OR2012 presentation on Text Mining in CORE

1. Text mining in CORE
Petr Knoth
The Open University
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2. Outline
• Introduction of the CORE system
• Three phases:
• Metadata and content harvesting
• Semantic Enrichment
• Providing services
• Supporting research in mining databases of scientific
publications (DiggiCORE)
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3. CORE objectives
• To provide a platform for the delivery of Open Access content
aggregated from multiple sources and to deliver a wide range of
services on top of this aggregation.
• A nation-wide aggregation system that will improve the discovery
of publications stored in British Open Access Repositories (OARs).
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4. CORE functionality
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5. CORE functionality
Content harvesting, processing
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6. CORE functionality
Semantic enrichment
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7. CORE functionality
Providing services
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8. CORE functionality
Content harvesting, processing
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9. Growth of items in Open Access repositories
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10. Growth of Open Access repositories
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11. Green Open Access - statistics
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12. Why we need aggregations?
“Each individual repository is of limited value for research: the real
power of Open Access lies in the possibility of connecting and tying
together repositories, which is why we need interoperability. In
order to create a seamless layer of content through connected
repositories from around the world, Open Access relies on
interoperability, the ability for systems to communicate with each
other and pass information back and forth in a usable format.
Interoperability allows us to exploit today's computational power so
that we can aggregate, data mine, create new tools and
services, and generate new knowledge from repository content.’’
[COAR manifesto]
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13. Aggregation in CORE
• OAI-PMH metadata harvesting
• Locating full-text
• Focused crawling (to locate full-texts)
• Focused crawling (driven by citation analysis)
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14. CORE functionality
Semantic enrichment
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15. Aggregations need access to content, not just metadata!
• Certain metadata types can be created only at the level of the
aggregation
• Certain metadata can be changing in time
• Ensuring content:
• accessibility
• availability
• validity
• quality
• …
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16. Semantic similarity and duplicates detection
• Cosine similarity calculated on tfidf vectors extracted from full-
texts
[Knoth et al, COLING 2010; Knoth et al, IMMM 2011]
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17. Semantic similarity and duplicates detection
• Heuristics to reduce the number of combinations (problem with
the query length)
• Cross-language linking tests [Knoth et al, NTCIR-9 CrossLink 2011;
Knoth et al IJC-NLP CLIA 2011]
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18. Information extraction, citation parsing and target recognition
• ParsCIT tool (based on CRF) for extraction of reference sections
• Levensthein distance used for target detection
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19. Text categorisation
• 17 top-level DOAJ classes
(http://www.doaj.org/doaj?func=browse&uiLanguage=en)
• 1080 examples
• SVM multiclass
• 10 fold cross-validation
• 91.4% accuracy
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20. CORE functionality
Providing services
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21. Who should be supported by aggregations?
The following users groups (divided according to the level of
abstraction of information they need):
• Raw data access.
• Transaction information access.
• Analytical information access.
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22. Who should be supported by aggregations?
• The following users groups (divided according to the level of
abstraction of information they need):
• Raw data access. Developers, DLs, DL researchers, companies …
• Transaction information access. Researchers, students, life-long learners …
• Analytical information access. Funders, government, bussiness intelligence
…
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23. Should a single aggregation system support all three user types?
Can be realised by more than one system
providing that
the dataset is the same!
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24. CORE applications
• CORE Portal
• CORE Mobile
• CORE Plugin
• CORE API
• Repository Analytics
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25. Who should be supported by aggregations?
• The following users groups (divided according to the level of
abstraction of information they need):
• Raw data access. Developers, DLs, DL researchers, companies …
• Transaction information access. Researchers, students, life-long learners …
• Analytical information access. Funders, government, bussiness intelligence
…
CORE API CORE Portal, CORE
Mobile, CORE Plugin Repository Analytics
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26. CORE Applications
CORE API – Enables external systems and services to interact with the
CORE repository.
• Search service
• Pdf and plain text
service
• Similarity service
• Classification service
• Citation service
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27. CORE Applications
CORE Portal – Allows searching and navigating scientific publications
aggregated from Open Access repositories
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28. Snippets
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29. CORE Applications
CORE Mobile – Allows
searching and
navigating scientific
publications aggregated
from Open Access
repositories
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30. CORE Applications
CORE Plugin – A plugin to system that recommendations for related
items.
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31. CORE Applications
Repository Analytics – is an analytical tool supporting providers of
open access content (in particular repository managers).
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34. CORE statistics
• Content
• 7M records
• 230 repositories
• 402k full-texts
• 1TB of data
• 40GB large index
• 35 million RDF triples in the CORE LOD repository
• Started: February 2011
• Budget: 140k£
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35. Outline
• Introduction of the CORE system
• Three phases:
• Metadata and content harvesting
• Semantic Enrichment
• Providing services
• Supporting research in mining databases of scientific
publications (DiggiCORE)
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36. objective
Software for exploration and analysis of very large and
fast-growing amounts of research publications stored
across Open Access Repositories (OAR).
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37. DiggiCORE networks
Three networks: (a) semantically related papers,
(b) citation network, (c) author citation network
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38. DiggiCORE objectives
Allow researchers to use this platform to analyse
publications.
Why?
• To identifying patterns in the behaviour of research
communities
• To detect trends in research disciplines
• To gain new insights into the citation behaviour of researchers
• To discover features that distinguish papers with high impact
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39. Summary
• The rapid growth of OA content provides great opportunity for
text-mining.
• Aggregations need to aggregate content, not just metadata.
• Aggregations should serve the needs of different user groups
including researchers who need access to data. CORE aims to
support them.
• We can have many services that are part of the infrastructure,
but should work with the same data.
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40. Thank you!
William Wallace
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