building skynet for science: discovering new frontiers using embedded knowledge

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Building SkyNet for Science Discovering New Frontiers Using Embedded Knowledge Richard Akerman NISO Discovery Tools Forum March 27, 2008

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Discovery in the digital environment is primarily mediated by machines. Unfortunately, the machines don't speak our language. Therefore, we must find standard ways of representing and communicating our requests, and standards for embedding and exchanging knowledge about digital objects. With the rise of the machines, we need to consider what information encodings will allow them to most efficiently process and analyze the vast range of information that is available. We need to find ways to communicate human recommendations and preferences, and to enable people to successfully explore the new digital frontier.

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Page 1: Building SkyNet for Science: Discovering New Frontiers Using Embedded Knowledge

BuildingSkyNet for Science

Discovering New FrontiersUsing Embedded Knowledge

Richard AkermanNISO Discovery Tools Forum

March 27, 2008

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Stanley

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How can we better serve the machines?

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The machines don’t speak our language

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We must become knowledge translators

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To Serve Machine

• Produce information in formats that machines can understand, in parallel with formats that are human readable

• Every web resource its machine reader

• Have a limited number of formats, keep them simple, and enable easy interchange of information

• Save the time of the machine

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Bibliographic Metadata as a First Class Citizen

• OpenURL (ANSI/NISO Z39.88 - 2004)

• COinS

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Unique Identifiers

• authors

• institutions

• text content

• data

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To Serve Human

• Delicious Library

• LibraryThing

• Machines can process and analyze information, but only humans can use and savour information (for now...)

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The Social Life of Humans

• Formal categorization

• Reviews

• Ratings

• Connections / Relatedness

• Informal categorization (tags, folksonomies)

• Use (frequency, time...)

• Groups (colleagues, friends, work groups...)

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The Social Life of Machines

• Feature extraction

• Similarity (count-based, vector-based)

• Impact factor / PageRank

• Context (location, others)

• Numbers numbers numbers

• Machines love unique identifiers

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Use Case

• Find me the best relevant information

• Without me asking for it?

• Wherever and whenever?

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Every Book Its Reader

• The WebOPAC is not a discovery interface

• Build a discovery layer over the catalogue metadata

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Open Data

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There is more to heaven and earth

• Licensed content and access

• Organization content

• The entire biblioverse and Internet

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Is there “too much” information?

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There is too much information poverty

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Seeing the forest - licensed content

• Federated search

• Local indexing

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I see... everything

• XML, RDF, RSS, GeoRSS...

• Microformats - Embedded knowledge

• Aggregators

• Recommender APIs

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Glen Newton

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Free the Humans!

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Richard AkermanNRC-CISTI

http://www.connotea.org/user/scilib/tag/nisodiscovery2008

© 2008 Government of CanadaLicensed in the Creative Commons

http://creativecommons.org/licenses/by-nc-sa/2.5/ca/