Commercially empowered Linked Open Data

Ecosystems in Research

Towards unfolding todays and tomorrows

scientific treasures

Michael Granitzer

University of Passau

1FP 7 Strep No. 296150

nani gigantum humeris insidentes

Standing on the shouldes of giants

– Research builds on the past

– We pass on knowledge, to create

new knowledge

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Root of (Western) Society

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

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Can we do better?

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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That‘s nice, but how?

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Linked Scientific Fact Data Warehouse

Extract& Integrate

AggregateAnalyse & Organise

Share & Commercialise

Visual Analytics &Collaborative mind-mapping

Text, Linked DataExperiments

Crowdsourcing &Marketplace

Dependency and Frequency Analysis

Machine Learning

Algorithm

CRF SVM

BiomedicalData Set 1

Graph Depencies

0" 5" 10" 15" 20"

Biomedical"

CRF"

DataSet1"

DataSet2"

SVM"

(Leer)"

Gesamtergebnis"Algorithms"

Domain"

Experiment"

(Leer)"

Gesamtergebnis"

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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Extract & Integrate: Example

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Numerical Facts

Dimension/Entity

In-Document Context

Ranking Facts

Extract & Integrate: Current Status

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TeamBeam — Meta-Data Extraction from Scientific LiteratureBy Roman Kern, Graz University of Technology; Kris Jack and Maya Hristakeva, Mendeley Ltd.; Michael Granitzer, University of Passau

TeamBeam -PDF

Structure Extraction

– Structural elements

– Focusing now on

tables

Entity Extraction in work

First Prototypes for

Table2RDFDataCube

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

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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http://www.w3.org/TR/vocab-data-cube/#ref_qb_measureType

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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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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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

Share: Approach and Challenges

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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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Find us, join us, ask us, help us

http://code-research.eu/http://www.facebook.com/CODEresearchEU

#CODEresearchEU

integrate

extract & visualise

organise

crowdsource