David Booth, Ph. D. Hp software Semantic Technology Conference 20-May-2008



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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


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.



Outline

  • PART 0: The problem

  • PART 1: RDF: The lingua franca for information exchange

    • Why
      • Focus on semantics
      • Easier data integration
      • Easier to bridge other formats/models
      • Looser coupling
    • How
      • RDF message semantics
      • REST-based SPARQL endpoints
      • XML with GRDDL transformations
      • Aggregators
  • PART 2: POC: A SPARQL adaptor for UCMDB

    • What is UCMDB
    • SPARQL adaptor


PART 0 The problem



Problem 1: Integration complexity

  • Multiple producers/consumers need to share data

  • Tight coupling hampers independent versioning



Problem 2: Babelization

  • Proliferation of data models (XML schemas, etc.)

  • Parsing issues influence data models

  • No consistent semantics

  • Data chaos



PART 1 RDF: The lingua franca for information exchange



Why?

  • Four reasons . . .



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:

  • Who cares about syntax?



Why? 2. Easier data integration



Why? 2. Easier data integration

  • Blue App has model



Why? 2. Easier data integration

  • Red App has model

  • Need to integrate Red & Blue models



Why? 2. Easier data integration

  • Step 1: Merge RDF

  • Same nodes (URIs) join automatically



Why? 2. Easier data integration



Why? 2. Easier data integration

  • Step 3: Define Green model

  • (Making use of Red & Blue models)



Why? 2. Easier data integration

  • What the Blue app sees:

    • No difference!


Why? 2. Easier data integration

  • What the Red app sees

  • No difference!



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



Why? 4. Looser coupling

  • Without breaking consumers:

    • Ontologies can be mixed and extended
    • Triples can be added
  • Producer & consumer can be versioned more independently



Example of looser coupling

  • RedCust and GreenCust ontologies added

  • Blue app is not affected



How?

  • Four ways . . .



How? 1. RDF message semantics

  • Interface contract specifies RDF, regardless of serialization

  • RDF pins the semantics



How? 2. REST-based SPARQL endpoints



REST-based SPARQL endpoints

  • Why REST:

    • HTTP is ubiquitous
    • Simpler than SOAP-based Web services (WS*)
    • Looser process coupling


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


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


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


Using GRDDL for XML document semantics

  • See: http://dbooth.org/2007/rdf-and-soa/rdf-and-soa-paper.htm



How? 4. Aggregators

  • Gets data from multiple sources

  • Provides data to consumers



Aggregator

  • Conceptual component

  • 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


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



IT Service Management (ITSM)

  • Manage IT environment

  • Configuration Management Data Base (CMDB) is central



The HP Universal CMDB (UCMDB)

  • Goal:

  • Maintain a comprehensive and current record of all configuration items (CIs) and their relationships



Example: host information



SPARQL adaptor



Architecture of SPARQL adaptor



UCMDB ontology

  • The HP UCMDB ontology defines CI types and relationship hierarchies.

  • Derived automatically from HP UCMDB metadata.



Jena based implementation

  • Jena, ARQ, Joseki developed at HP Labs*.



Query returning a table



Query returning an RDF subgraph



Example RDF result set



Outline

  • PART 0: The problem

  • PART 1: RDF: The lingua franca for information exchange

    • Why
      • Focus on semantics
      • Easier data integration
      • Easier to bridge other formats/models
      • Looser coupling
    • How
      • RDF message semantics
      • REST-based SPARQL endpoints
      • XML with GRDDL transformations
      • Aggregators
  • PART 2: POC: A SPARQL adaptor for UCMDB

    • What is UCMDB
    • SPARQL adaptor




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