Concept-Based Search Semantic Multi-Faceted Search Semantic Workplace Semantic Data Registry Semantic Portal Semantic Web Server Generative Documentation News Aggregator Recommender Application Integrator Informal Human Machine

14. Semantic Data Integrator: Consulting Services Company Data Quality Semantic Data Integrator: FAA Air Passenger Threat Analyzer Semantic Content Registry: European Environment Agency ReportNet Rights Mediator: RightsCom Policy Engine An international services company wanted to see side-by-side information from its American & European divisions. Different divisions had their own definitions of key business indicators such as utilization rates. The system uses technology from Unicorn Solutions. Product Design Assistant: Semantic Testcar Configurator The system allows security personnel to assess passenger threats. Based on an Ontology and the Semagix Freedom engine, the system interfaces with diverse information sources, extracts relevant information in near real-time, unifying the data against the model. Concept-based Search: Siemens Self-Service for Industrial Equipment Expert Locator: Boeing’s Expert Locator The Semantic Content Registry gets its information from multiple Data Repositories through harvesting them for metadata (pull) or through notifications after upload events (push). The registry uses RDF to keep track of the deliveries of data sets. Using OWL and semantic technology from Network Inference, RightCom has built an integrated solution for rights management in the media and entertainment industry. A major European car manufacturer uses semantic technologies provided by Ontoprise to represent complex design knowledge in electronic form. Knowledge is integrated from different sources, across which the system draws logical conclusion. Simatic is a self-service WEB application for Siemens Industrial Control Products. The system uses a model-based CBR engine called Orenge from Empolis. Boeing has a large workforce of experts making it hard to find the right person. This web-based system returns details on potentially appropriate experts. The Boeing technical thesaurus was harnessed to create expert profiles. Early Adopters:A Quick Look at 7 Capability Cases

15. Ontologies are like and unlike other IT models • Like databases ontologies are used by applications at run time (queried and reasoned over) • Unlike databases, relationships are first-class constructs • Like object models ontologies describe classes and attributes (properties) • Unlike object models, ontologies are set-based • Like business rules they encode rules • Unlike business rules, ontologies organize rules using axioms • Like XML schemas they are native to the web (and are in fact serialized in XML) • Unlike XML schemas, ontologies are graphs not trees and used for reasoning

16. This is an Ontology

17. These are Ontologies

18. Think Triples Subject predicate Object hasTrack Conference Session Session hasStartTime xsd: time

19. Classes are Sets Sets can have Sub-Sets Relationships are Properties Properties are expressed as “Subject-Property-Object” Triples Properties can have qualifiers The “From-End” of the Property is the Domain and the “To-End” is the Range Classes can specify restrictions on property ranges Domains, Ranges and Restrictions can be Set Expressions Class Membership is based on Properties EA Activities Capabilities CAP 1 CAP 2 CAP 3 CAP 4 Services allValuesFrom someValuesFrom hasValue minCardinality maxCardinality cardinality Semantic Technology 101

20. What can you do with OWL? • Represent and Aggregate Knowledge • Make Inferences and Discover New Knowledge • Make more informed Decisions • Supply Context-Based Information • Integrate Disparate Databases • Make Recommenders

21. Semantic technology is about putting Ontologies to work • So, what is an ontology? • It is a run time model of information • Defined using constructs for: • Concepts – classes • Relationships – properties (object and data) • Rules – axioms and constraints • Instances of concepts – individuals (data) • Semantic web ontologies are defined using W3C standards: RDF/S and OWL

22. F.I .P.D.A. • FIND: • Capability and Services Directory • Context-aware retrieval • INTERPRET: • Compliance Checker • Dependency Discoverer • Capability-Centric Communities of Practice • PREDICT: • Impact Analyzer • What-If Analyzer • DECIDE: • Tradeoff Analyzer • Signoff Coordinator • ACT: • Interest-Based Information Provider • Capability Configurator Decision Flow

23. Enterprise Architecture – a Semantic Sweet-spot

24. Federal Enterprise Architecture Performance Reference Model (PRM) • Government-wide Performance Measures & Outcomes • Line of Business-Specific Performance Measures & Outcomes Business Reference Model (BRM) • Lines of Business • Agencies, Customers, Partners Business-Driven Approach (Citizen-Centered Focus) Service Component Reference Model (SRM) Component-Based Architectures • Service Layers, Service Types • Components, Access and Delivery Channels Technical Reference Model (TRM) • Service Component Interfaces, Interoperability • Technologies, Recommendations Data Reference Model (DRM) • Business-focused data standardization • Cross-Agency Information exchanges

25. Example of a Registry:Showing DOD extensions to FEA Agency-specific extensions shown “green” Hot links to TRM areas

26. srm:accessedThrough srm: runsOn … … prm: OperationalizedMeasurementIndicator fea: Customer prm: providesValue fea: Process prm: recivesValue prm:measuredBy fea: ValuePoint prm: hasPerformance prm: Performance Using Ontologies,FEA-RMO delivers “Line of Sight” fea: Mission fea: intentOf srm: allignedWith srm: Component trm: Technology fea: Agency srm: develops fea: hasIntent fea: SubFunction fea: IT Initiative fea:undertakes brm: allignedWith rdfs:subClassOf rdfs:subPropertyOf Other relationships

27. Architecture of the Oracle RDF Data Model

28. Why Specialized Triple Stores?

29. Why Oracle Supports RDF • Oracle supports open standards and RDF and OWL became W3C standards in 2004 • Life Sciences customers requested the functionality • Semantic Web provides important advances for data integration and search • Already had graph capability with Network Data Model

30. P1 S1 O1 P2 S2 P2 O2 RDF Data Model • RDF data stored in a directed, logical network • Subjects and objects mapped to nodes • Predicates mapped to links that have subject start nodes and object end nodes • Links represent complete RDF triples • RDF Triples: • {S1, P1, O1} • {S1, P2, O2} • {S2, P2, O2}

31. RDF_MODEL$ MODEL_ID OWNER RDF_BLANK_NODE$ MODEL_NAME NODE_ID TABLE_NAME NODE_VALUE COLUMN_NAME ORIG_NAME MODEL_ID RDF Data Model RDF_LINK$ RDF_VALUE$ LINK_ID VALUE_ID START_NODE_ID VALUE_NAME END_NODE_ID VALUE_TYPE CANON_END_NODE_ID LITERAL_TYPE LANGUAGE_TYPE LINK_COST_COLUMN LONG_VALUE P_VALUE_ID MODEL_ID RDF_NODE$ NODE_ID …

32. Reification • Resource generated from unique LINK_ID to represent reified statement • Resource can then be used in subject or object

33. Containers and Collections

34. SDO_RDF_TRIPLE ( subject VARCHAR2(2000), property VARCHAR2(2000), object VARCHAR2(2000)); SDO_RDF_TRIPLE_S ( RDF_T_ID NUMBER, RDF_M_ID NUMBER, RDF_S_ID NUMBER, RDF_P_ID NUMBER, RDF_O_ID NUMBER, ... RDF Triple Implementation CREATE TABLE jobs (triple SDO_RDF_TRIPLE_S); SELECT j.triple.GET_RDF_TRIPLE() FROM jobs j;

35. Rules and Rulebases • A rule is an object that can be applied to draw inferences from RDF Data • An IF side pattern for the antecedents • An optional filter condition that further restricts the subgraphs matched by the IF side pattern • A THEN side pattern for the consequents • A rulebase is an object that contains rules. RDF and RDFS rulebases are provided

36. Rule Index • Rules index contains pre-computed triples that can be inferred from applying rulebases to models • If a query refers to a rulebase, then a rule index must exist for the rulebase-model combination • Flexible model for updating the rules index

37. RDF_MATCH • The RDF_MATCH table function allows a graph query to be embedded in a SQL query • Searches for an arbitrary pattern against the RDF data, including inferencing, based on RDF, RDFS, and user-defined rules • Automatically resolve multiple representations of the same point in value space

38. Enterprise Functionality: Scalability, High Availability Data Loads Protein data Chemistry Genome data High-speed interconnect

39. LDAP User Management •  •  Virtual Private Database Selective Encryption Single Sign-On Enterprise Functionality: Security

40. Enterprise Functionality:Performance Image Source: VLDB 2005

41. Enterprise Functionality: Performance Image Source: VLDB 2005

42. Data Integration • SQL / RDBMS • Concise, efficient transactions • Transaction metadata is embedded or implicit in the application or database schema • XQuery / XML • Transaction across organizational boundaries • XML wraps the metadata about the transaction around the data • SPARQL / RDF • Information sharing with ultimate flexibility • Enables semantics as well as syntax to be embedded in documents

43. Life Sciences Use Cases and Demos

44. Case Study 1: Identification of Clinical Trial Candidates Natural Language Rule

45. Case Study 1: Identification of Clinical Trial Candidates Oracle Rule

46. Case Study 1: Identification of Clinical Trial Candidates RDF Inference

47. Case Study 2: Bioinformatics Data Integration and Navigation

48. Case Study 2: Bioinformatics Data Integration and Navigation

49. Case Study 3: Drug Safety Determination

50. Case Study 3: Drug Safety Determination IF compound has >90% structural similarity to a failed compound AND compound binds to target with more than 5 SNPs AND therapeutic index is low AND histology indicates > 5% incidence of liver necrosis in rats AND ALT reading is > 2x above normal in phase I AND therapeutic dose is > 30 mg in phase II AND >80% of patients with Cytochrome P450 2DE report skin rash in phase III THEN consider immediately stopping trials for those patients