Overview of scientific discourse annotatoin

A brief introduc.on to current efforts in

scien.fic discourse annota.on

Anita de Waard

Disrup/ve Technology Director, Elsevier Labs-‐ also on behalf of HCLS/UU/D2S...

hEp://elsatglabs.com/labs/anita

Thursday, October 20, 2011

http://elsatglabs.com/labs/anita


One way of subdividing the field ofScien/fic Discourse Annota/on:

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Five levels of markup:

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Five levels of markup: 1.Sec/on: “Discussion”, ~ several paragraphs (+HCLS)

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2.Module: “Research Ques/on”, ~ paragraph

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3.Statement: “Hypothesis”, ~ sentence/clause (+UU)

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3.Statement: “Hypothesis”, ~ sentence/clause (+UU)4.Rela/on: “Supports”, ~ hyperlink(En/ty: “Gene Name”, ~ NP)

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5.Special case of 3&4: Claim/Evidence network (+D2S)

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For each level:

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For each level: a)Why? Use cases

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b)What, by whom? Concepts, ontology, authors?2








b)What, by whom? Concepts, ontology, authors?

c) How? Manual, automated? 2


1. Sec/on-‐level markup

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3

INTRODUCTION



a.Why mark up sec/ons?-‐ Search: e.g. search for en//es in Methods

-‐ Visualisa/on: e.g. structured browse at sec/on level

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INTRODUCTION





b.What, by whom?-‐ Background/Contribu/on/Discussion model for CS

-‐ HCLS: Ontology of Rhetorical Blocks (ORB)= IMRaD in OWL

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INTRODUCTION







3

INTRODUCTION

Annotation







c.Automate? -‐ Yes -‐[Hovy/Ramakrishnan]

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INTRODUCTION

Annotation


HCLS SciDis: develop standards for this markup

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2.a. Module-‐level markup: why?

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-‐ BeEer search: e.g. query inside ‘Research ques/on’

-‐ Ini/al idea: content reuse, e.g.

-‐ Write Methods sec/ons once, import/link many

-‐ Different way of crea/ng a collec/on of scholarly content: not standalone narra/ve, but connected set of modules


2.b. Module-‐level markup: What, by whom?

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-‐ Kircz, ’98: “a much more radical approach would be to [break] apart the linear text into independent modules, each with its own unique cogni/ve character.”


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-‐ Harmsze, ‘00: modular model for physics papers


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-‐ LiquidPub, 2010: Structured Knowledge Objects


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-‐ HCLS: Medium-‐grained structure: core narra/ve components


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-‐ HCLS: Medium-‐grained structure: core narra/ve components


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The Story of Goldilocks and the Three Bears

Story Grammar Paper The AXH Domain of Ataxin-1 Mediates Neurodegeneration through Its Interaction with Gfi-1/Senseless Proteins

Once upon a time Time Setting Background The mechanisms mediating SCA1 pathogenesis are still not fully understood, but some general principles have emerged.

a little girl named Goldilocks Characters

Setting

Objects of study the Drosophila Atx-1 homolog (dAtx-1) which lacks a polyQ tract,

She went for a walk in the forest. Pretty soon, she came upon a house.

Location

Setting

Experimental setup

studied and compared in vivo effects and interactions to those of the human protein

She knocked and, when no one answered,

Goal Theme Researchgoal

Gain insight into how Atx-1's function contributes to SCA1 pathogenesis. How these interactions might contribute to the disease process and how they might cause toxicity in only a subset of neurons in SCA1 is not fully understood.she walked right in. Attempt

Theme

Hypothesis Atx-1 may play a role in the regulation of gene expression

At the table in the kitchen, there were three bowls of porridge.

Name Episode 1 Name dAtX-1 and hAtx-1 Induce Similar Phenotypes When Overexpressed in Files

Goldilocks was hungry. Subgoal

Episode 1

Subgoal test the function of the AXH domain

She tasted the porridge from the first bowl.

Attempt

Episode 1

Method overexpressed dAtx-1 in flies using the GAL4/UAS system (Brand and Perrimon, 1993) and compared its effects to those of hAtx-1. This porridge is too hot! she

exclaimed.Outcome

Episode 1

Results Overexpression of dAtx-1 by Rhodopsin1(Rh1)-GAL4, which drives expression in the differentiated R1-R6 photoreceptor cells (Mollereau et al., 2000 and O'Tousa et al., 1985), results in neurodegeneration in the eye, as does overexpression of hAtx-1[82Q]. Although at 2 days after eclosion, overexpression of either Atx-1 does not show obvious morphological changes in the photoreceptor cells

So, she tasted the porridge from the second bowl.

Activity

Episode 1

Data (data not shown),

This porridge is too cold, she said Outcome

Episode 1

Results both genotypes show many large holes and loss of cell integrity at 28 days

So, she tasted the last bowl of porridge.

Activity

Episode 1

Data (Figures 1B-1D).

Ahhh, this porridge is just right, she said happily and

Outcome

Episode 1

Results Overexpression of dAtx-1 using the GMR-GAL4 driver also induces eye abnormalities. The external structures of the eyes that overexpress dAtx-1 show disorganized ommatidia and loss of interommatidial bristles she ate it all up.

Episode 1

Data (Figure 1F),

Story grammar model for science


http://www.sciencedirect.com/science?_ob=ArticleURL&_aset=V-WA-A-W-V-MsSAYZW-UUA-U-AACAVYEDAD-AAVEUDUCAD-YEEEYVDDW-V-U&_rdoc=2&_fmt=full&_udi=B6WSN-4GYG5B3-K&_coverDate=08%252F26%252F2005&_cdi=7051&_orig=search&_st=13&_sort=d&view=c&_acct=C000000593&_version=1&_urlVersion=0&_userid=3222073&md5=1fc244e0e7cfb82d0d6fdb19a49a89f9

http://www.sciencedirect.com/science?_ob=ArticleURL&_aset=V-WA-A-W-V-MsSAYZW-UUA-U-AACAVYEDAD-AAVEUDUCAD-YEEEYVDDW-V-U&_rdoc=2&_fmt=full&_udi=B6WSN-4GYG5B3-K&_coverDate=08%252F26%252F2005&_cdi=7051&_orig=search&_st=13&_sort=d&view=c&_acct=C000000593&_version=1&_urlVersion=0&_userid=3222073&md5=1fc244e0e7cfb82d0d6fdb19a49a89f9

2.c. Module-‐level markup: how?

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2.c. Module-‐level markup: how?-‐ Automated recogni/on: very difficult:

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2.c. Module-‐level markup: how?-‐ Automated recogni/on: very difficult:-‐ How do you know where the boundaries are?

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-‐ Even difficult for author to iden/fy!

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-‐ Author creates: templates.

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-‐ Author creates: templates.-‐ XPharm, 2001: modular text book in pharmacology:

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-‐ Only works if you pay authors!

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-‐ Only works if you pay authors!

-‐ Mo/f detec/on workmight offer help?

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3. Statement-‐level markup

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a.Why?-‐ Automated summarisa/on?

-‐ Towards claim-‐evidence networks



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a.Why?-‐ Automated summarisa/on?

-‐ Towards claim-‐evidence networks

b.How, by whom? -‐ Comparison of three groups: 1.Liakata et al.: CoreSC2.Ananiadou et al.: Metaknowledge annota/on3.De Waard/Pander Maat: Discourse Segment Types

-‐ Annotated three texts: compare schemes, levels, annota/on overlap


3.1 Liakata et al.: Core-‐Scien/fic Concepts (CoreSC) Annota/on Scheme

s


3.1 Liakata et al.: Core-‐Scien/fic Concepts (CoreSC) Annota/on Scheme

Three-‐layer, ontology-‐mo/vated annota/on scheme for sentence annota/on, which views a paper as the humanly readable representa0on of a scien0fic inves0ga0on: [45-‐page guideline: Liakata & Soldatova 2008] -‐ 1st layer: Core Scien4fic Concepts (CoreSCs): Hypothesis, Mo/va/on, Goal, Object, Background, Method, Experiment, Model, Observa/on, Result, Conclusion

-‐ 2nd layer: Proper4es of CoreSCs. Novelty (New/Old) and Advantage (advantage/disadvantage)

-‐ 3rd layer: Concept Iden4fiers: linking sentences together which refer to the same instance of a CoreSC

s


3.1 CoreSC Annota/on tool:


3.1 CoreSC Annota/on tool:

• Automated annota/on with CoreSC system well underway!


Class / Type(Grounded to an event

ontology)

Knowledge Type• Inves4ga4on• Observa4on• Analysis• General

Manner• High• Low• Neutral

Certainty Level•L3•L2•L1

Polarity• Nega4ve• Posi4ve

Source• Other• Current

Par4cipants• Theme(s)• Actor(s)

Bio-‐Event(Centred on an Event

Trigger)

3.2. Ananiadou et al: Metaknowledge annota/on:


S3 = These results suggest that Y has no effect on expression of X

EventKnowledge

Type

Certainty

Level

Lexical

PolarityManner Source

E1 General L3 Posi4ve Neutral Current

E2 Analysis L2 Nega4ve Neutral Current

3.2 Example of Metaknowledge annota/on:



EventKnowledge

Type

Certainty

Level

Lexical







EventKnowledge

Type

Certainty

Level

Lexical







EventKnowledge

Type

Certainty

Level

Lexical




• Manual annota/on underway of the GENIA event corpus (1000 MEDLINE abstracts)



Both seminomas and the EC component of nonseminomas share features with ES cells. To exclude that the detection of miR-371-3 merely reflects its expression pattern in ES cells, we tested by RPA miR-302a-d, another ES cells-specific miRNA cluster (Suh et al, 2004). In many of the m i R - 3 7 1 - 3 e x p r e s s i n g s e m i n o m a s a n d nonseminomas, miR-302a-d was undetectable (Figs S7 and S8), suggesting that miR-371-3 expression is a selective event during tumorigenesis.

3.3 de Waard/Pander Maat: Discourse Segment Types



Both seminomas and the EC component of nonseminomas share features with ES cells.To exclude thatthe detection of miR-371-3 merely reflects its expression pattern in ES cells,we tested by RPA miR-302a-d, another ES cells-specific miRNA cluster (Suh et al, 2004).In many of the miR-371-3 expressing seminomas and nonseminomas, miR-302a-d was undetectable (Figs S7 and S8),suggesting thatmiR-371-3 expression is a selective event during tumorigenesis.





Fact





Fact

Hypothesis





Fact

Hypothesis

Method





Fact

Hypothesis

Method

Result





Fact

Hypothesis

Method

Result

Implication





Fact

Hypothesis

Method

Result

Implication

Goal





Fact

Hypothesis

Method

Result

Implication

Goal

Reg-Implication





Fact

Hypothesis

Method

Result

Implication

Goal

Reg-Implication

Conceptual knowledge





Fact

Hypothesis

Method

Result

Implication

Goal

Reg-Implication

Conceptual knowledge

ExperimentalEvidence



3.3 Iden/fica/on of DSTs:

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3.3 Iden/fica/on of DSTs:-‐ Verb form: tense, e.g. -‐ Concepts in ‘state’ (gnomic) present:‘Dopaminergic innerva/on plays a major role in the control of mood and its perturba/on’

-‐ Experiments in ‘event’ past: ‘Four out of seven cell lines expressed this cluster’

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-‐ Seman4c verb class: -‐ Research verbs (Inves/ga/on, Predic/on, Procedure, Observa/on, Interpreta/on)

-‐ Discourse verbs -‐ Proper/es and rela/onships -‐ between things and concepts

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-‐ Seman4c verb class: -‐ Research verbs (Inves/ga/on, Predic/on, Procedure, Observa/on, Interpreta/on)

-‐ Discourse verbs -‐ Proper/es and rela/onships -‐ between things and concepts

-‐ Modality -‐ Types: Source {Author, others, unknown}, Basis {Data, Reasoning, 0}, Value {Certain, probable, possible, unknown}

-‐ Markers: Modal aux, verb class Interpreta/on, epistemic adverbs15


3. Same Statement annotated three ways:


3. Same Statement annotated three ways:CoreSC:<annotationART atype="GSC" type="Res" conceptID="Res24" novelty="None" advantage="None">Here we show that BOB.1/OBF.1 regulates Btk gene expression.</annotationART> BioEvent/MetaKnowledge:<sentence id="S6">Here we show that <term id="T13" sem="Protein_family_or_group">

<gene-or-gene-product id="G9">BOB.1</gene-or-gene-product>/<gene-or-gene-product id="G10">OBF.1</gene-or-gene-product>

</term> regulates <term id="T14" sem="Biological_process">

<term id="T15" sem="DNA_domain_or_region"><gene-or-gene-product id="G11">Btk

</gene-or-gene-product> gene</term> expression

</term>. </sentence>

Discourse Segments:<segment segID ="286" section = "D" segtype = "RegImplication">Here we show that</segment><segment segID ="287" section = "D" segtype = "Implication">








Discourse Segments:<segment segID ="286" section = "D" segtype = "RegImplication">Here we show that</segment><segment segID ="287" section = "D" segtype = "Implication">BOB.1/OBF.1 regulates Btk gene expression.</segment> Thursday, October 20, 2011







Discourse Segments:<segment segID ="286" section = "D" segtype = "RegImplication">Here we show that</segment><segment segID ="287" section = "D" segtype = "Implication">BOB.1/OBF.1 regulates Btk gene expression.</segment> Thursday, October 20, 2011

Who Why What How

Liakata: CoreSC Iden/fy main components of scien/fic inves/ga/on for machine learning

Sentence Manual corpus, automated annota/on tools -‐ working on automated detec/on

Ananiadou: MetaKnowledge/BioEvents

Enhance informa/on extrac/on for biomedical texts to enable metadiscourse annota/on

Events (intra-‐senten/al): can be several per sentence, or one in more sentences

Manual corpus, working on automated detec/on

de Waard:Discourse Segment Types

Iden/fy mechanisms of conveying (epistemic) knowledge in scien/fic discourse

Clause Manual, ideas (but no real plans!) for automated iden/fica/on

3. Comparing statement-‐level annota/on models


4. Rela.ons

a.Why?-‐ Argumenta/on visualisa/on

b.What, by whom? -‐ Harmsze (1999): Ontology of content rela/onships

-‐ IBIS, ClaiMaker (2001)

-‐ Diligent argumenta/on ontology (2005)

-‐ SALT: RST (2007)

-‐ SWAN (2010)

c.How? -‐ So far: Manually 18


4. Rela.ons





-‐ SALT: RST (2007)

-‐ SWAN (2010)



4. Rela.ons





-‐ SALT: RST (2007)

-‐ SWAN (2010)



4. Rela.ons





-‐ SALT: RST (2007)

-‐ SWAN (2010)



4. Rela.ons





-‐ SALT: RST (2007)

-‐ SWAN (2010)



5.Claim-‐Evidence Networks (statement + rela/ons)

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a.Why?



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a.Why?Show argumenta/on across body of work



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b.What, by whom?



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b.What, by whom?-‐ Buckingham Shum, 1999



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-‐ SWAN: Clark, Ciccarese et al., 2005



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-‐ Nanopublica/ons: Mons, 2010



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-‐ Nanopublica/ons + SWAN, 2011



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c.How?



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c.How?

-‐ So far: manuallyThursday, October 20, 2011


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c.How?

-‐ So far: manuallyThursday, October 20, 2011

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A. Philips’ Electronic Patient Records B. Elsevier-published Clinical Guideline

C. Elsevier (or other publisher’s) Research Report or Data

D2S Use case: Claim-‐Evidence Network in Medicine


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Step 1: Patient data + diagnosis link to Guideline recommendation



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Step 2: Guideline recommendation links to evidence in report or data



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Step 2: Guideline recommendation links to evidence in report or data


Related HCLS Use Case: Accelerate uptake of medical research on drug-‐drug interac/on in product inserts


Five levels of scien/fic discourse annota/on

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Why What/Who How

Sec/on Search, UI ABCDE, ORB/HCLSAutomated!

Publisher helps?

Module Content reuseHarmsze/Kircz, LiquidPub,

HCLSManual: templates

Statement Summaries; towards networks

Teufel, Ananiadou, Liakata, UU

Working towards automated detec/on

Rela/ons NetworksSALT, ScholOnto, Diligent,

SWANManual: some tools, never took off...

Claim/Evidence

Argumenta/on networks

ScholOnto, SWAN, Nanopublica/ons, D2S

Manual-‐ towards automa/on?


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Why What/Who How


Publisher helps?








Claim/Evidence





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Why What/Who How


Publisher helps?








Claim/Evidence





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Why What/Who How


Publisher helps?








Claim/Evidence





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Why What/Who How


Publisher helps?








Claim/Evidence





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Why What/Who How


Publisher helps?








Claim/Evidence





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Why What/Who How


Publisher helps?








Claim/Evidence





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MoFfs?


Ques.ons:

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Ques.ons:

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Scien/fic discourse -‐> fairy tales: how can we transfer knowledge here to scien/fic discourse community?


Ques.ons:

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Issue: Scien/sts do not like being told that they write fairy tales!


Ques.ons:

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Fairy tales -‐> scien/fic discourse: is anyone here/in this community interested in working on inter-‐domain transfer of tools, technologies and theories?


Ques.ons:

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Fairy tales -‐> scien/fic discourse: is anyone here/in this community interested in working on inter-‐domain transfer of tools, technologies and theories?

Anita de Waard, [email protected]

-‐HCLS: hEp://www.w3.org/wiki/HCLSIG/SWANSIOC

-‐UU: hEp://elsatglabs.com/labs/anita

-‐D2S: hEp://www.data2seman/cs.org


mailto:[email protected]

mailto:[email protected]

http://www.w3.org/wiki/HCLSIG/SWANSIOC/Actions/RhetoricalStructure/

http://www.w3.org/wiki/HCLSIG/SWANSIOC/Actions/RhetoricalStructure/



http://www.data2semantics.org

http://www.data2semantics.org