1. © 2008 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice Enterprise Information Integration using Semantic Web Technologies: RDF as the Lingua Franca David Booth, Ph.D. HP Software Semantic Technology Conference 20-May-2008 In collaboration with Steve Battle, HP Labs Latest version of these slides: http://dbooth.org/2008/stc/slides.ppt

2. 2 Disclaimer This work reflects research and is presented for discussion purposes only. No product commitment whatsoever is expressed or implied. Furthermore, views expressed herein are those of the author and do not necessarily reflect those of HP.

3. 3 Outline PART 0: The problem PART 1: RDF: The lingua franca for information exchange −Why 1.Focus on semantics 2.Easier data integration 3.Easier to bridge other formats/models 4.Looser coupling −How 1.RDF message semantics 2.REST-based SPARQL endpoints 3.XML with GRDDL transformations 4.Aggregators PART 2: POC: A SPARQL adaptor for UCMDB −What is UCMDB −SPARQL adaptor

4. 4 PART 0 The problem

5. 5 Problem 1: Integration complexity Multiple producers/consumers need to share data Tight coupling hampers independent versioning Provisioning Discovery Change Management Compliance Management Incident Management Release Management Monitoring Ticketing Release Managers Unix System Administrators Networking Engineers Networking Administrators Compliance Managers Windows System Administrators Operation Centers Storage Administrators Source Control

6. 6 Problem 2: Babelization Proliferation of data models (XML schemas, etc.) Parsing issues influence data models No consistent semantics Data chaos Tower of Babel, Abel Grimmer (1570-1619)

7. 7 PART 1 RDF: The lingua franca for information exchange

8. 8 Why? Four reasons...

9. 9 Why? 1. Focus on semantics XML: −Schema is focused on how to serialize Constrains more than the model −Parent/child and sibling relationships are not named Are their semantics documented? E.g., does sibling order matter? RDF: −One URI per concept −Syntax independent Who cares about syntax?

10. 10 Why? 2. Easier data integration

11. 11 Why? 2. Easier data integration Blue App has model

12. 12 Why? 2. Easier data integration Red App has model Need to integrate Red & Blue models

13. 13 Why? 2. Easier data integration Step 1: Merge RDF Same nodes (URIs) join automatically

14. 14 Why? 2. Easier data integration Step 2: Add relationships and rules (Relationships are also RDF)

15. 15 Why? 2. Easier data integration Step 3: Define Green model (Making use of Red & Blue models)

16. 16 Why? 2. Easier data integration What the Blue app sees: −No difference!

17. 17 Why? 2. Easier data integration What the Red app sees No difference!

18. 18 Why? 3. RDF helps bridge other formats/models Producers and consumers may use different formats/models Rules can specify transformations Inference engine finds path to desired result model RDF Model Transform A1 A2 A3 B1 B2 C1 C2 X Y Z Ontologies & Rules

19. 19 Why? 4. Looser coupling Without breaking consumers: −Ontologies can be mixed and extended −Triples can be added Producer & consumer can be versioned more independently

20. 20 Example of looser coupling RedCust and GreenCust ontologies added Blue app is not affected (Blue app) ConsumerProducer

21. 21 How? Four ways...

22. 22 How? 1. RDF message semantics Interface contract specifies RDF, regardless of serialization RDF pins the semantics Consumer Producer RDF

23. 23 How? 2. REST-based SPARQL endpoints Consumer Producer SPARQL RDF HTTP

24. 24 REST-based SPARQL endpoints Why REST: −HTTP is ubiquitous −Simpler than SOAP-based Web services (WS*) −Looser process coupling

25. 25 REST-based SPARQL endpoints Why SPARQL: −One endpoint supports multiple data needs Each consumer gets what it wants −Insulates consumers from internal model changes Inferencing transforms data to consumer's desired model Looser data coupling

26. 26 How? 3. XML with GRDDL transformations GRDDL is a W3C standard GRDDL permits RDF to be "gleaned" from XML −XML document or schema specifies desired GRDDL transformation −GRDDL transformation produces RDF from XML document −Mostly intended for getting microformat and other data/metadata from HTML pages

27. 27 Using GRDDL for XML document semantics Each XML format can be viewed as a custom serialization of RDF! −GRDDL transformation produces semantics of the XML document Helps bridge XML and RDF worlds Same XML document can be consumed by: −Legacy XML app −RDF app App interface contract can specify RDF −Serializations can vary −Semantics are pinned by RDF

28. 28 Using GRDDL for XML document semantics See: http://dbooth.org/2007/rdf-and-soa/rdf-and-soa-paper.htm Client Normalize to RDF Serialize as XML/other/RDF RDF Engine / Store Service Core App Processing

29. 29 How? 4. Aggregators Gets data from multiple sources Provides data to consumers Aggregator A1 A2 A3 B1 B2 C1 C2 X Y Z Ontologies & Rules SPARQL

30. 30 Aggregator Conceptual component −Not necessarily a separate physical service Handles mechanics of getting data −Different adaptors for different sources REST, WS*, Relational, XML, etc. Diverse data models −Might do caching and query distribution (federation) Provides model transformation −Plug in ontologies and inference rules as needed

31. 31 PART 2 Proof-of-Concept: A SPARQL adaptor for UCMDB

32. 32 IT Service Management (ITSM) Manage IT environment Configuration Management Data Base (CMDB) is central

33. 33 The HP Universal CMDB (UCMDB) CMDB : Configuration Management DB Goal: Maintain a comprehensive and current record of all configuration items (CIs) and their relationships

34. 34 Example: host information Host properties One particular host machine http://cmdb.mercury.com#nt.35014541

35. 35 SPARQL adaptor Uses existing SOAP interface to UCMDB Enables SPARQL queries Results can be RDF No model transformation (yet) SOAP interface SPARQL adaptor HP UCMDB

36. 36 Architecture of SPARQL adaptor SOAP interface SPARQL adaptor HP UCMDB SPARQL compile TQL XML RDF lift Database CMDB metadata export submit OWL Design Time Run Time

37. 37 UCMDB ontology The HP UCMDB ontology defines CI types and relationship hierarchies. Derived automatically from HP UCMDB metadata.

38. 38 Jena based implementation Jena, ARQ, Joseki developed at HP Labs*. Jena : Semantic Web toolkit RDF, OWL, inference ARQ : Query Engine SPARQL query algebra, evaluation Joseki : SPARQL server SPARQL protocol * http://www.hpl.hp.com/semweb/

39. 39 Query returning a table host_name "ILDTRD129" ^^ "JONI" ^^ "MBADIR-IL" ^^ Select the names of host servers on the network with addresses from 192.168.81.0 SELECT ?host_name WHERE { [ a object:network ] attr:network_netaddr "192.168.81.0" ; link:member [ a object:host ; attr:host_dnsname ?host_name ] }

40. 40 Query returning an RDF subgraph Describe a network (192.168.81.0) with host servers containing a DB.

41. 41 Example RDF result set Database Host

42. 42 Outline PART 0: The problem PART 1: RDF: The lingua franca for information exchange −Why 1.Focus on semantics 2.Easier data integration 3.Easier to bridge other formats/models 4.Looser coupling −How 1.RDF message semantics 2.REST-based SPARQL endpoints 3.XML with GRDDL transformations 4.Aggregators PART 2: POC: A SPARQL adaptor for UCMDB −What is UCMDB −SPARQL adaptor

43. © 2008 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice Questions?