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Enterprise Solutions for the Semantic Web Ralph Hodgson, TopQuadrant Susie Stephens, Oracle SICOP 4 th Semantic Interoperability for e-Government Conference,

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Presentation on theme: "Enterprise Solutions for the Semantic Web Ralph Hodgson, TopQuadrant Susie Stephens, Oracle SICOP 4 th Semantic Interoperability for e-Government Conference,"— Presentation transcript:

1 Enterprise Solutions for the Semantic Web Ralph Hodgson, TopQuadrant Susie Stephens, Oracle SICOP 4 th Semantic Interoperability for e-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 Us is

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

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

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

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

10 The Semantic Stack

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

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

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

14 Semantic Data Integrator: Consulting Services Company Data Quality 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. Semantic Data Integrator: FAA Air Passenger Threat Analyzer 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. Early Adopters: A Quick Look at 7 Capability Cases Semantic Content Registry: European Environment Agency ReportNet 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. Rights Mediator: RightsCom Policy Engine Using OWL and semantic technology from Network Inference, RightCom has built an integrated solution for rights management in the media and entertainment industry. Product Design Assistant: Semantic Testcar Configurator 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. Concept-based Search: Siemens Self-Service for Industrial Equipment Simatic is a self-service WEB application for Siemens Industrial Control Products. The system uses a model-based CBR engine called Orenge from Empolis. Expert Locator: Boeing’s Expert Locator 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.

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 Conference Session hasTrack Session xsd: time hasStartTime SubjectObjectpredicate

19 Semantic Technology 101 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 Services CAP 1 CAP 2 CA P 4 CAP 3 allValuesFrom someValuesFrom hasValue minCardinality maxCardinality cardinality

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 Business Reference Model (BRM) Lines of Business Agencies, Customers, Partners Service Component Reference Model (SRM) 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 Performance Reference Model (PRM) Government-wide Performance Measures & Outcomes Line of Business-Specific Performance Measures & Outcomes Business-Driven Approach (Citizen-Centered Focus) Component-Based Architectures

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

26 Using Ontologies, FEA-RMO delivers “Line of Sight” fea: Mission fea: intentOf fea: Agency fea:undertakes fea: SubFunction fea: hasIntent brm: allignedWith fea: IT Initiative srm: develops trm: Technology fea: ValuePoint srm: Component srm: allignedWith prm: providesValue prm: recivesValue prm: hasPerformance prm: Performance prm:measuredBy prm: OperationalizedMeasurement Indicator srm:accessedThrough srm: runsOn ………… rdfs:subClassOf rdfs:subPropertyOf fea: Customer fea: Process 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 RDF Data Model RDF Triples: {S 1, P 1, O 1 } {S 1, P 2, O 2 } {S 2, P 2, O 2 } S1S1 O1O1 O2O2 S2S2 P2P2 P2P2 P1P1 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

31 RDF Data Model RDF_VALUE$ VALUE_NAME VALUE_ID VALUE_TYPE LITERAL_TYPE LANGUAGE_TYPE LONG_VALUE … NODE_ID RDF_NODE$ NODE_VALUE NODE_ID ORIG_NAME MODEL_ID RDF_BLANK_NODE$ LINK_ID END_NODE_ID RDF_LINK$ START_NODE_ID LINK_COST_COLUMN P_VALUE_ID MODEL_ID RDF_MODEL$ OWNER MODEL_ID MODEL_NAME TABLE_NAME COLUMN_NAME CANON_END_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 RDF Triple Implementation 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,... 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 High-speed interconnect Data LoadsGenome dataProtein dataChemistry

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

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

49 Case Study 3: Drug Safety Determination

50 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

51 Demos BioDASH Family Tree

52 SAPPHIRE Project Systematic and Continuous Collection, Analysis, Interpretation, and Dissemination of Diagnostic and Pre-Diagnostic Data for use in Timely and Sensitive Detection of Public health Incidents (Bioterrorism or Natural) to Reduce Morbidity and Mortality by Better Response Planning and Coordination. The Center for Biosecurity and Public Health Informatics Research Situation-Aware Prevention of Public Heath Incidents using Reasoning Engines University of Texas, Houston

53 SAPPHIRE Capability Cases The Center for Biosecurity and Public Health Informatics Research

54 SAPPHIRE Ontology Architecture The Center for Biosecurity and Public Health Informatics Research

55 SAPPHIRE Tools The Center for Biosecurity and Public Health Informatics Research

56 Best Practices for Ontology Engineering

57 Technology Adoption of Ontology Engineering Modeling Guidance Methodology for Ontology Engineering Techniques Ontology Modeling Patterns Tools

58 Ontology Modeling Guidance: Techniques (in frequently-asked order) How to build an Ontology How to develop an Ontology Architecture How to integrate Databases Federated Search Concept-Based Search How to annotate information resources Entity and Concept Extraction Techniques Working with XML Schemas Working with UML How to use an Upper Ontology

59 Ontology Modeling Methodology IBM Uschold & King Methontology Solution Envisioning Checkland’s Soft Systems Methodology ODM CommonKADS Boundary Objects - Coordination & Negotiation of Meaning in Organizations TOVE TopSAIL™ MOKA LIBRA Grunniger & Fox Ontology Architecture, Ontology Patterns, Knowledge Maps + Agile Methods Competency Questions Stakeholder Analysis, CV, Boundary Criteria, Context as Settings Stakeholders Forces, Barriers, Challenges, Results, Capability Cases & Capability Architecture Problem Modeling, CATWOE Boundary Object re-contextualization Technique Agent Model, Task Model, Roles Insights on Software Reuse Workproduct Concept Modeling Patterns

60 An Ontology Architecture is crucial: Some dependencies in the EA Ontologies EA CAPCASE DC ECM Industry TQEC Enterprise Architecture Capability Cases Enterprise Capability Enterprise Core Ontology Dublin Core TQC HR Enterprise Process ORGS Standards EPM Time Technology TopQuadrant Core Ontology Product

61 Ontology Architecture Requirements Specification (OARS) Ontology of Ontologies Stakeholders Systems Competency Questions Capability Questions Architecture Dependencies Ontology Reuse

62 Ontology Modeling: Top 10 Guidelines Standardize: modelling patterns, concept and property names and namespaces - provide human-readable names with rdfs:label Keep ontologies small and modular - evolve an ontology architecture Assimilate enterprise knowledge, for example, internal lists, vocabularies, taxonomies. Be clear on the role of each ontology: specification versus knowledge discovery Agile re-factoring using ontology re-factoring patterns Use domain and range with care Analyze  Synthesize  Evaluate: Iterate with stakeholders using blueprints. Validate models using competency questions Test often using sample data Be careful with open and closed world reasoning when using restrictions and avoid the ‘allValuesFrom’ restriction with ‘Equivalent Classes’ Model for reuse – separate instances from classes

63 Ontology Modeling Guidance: Patterns Trust My Classifier n-ary relations Class Bridge Class-Instance Mirror Transitive Parent Property Abstraction

64 Ontology Design Pattern: Model Bridge - Example Lodging Hotel B&B Travel Services Hotel Airline US travel model British travel model subClassOf

65 Tooling Protégé SWOOP Semantic Works TopBraid Studio

66 TopBraid Studio – 1 Class Tree List of ontologies used in each project, user can switch between them Properties

67 TopBraid Studio – 2 Form Editor Tracking changes

68 TopBraid Studio - 3 Switching the view from a Form to RDF Source Instance Window Multiple serializations are available Edits can be made directly

69 TopBraid Studio - 4

70 Wrap Up

71 Further Information - Oracle _technologieshttp://www.oracle.com/technology/tech/semantic _technologies sciences/index.htmlhttp://www.oracle.com/technology/industries/life_ sciences/index.html

72 Further Information - TopQuadrant Irene Polikoff and Robert Coyne, “Towards Executable Enterprise Models: Ontology and Semantic Web Meet Enterprise Architecture”, Journal of Enterprise Architecture, Fawcette Publications, August 2005 Dean Allemang, Irene Polikoff, Ralph Hodgson, “Enterprise Architecture Reference Modeling in OWL/RDF”, ISWC, International Semantic Web Conference, Ireland, 2005 TopQuadrant White Paper on FEA-RMO, 2/21/2005 FEA Ontology Models FEA - BRM2PRM - PRM - BRM - SRM - TRM - Merged Ontology - Ontologyhttp://www.osera.gov/owl/2004/11/fea/feac.owl

73 Books on Semantic Technology - 1 Dieter Fensel, Wolfgang Wahlster, Henry Lieberman, James Hendler (Eds.): “Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential”, MIT Press, 2002 John Davies, Dieter Fensel & Frank van Harmelen:, “Towards the Semantic WEB – Ontology Driven Knowledge Management”, John Wiley, 2002 Johan Hjelm, “Creating the Semantic Web with RDF”, John Wiley, 2001 Dieter Fensel: “Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce”, Springer Verlag, 2001 Sheller Powers, “Practical RDF”, O’Reilly, 2003 Michael C. Daconta, Leo J. Obrst, Kevin T. Smith: “The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management”, John Wiley, 2003 Vladimir Geroimenko (Editor), Chaomei Chen (Editor), “Visualizing the Semantic Web”, Springer-Verlag, 2003 M. Klein and B. Omelayenko (eds.), “Knowledge Transformation for the Semantic Web”, Vol. 95, Frontiers in Artificial Intelligence and Applications, IOS Press, 2003Frontiers in Artificial Intelligence and Applications

74 Books on Semantic Technology - 2 Thomas B. Passin, "Explorer's Guide to the Semantic Web", ISBN , June 2004 Jeff Pollock and Ralph Hodgson, "Adaptive Information: Improving Business Through Semantic Interoperability, Grid Computing, and Enterprise Integration“, John Wiley, September 2004 Grigoris Antoniou and Frank van Harmelen, “A Semantic Web Primer”, The MIT Press, April 2004 Lee W. Lacy, “OWL: Representing Information Using the Web Ontology Language”, Trafford Publishing, 2005 Munindar P. Singh, Michael N. Huhns, “Service-Oriented Computing : Semantics, Processes, Agents”, John Wiley & Sons, 2005 Irene Polikoff et al, ”Capability Cases – A Solution Envisioning Approach”, Addison-Wesley, 2005

75

76 Integration and Aggregation of Data Image Source: BioDASH

77 Integration and Aggregation of Data Image Source: BioDASHhttp://www.w3.org/2005/4/swls/BioDash/Demo


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