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XXIX Charleston Semantic Web (5 Nov 2009) Hulbert

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Semantic Web Panel with presentations from Collexis, AIP and Silverchair.

Semantic Web Panel with presentations from Collexis, AIP and Silverchair.

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  • 1. The Semantic Web: what you need to know and why it’s important for your user community Terry Hulbert Director of Business Development American Institute of Physics XXIX Charleston Conference Charleston, SC 5 November 2009
  • 2. Contents
    • Why is semantic technology a hot topic?
    • What can we do and what are some doing?
    • Some examples at AIP
  • 3. Why?
    • 98% of STM researchers prefer & use online journals (Hemminger et al, 2007)
    • No. of articles read per researcher was 30% higher in ‘06 than ’96 (Tenopir & King, 2008)
    • Reading time per article is falling, from 32 minutes in ‘96, to 24 in ‘06 (Tenopir & King, 2008)
  • 4. Why?
    • Researchers are actually trying to avoid reading as much as possible during “discovery phase”, preferring instead to evaluate article “components”…indexing terms, related content, citations, and other “indicators of value” to isolate articles worth their “real” reading time.
  • 5. Why?
    • “ Scientists skim journal articles to discover valuable information. They scan for terminology, segments, diagrams, and summaries of particular interest. But they don’t read individual articles left-to-right, top-to-bottom”
    • Allen H. Renear and Carole L. Palmer, Science 325 (5942), 828 (14 August 2009) doi: 10.1126/science.1157784
  • 6. Online reading
    • “ Reading” an online journal approximates a bookstore visit – a place to skim, flit, discover, fritter, twitter, connect, talk, be surprised, and socialize – but most of all, not to read too deeply during initial search and discovery mode .
  • 7. Strategic reading; power browsing
    • Preference for atomised/deconstructed article “nuggets” that reveal the essence of the science within.
    • Trend toward scanning for critical ‘cues & clues’, ‘hooks & hints’ to “flash determine” the value of an article – is it worthy of a closer, deeper read?”
    • Scanning for accurate signposts to the literature, not the literature… not yet (“close reading” reserved for articles of interest found along the way)
  • 8. How to help strategic reading
    • Faceting
      • Authors, topics, popularity,…
    • Data visualisation
      • Citation density, author connections, geo viz
    • Related linking, ‘More Like This’
    • “ Bite”-serving
      • Article extracts, snippets, image thumbnails, video/audio, trending
  • 9. How are publishers doing this?
    • Reengineering production workflows
    • Implementation of semantic engine(s) & tools
      • e.g. Temis, Silverchair, Access Innovations, Collexis
      • automatic categorisation
      • intelligent association
      • taxonomy management
    • Enhanced XML repository (MarkLogic)
    • Content enrichment, text mining, entity & fact extraction, etc.
  • 10. Content enrichment
    • Highlight the meaning of content
    • Enhances granularity
      • e.g. book, chapter, image, topics, etc.
    • Taxonomy is key for normalisation
      • e.g. PACS, MeSH, MSCs
  • 11. What is the output?
    • Entities
      • products, people, places, technologies, organisations, companies
      • infer facts related to relationships, functions,…
    • Richer, semantic content linking and tagging
      • easier navigation
      • better discoverability
  • 12. What will this enable?
    • Search precision
      • reveal hard-to-find objects and information
    • New products
      • e.g. image/video/animation/multimedia collections
    • Microproducts; mash-ups
      • VJs, learning objects, etc.
    • Profiling; social networking; personalisation
  • 13. AIP examples
    • AIP UniPHY
      • social networking site for physical sciences
      • some obvious uses of granularity
      • plus ‘scientific context”
  • 14.  
  • 15.  
  • 16.  
  • 17.  
  • 18. AIP examples
    • Geotagging
      • research by geo area
      • geophysics – seismic activity plotted
        • or plot an area and highlight research published
    • Tag products
      • e.g. Review of Scientific Instruments
  • 19.  
  • 20. Other examples - chemistry
    • Royal Society of Chemistry
      • ChemSpider (acquired May 2009)
      • aggregating and indexing chemical structures and associated information
      • literature, chemical vendor catalogs, molecular properties, environmental data, toxicity data, analytical data, etc.
    • Search by chemical reactions, solvents, reactants – all extracted from documents
  • 21. Summary
    • Rapid review of:
      • why semantic tagging, semantic technologies and the semantic Web is becoming increasingly important
      • top level view of what’s possible
      • some examples
      • more detailed knowledge & expertise from my colleagues
  • 22. Thank you. Terry Hulbert Director of Business Development XXIX Charleston Conference Charleston, SC 5 November 2009 [email_address] www.twitter.com/thulbert www.friendfeed.com/tcjh007 www.delicious.com/tcjh007