This document discusses developing a linked open data ecosystem to better organize and analyze scientific literature and research data. It proposes extracting structured elements and facts from papers using natural language processing and crowdsourcing. Extracted data would be aggregated and stored using the RDF Data Cube Vocabulary to enable analysis across different dimensions. Visual analytics tools and a sharing platform could help analyze relationships and commercialize insights. The overall aim is to make more of the empirical evidence and knowledge from research discoverable, comparable and reusable.

1. Commercially empowered Linked Open Data
Ecosystems in Research
Towards unfolding todays and tomorrows
scientific treasures
Michael Granitzer
University of Passau
FP 7 Strep No. 296150
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2. nani gigantum humeris insidentes
 Standing on the shouldes of giants
– Research builds on the past
– We pass on knowledge, to create
new knowledge
Root of (Western) Society
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3. Lying under a pile of text documents
 .. with varying quality
 .. with contradicting facts
 .. with missing data
 .. labour intensive to compare results
 Some examples
– “Improvements that don’t add up”
Armstrong et. al. 2009
– “Why most research results are false”
Ioannidis, 2005
Can we do better?
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4. Yes, we (think) we can...
 Make Facts and Figures explicit, discoveralbe and comparable
 Giving textually enCODED scientific knowledge, we can
– Extract facts from research papers
– Integrate those facts with existing knowledge
– Make it available for (visual) analysis
– Crowdsource
 Focus on
– Empirical observations/facts
– Linked Open Data
– Computer Science and Biomedical Domain
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5. That‘s nice, but how?
Extract Analyse & Share &
Aggregate
& Integrate Organise Commercialise
Dependency and Frequency Analysis
Graph Depencies
Machine
Algorithm
Learning
CRF SVM
Biomedical
Data Set 1
Gesamtergebnis"
Algorithms"
(Leer)"
SVM" Domain"
DataSet2"
Experiment"
DataSet1"
CRF" (Leer)"
Biomedical" Gesamtergebnis"
0" 5" 10" 15" 20"
Text, Linked Data Linked Scientific Fact Visual Analytics & Crowdsourcing &
Experiments Data Warehouse Collaborative Marketplace
mind-mapping
5

6. Extract & Integrate: Approach and Challenges
 Extracting Structural Elements
– Tables
– Figures
– Sections and sub-sections
 Extracting Facts from Structural Elements
– Entity extraction (e.g. algorithms, data sets, genes, significance levels etc.)
– Fact extraction – <Entity, Relation, Measure>
– Table Triplification
 Crowdsourcing Extraction
– Extraction quality and domain knowledge remains a key issue
 Empower users to maintain their own extraction model
 Allow to semantically annotate research papers (e.g. entities, facts)
 Result: Semantically annotated scientific data as LOD Endpoint
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7. Extract & Integrate: Example
Numerical Facts
Dimension/
Entity
In-Document
Context
Ranking Facts
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8. Extract & Integrate: Current Status
 TeamBeam -PDF
Structure Extraction
– Structural elements
– Focusing now on
tables
 Entity Extraction in work
 First Prototypes for
Table2RDFDataCube
TeamBeam — Meta-Data Extraction from Scientific Literature
By Roman Kern, Graz University of Technology; Kris Jack and Maya Hristakeva, Mendeley Ltd.; Michael
Granitzer, University of Passau 8

9. Aggregate: Approach and Challenges
 Representation and Storage
– Representation using the RDF Data Cube Vocabulary
• Dimensions (e.g. Algorithms, Genes)
• Measures (e.g. 0.3, 37) and Attributes (e.g. %, °)
– Challenge 1: Ensure independency of dimensions
– Challenge 2: Decentralized querying and aggregation
http://www.w3.org/TR/vocab-data-cube/#ref_qb_measureType
 SPARQL Data Warehousing Wizard
– Provide simple and intuitive Wizard for creating aggregation queries
• Google-like starting point
• Pivot table creation similar like in Spreadsheets
– Store using RDF Data Cube Vocabulary
 Linked Scientific Fact Data Warehouse for non-IT Experts
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10. Aggregate: Current Status
 Representation and Storage
– Data Model implemented
– Triplification of Benchmarking Data (e.g. CLEF, TPC-H etc.)
We are looking for data
 SPARQL Data Warehousing Wizard
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11. Analyse: Approach and Challenges
 Visual Analytics for Linked Scientific Facts
– RDF based description of visualisations
• Glue between data and single visualisations
• Make visualisation state explicit
• Share visualisation state
– HTML 5 based visualisations and visualisation wizard
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12. Share: Approach and Challenges
 Provenance
– Who published data?
– Who modified data?
 Share aggregated data sets and annotation models
– Build on insights created by others
– Re-use text annotation models
 Share visual analytics applications
– Simple visualisations might be misleading
– Sharing whole states of a visual analysis will reveal
more details on certain decisions
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13. Why should YOU do it?
Marketplace concept for research data
 Users (=researchers) will be enabled to “sell” their analysis results
(or give it away for free)
 Serveral concepts to be investigated: Revenue chains, roles, models
(donations, paid subscription for data feeds, purchase etc.)
 Increased opportunities for researchers and research data
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14. integrate crowdsource
extract &
organise
visualise
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