Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Automatically indexing science usingnatural-language processing, RDF and
SPARQL
Andrew Walkingshaw, Nick Day, Peter Corbett, JimDowning, Joe Townsend, Peter Murray-Rust
February 16, 2008
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Data sources
• Supplemental and experimental data
• Journals
• Self-archived papers (e.g. arXiv)
• Mainstream journalism
• Blogs
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Data sources
• Supplemental and experimental data
• Journals
• Self-archived papers (e.g. arXiv)
• Mainstream journalism
• Blogs
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Data sources
• Supplemental and experimental data
• Journals
• Self-archived papers (e.g. arXiv)
• Mainstream journalism
• Blogs
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Data sources
• Supplemental and experimental data
• Journals
• Self-archived papers (e.g. arXiv)
• Mainstream journalism
• Blogs
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Data sources
• Supplemental and experimental data
• Journals
• Self-archived papers (e.g. arXiv)
• Mainstream journalism
• Blogs
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Supplemental data: CrystalEye
• http://wwmm.ch.cam.ac.uk/crystaleye/
• Repository for crystallographic data
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Supplemental data: CrystalEye
• http://wwmm.ch.cam.ac.uk/crystaleye/
• Repository for crystallographic data
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Journals and arXiv
• “Traditional” journal articles
• Titles and abstracts. . .
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Journals and arXiv
• “Traditional” journal articles
• Titles and abstracts. . .
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Journalism and blogs
• Unstructured text with little semantics;
• . . . hence Google Scholar, Web of Science, etc.
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Journalism and blogs
• Unstructured text with little semantics;
• . . . hence Google Scholar, Web of Science, etc.
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Semi-structured data: Golem
• We’ve got a lot of chemical data as CML
• http://en.wikipedia.org/wiki/Chemical Markup Language
• . . . but we still need to get data out of that and into amore useful form
• hence Golem: http://www.lexical.org.uk/science/golem/
• GRDDLish strategy for extracting data from CML files:identify dialect-specific concepts with XPath expressionsand XSLT stylesheets
• upshot: we can extract JSON objects from CML files.
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Semi-structured data: Golem
• We’ve got a lot of chemical data as CML
• http://en.wikipedia.org/wiki/Chemical Markup Language
• . . . but we still need to get data out of that and into amore useful form
• hence Golem: http://www.lexical.org.uk/science/golem/
• GRDDLish strategy for extracting data from CML files:identify dialect-specific concepts with XPath expressionsand XSLT stylesheets
• upshot: we can extract JSON objects from CML files.
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Semi-structured data: Golem
• We’ve got a lot of chemical data as CML
• http://en.wikipedia.org/wiki/Chemical Markup Language
• . . . but we still need to get data out of that and into amore useful form
• hence Golem: http://www.lexical.org.uk/science/golem/
• GRDDLish strategy for extracting data from CML files:identify dialect-specific concepts with XPath expressionsand XSLT stylesheets
• upshot: we can extract JSON objects from CML files.
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Semi-structured data: Golem
• We’ve got a lot of chemical data as CML
• http://en.wikipedia.org/wiki/Chemical Markup Language
• . . . but we still need to get data out of that and into amore useful form
• hence Golem: http://www.lexical.org.uk/science/golem/
• GRDDLish strategy for extracting data from CML files:identify dialect-specific concepts with XPath expressionsand XSLT stylesheets
• upshot: we can extract JSON objects from CML files.
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Semi-structured data: Golem
• We’ve got a lot of chemical data as CML
• http://en.wikipedia.org/wiki/Chemical Markup Language
• . . . but we still need to get data out of that and into amore useful form
• hence Golem: http://www.lexical.org.uk/science/golem/
• GRDDLish strategy for extracting data from CML files:identify dialect-specific concepts with XPath expressionsand XSLT stylesheets
• upshot: we can extract JSON objects from CML files.
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Semi-structured data: Golem
• We’ve got a lot of chemical data as CML
• http://en.wikipedia.org/wiki/Chemical Markup Language
• . . . but we still need to get data out of that and into amore useful form
• hence Golem: http://www.lexical.org.uk/science/golem/
• GRDDLish strategy for extracting data from CML files:identify dialect-specific concepts with XPath expressionsand XSLT stylesheets
• upshot: we can extract JSON objects from CML files.
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Free text: OSCAR3
• http://oscar3-chem.sourceforge.net/
• Natural-language parser for documents about chemistry
• Dark magic: don’t ask me how it works!
• . . . but it can be run as a Jetty webservice so as long as itdoes, I’m happy
• Author’s blog:http://wwmm.ch.cam.ac.uk/blogs/corbett/
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Free text: OSCAR3
• http://oscar3-chem.sourceforge.net/
• Natural-language parser for documents about chemistry
• Dark magic: don’t ask me how it works!
• . . . but it can be run as a Jetty webservice so as long as itdoes, I’m happy
• Author’s blog:http://wwmm.ch.cam.ac.uk/blogs/corbett/
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Free text: OSCAR3
• http://oscar3-chem.sourceforge.net/
• Natural-language parser for documents about chemistry
• Dark magic: don’t ask me how it works!
• . . . but it can be run as a Jetty webservice so as long as itdoes, I’m happy
• Author’s blog:http://wwmm.ch.cam.ac.uk/blogs/corbett/
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Free text: OSCAR3
• http://oscar3-chem.sourceforge.net/
• Natural-language parser for documents about chemistry
• Dark magic: don’t ask me how it works!
• . . . but it can be run as a Jetty webservice so as long as itdoes, I’m happy
• Author’s blog:http://wwmm.ch.cam.ac.uk/blogs/corbett/
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Free text: OSCAR3
• http://oscar3-chem.sourceforge.net/
• Natural-language parser for documents about chemistry
• Dark magic: don’t ask me how it works!
• . . . but it can be run as a Jetty webservice so as long as itdoes, I’m happy
• Author’s blog:http://wwmm.ch.cam.ac.uk/blogs/corbett/
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Getting the data in
• Everything (more or less) talks RSS nowadays. . .
• RSS 0.91, RSS 1.0 (which one?), Atom, etc etc etc.
• Thankfully: feedparser (http://feedparser.org/)
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Getting the data in
• Everything (more or less) talks RSS nowadays. . .
• RSS 0.91, RSS 1.0 (which one?), Atom, etc etc etc.
• Thankfully: feedparser (http://feedparser.org/)
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Getting the data in
• Everything (more or less) talks RSS nowadays. . .
• RSS 0.91, RSS 1.0 (which one?), Atom, etc etc etc.
• Thankfully: feedparser (http://feedparser.org/)
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Serializing metadata
• RDF – using:
• Dublin Core terms
• A homebrew ontology based on the IUCr’s CIF data format
• and another homebrew ontology for OSCAR annotations
• (it’d be good to standardise these, but to be honest, notmany people are doing this sort of thing)
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Serializing metadata
• RDF – using:
• Dublin Core terms
• A homebrew ontology based on the IUCr’s CIF data format
• and another homebrew ontology for OSCAR annotations
• (it’d be good to standardise these, but to be honest, notmany people are doing this sort of thing)
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Serializing metadata
• RDF – using:
• Dublin Core terms
• A homebrew ontology based on the IUCr’s CIF data format
• and another homebrew ontology for OSCAR annotations
• (it’d be good to standardise these, but to be honest, notmany people are doing this sort of thing)
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Serializing metadata
• RDF – using:
• Dublin Core terms
• A homebrew ontology based on the IUCr’s CIF data format
• and another homebrew ontology for OSCAR annotations
• (it’d be good to standardise these, but to be honest, notmany people are doing this sort of thing)
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Serializing metadata
• RDF – using:
• Dublin Core terms
• A homebrew ontology based on the IUCr’s CIF data format
• and another homebrew ontology for OSCAR annotations
• (it’d be good to standardise these, but to be honest, notmany people are doing this sort of thing)
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
The process
• For each feed in a list of feeds:
• If it’s supplying CML data, set Golem on each entry, getthe observables out, and turn them into triples; runOSCAR3 over the title and/or abstract
• If it’s not, extract the free text from each entry, send it tothe OSCAR web service, and assign triples based on thechemical entities OSCAR finds
• Upload the RDF to your triple store
• (I’m using the Talis platform, so that’s just curl)
• And. . .
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
The process
• For each feed in a list of feeds:
• If it’s supplying CML data, set Golem on each entry, getthe observables out, and turn them into triples; runOSCAR3 over the title and/or abstract
• If it’s not, extract the free text from each entry, send it tothe OSCAR web service, and assign triples based on thechemical entities OSCAR finds
• Upload the RDF to your triple store
• (I’m using the Talis platform, so that’s just curl)
• And. . .
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
The process
• For each feed in a list of feeds:
• If it’s supplying CML data, set Golem on each entry, getthe observables out, and turn them into triples; runOSCAR3 over the title and/or abstract
• If it’s not, extract the free text from each entry, send it tothe OSCAR web service, and assign triples based on thechemical entities OSCAR finds
• Upload the RDF to your triple store
• (I’m using the Talis platform, so that’s just curl)
• And. . .
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
The process
• For each feed in a list of feeds:
• If it’s supplying CML data, set Golem on each entry, getthe observables out, and turn them into triples; runOSCAR3 over the title and/or abstract
• If it’s not, extract the free text from each entry, send it tothe OSCAR web service, and assign triples based on thechemical entities OSCAR finds
• Upload the RDF to your triple store
• (I’m using the Talis platform, so that’s just curl)
• And. . .
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
The process
• For each feed in a list of feeds:
• If it’s supplying CML data, set Golem on each entry, getthe observables out, and turn them into triples; runOSCAR3 over the title and/or abstract
• If it’s not, extract the free text from each entry, send it tothe OSCAR web service, and assign triples based on thechemical entities OSCAR finds
• Upload the RDF to your triple store
• (I’m using the Talis platform, so that’s just curl)
• And. . .
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
The process
• For each feed in a list of feeds:
• If it’s supplying CML data, set Golem on each entry, getthe observables out, and turn them into triples; runOSCAR3 over the title and/or abstract
• If it’s not, extract the free text from each entry, send it tothe OSCAR web service, and assign triples based on thechemical entities OSCAR finds
• Upload the RDF to your triple store
• (I’m using the Talis platform, so that’s just curl)
• And. . .
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
SPARQL is great.
Just post queries at a SPARQL endpoint:authortemplate=’’’PREFIX dc: <http://purl.org/dc/terms/>PREFIX ce:<http://wwmm.ch.cam.ac.uk/crystaleye/dictionary#>DESCRIBE ?file WHERE { ?file dc:contributorsome author . }’’’
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
SPARQL isn’t (entirely) great.
• Scientists shouldn’t have to know this stuff.
• So we need to build a front end which your average senioracademic might be able to use. . .
• (i.e. it’s got to look like a website.)
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
SPARQL isn’t (entirely) great.
• Scientists shouldn’t have to know this stuff.
• So we need to build a front end which your average senioracademic might be able to use. . .
• (i.e. it’s got to look like a website.)
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
SPARQL isn’t (entirely) great.
• Scientists shouldn’t have to know this stuff.
• So we need to build a front end which your average senioracademic might be able to use. . .
• (i.e. it’s got to look like a website.)
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
What queries do we want?
• What experimental data is an author responsible for?
• What chemical entities are in some data?
• Where is a given chemical entity talked about?
• So we can build a web app around these queries.
• django + rdflib + sparql + Talis Platform
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
What queries do we want?
• What experimental data is an author responsible for?
• What chemical entities are in some data?
• Where is a given chemical entity talked about?
• So we can build a web app around these queries.
• django + rdflib + sparql + Talis Platform
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
What queries do we want?
• What experimental data is an author responsible for?
• What chemical entities are in some data?
• Where is a given chemical entity talked about?
• So we can build a web app around these queries.
• django + rdflib + sparql + Talis Platform
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
What queries do we want?
• What experimental data is an author responsible for?
• What chemical entities are in some data?
• Where is a given chemical entity talked about?
• So we can build a web app around these queries.
• django + rdflib + sparql + Talis Platform
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
What queries do we want?
• What experimental data is an author responsible for?
• What chemical entities are in some data?
• Where is a given chemical entity talked about?
• So we can build a web app around these queries.
• django + rdflib + sparql + Talis Platform
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Demo!
And here it is.
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Thanks to. . .
• Talis (http://n2.talis.com/) for access to their platform
• and to the RSC and IUCr for their support of CrystalEye.
Automaticallyindexing
science usingnatural-language
processing,RDF andSPARQL
AndrewWalkingshaw,
Nick Day,Peter Corbett,Jim Downing,
JoeTownsend,
PeterMurray-Rust
Gatheringdata
Extracting(meta)data
Using the data
Thanks
Thanks to. . .
• Talis (http://n2.talis.com/) for access to their platform
• and to the RSC and IUCr for their support of CrystalEye.
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