ALOA: A Web Services Driven Framework for Automatic Learning Object Annotation

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Presentation at ECTEL 2008

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  • 1. Mohamed Amine Chatti, Nanda Firdausi Muhammad, Matthias Jarke RWTH Aachen University, Germany ECTEL 2008 Maastricht, The Netherlands September 19, 2008 ALOA – A Web Services Driven Framework for A utomatic L earning O bject A nnotation
  • 2. Agenda
    • Why Automatic Metadata Generation?
    • AMG v.1
    • AMG v.2 – SAmgI
    • ALOA
    • ALOA and AMG
    • Conclusion and Future Work
  • 3. Metadata
    • Metadata is crucial for search, access, share, and reuse.
    • Dealing with metadata cannot be a human task (Duval and Hodgins, 2004)
      • Complex metadata standards (e.g. 9 LOM categories and 45 records of LOM level two)
      • Benefit not immediately appreciated
      • Metadata creators too expensive to be employed
      • Tools not user friendly (“electronic forms must die”)
      • Need for Automatic Metadata Generation
  • 4. Automatic Approach
    • Use information about the LO and its context to extract or generate its metadata.
    • 4 aspects of AMG (Cardinaels et al., 2005)
      • Content analysis (LO itself, e.g. keyword, language)
      • Context analysis (environment the LO is stored or used in, e.g. LMS)
      • Usage analysis (e.g. time spent reading a doc)
      • Structure analysis (relationship amongst LOs)
  • 5. AMG v.1
    • AMG at KUL (Cardinaels et al., 2005; Ochoa et al., 2005)
  • 6. AMG v.1 Limitations
    • It was an application (Java-based)
    • No support for different languages
    • Not possible to have a metadata subset as a result
    • Not flexible and extensible
    • Not really interoperable between platforms
  • 7. AMG v.2
    • Federated AMG
    • Simple AMG Interface (SAmgI) (Meire et al., 2007)
    • Main Design Goals:
      • Extensibility – Pluggability
      • Interoperability (Service oriented)
  • 8. AMG v.2 Extensibility
    • ObjectBasedGenerators based on the Factory design pattern
    • Problem: checkout source code, recompile and rebuild the whole application
  • 9. AMG v.2 Interoperability
    • Federated AMG Engine - SAmgI installations / service endpoints
    • Problem: some programming required (SAmgI WSDL specification, XML schemas, etc.)
  • 10. ALOA
    • A Framework for LOM-based Automatic LO Annotation
    • Service Oriented Architecture (SOA) / Web Services
    • Main focus on flexibility and extensibility
  • 11. ALOA Core Engine
    • Indexer performing these actions:
      • read all configurations in the properties file (i.e. available extractors and generators, priority of each generator, maximum generated values)
      • access the LO as an array of bytes
      • detect the mime type of the LO
      • look for the available extractor for this particular mime type
      • extract the content and the embedded properties of the LO
      • contact the available generators
      • solve conflicts
      • translate the generated metadata into the required languages
      • return the generation result to the Web Service stub
    • ConflictResolver
      • considers priorities of the generators
    • Translator
      • uses Google Translate as its translation service
  • 12. ALOA Components
    • Extractors
      • extract content information and embedded properties from LOs
      • only one extractor for each LO mime type
      • html extractor (Jericho library)
      • pdf extractor (pdfBox library)
      • word extractor (Apache POI library)
      • ppt extractor (Apache POI library)
    • Generators
      • use the output of the extractors to generate one or parts of the metadata
      • text/data mining libraries (e.g. Yahoo! Term Extraction, Tagthe, Topicalizer, LingPipe, Balie, Classifier4J)
  • 13. ALOA User Interface
    • Based on the ALOA Web Services API
    • Automatically generate metadata from online LOs (html, plain text, word, ppt, pdf)
    • Parameters
      • URL location of the LO
      • Target metadata languages (English, German, Arabic, French, Spanish, Korean)
      • Subset of the generated metadata
      • Output format (LOM XML, HTML, LOM Editor)
  • 14. ALOA Configuration Management Interface
    • Enables to easily plug-in new components (extractors and generators), for instance:
      • Extractor for multimedia LO (e.g. audio, video, image, flash)
      • Generator for a specific context (e.g. LMS)
    • The components can be deployed on different machines or on different application servers
    • Once deployed, a component can be plugged into ALOA by just giving the address of the component service
    • ALOA core engine validates and adds it to the component list in the properties file
    • Dynamic addition in run time; no need to recompile and rebuild the system
    • ALOA CMI also enables to manage the priorities of the generators and to define the maximum generated values (used by ALOA core engine)
  • 15. ALOA and AMG
    • ALOA adopts a slightly modified version of SAmgI WSDL specification
    • New methods: getLanguages , setLanguages
    • Modified method: getMetadata
    • Web Services-based interactions between ALOA and AMG possible
    • ALOA as a new SAmgI installation used by the federated AMG engine
    • AMG as a new component (i.e. extractor or generator) of ALOA
  • 16. Conclusion
    • ALOA – A framework for LOM-based automatic metadata generation
    • ALOA already implements different components (i.e. extractors and generators)
    • ALOA already generates LOM from different types of LOs (html, plain text, pdf, ppt, word)
    • Primary focus on flexibility and extensibility of the framework
    • SOA-based architecture enabling new components to be easily plugged into the basic system
    • ALOA provides a public Web Services API for third party applications
  • 17. Future Work
    • Interactions between ALOA and AMG
    • Extension with more extractors and generators based on other text/data mining techniques
    • Look at model transformation techniques to support other metadata schemas (e.g. DC, MPEG)
    • Further research of the quality of automatically generated metadata
    • Combination of automatic metadata generation with a bottom up approach (e.g. Web 2.0 social tagging)
  • 18.