executing the research paper
DESCRIPTION
Talk held at STM Innovations Workshop, London, UK December 2, 2011http://www.stm-assoc.org/events/stm-innovations-seminar-2011/TRANSCRIPT
How to Execute the Research Paper
Anita de WaardDisruptive Technology Director, Elsevier Labs
http://elsatglabs.com/labs/anita
How to execute a research paper
- Why?
- Three use cases for linked, integrated knowledge
- What?
- Three technologies for enabling this linking and execution
- How?
- Three tools for annotation, storage and access
- What next?
- Force11 and ideas about the future
Three Use Cases
3
Use case #1: Claim-Evidence Network in Medicine
Background: Proper implementation of clinical decision support systems (CDS) can:- Reduce errors in medical care - Bring research results faster to the front-line clinician - Significantly improve patient outcome.
Use case #1: Claim-Evidence Network in Medicine
Background: Proper implementation of clinical decision support systems (CDS) can:- Reduce errors in medical care - Bring research results faster to the front-line clinician - Significantly improve patient outcome.
Requirements: To that end, such systems need to:- Be able to answer complex questions - Aggregate data from multiple sources, combining complex patient specific data
with information from external sources- Be semantically aware - Be continually updated with the latest validated research results.
Use case #1: Claim-Evidence Network in Medicine
Background: Proper implementation of clinical decision support systems (CDS) can:- Reduce errors in medical care - Bring research results faster to the front-line clinician - Significantly improve patient outcome.
Requirements: To that end, such systems need to:- Be able to answer complex questions - Aggregate data from multiple sources, combining complex patient specific data
with information from external sources- Be semantically aware - Be continually updated with the latest validated research results.
Components: To develop such semantically aware systems, we need:- Flexible frameworks supporting the development of such applications- Seamless integration of relevant content- Content sources with high quality content - Tools enabling the extraction and aggregation of such content.
Use case #1: Claim-Evidence Network in Medicine
5
A. Philips’ Electronic Patient Records B. Elsevier-published Clinical Guideline
C. Elsevier (or other publisher’s) Research Report or Data
Use case #1: Claim-Evidence Network in Medicine
5
A. Philips’ Electronic Patient Records B. Elsevier-published Clinical Guideline
C. Elsevier (or other publisher’s) Research Report or Data
Step 1: Patient data + diagnosis link to Guideline recommendation
Use case #1: Claim-Evidence Network in Medicine
5
A. Philips’ Electronic Patient Records B. Elsevier-published Clinical Guideline
C. Elsevier (or other publisher’s) Research Report or Data
Step 1: Patient data + diagnosis link to Guideline recommendation
Step 2: Guideline recommendation links to evidence in report or data
Use case #1: Claim-Evidence Network in Medicine
Use case #2: Updating Drug-Drug Interactions
Background: - Drug-drug interactions (DDIs) are a significant source of preventable adverse
effects- Factors contributing to the occurrence of preventable DDIs include:
- a lack of knowledge of the patient’s concurrent medications - inaccurate or inadequate knowledge of interactions by health care providers
Use case #2: Updating Drug-Drug Interactions
Background: - Drug-drug interactions (DDIs) are a significant source of preventable adverse
effects- Factors contributing to the occurrence of preventable DDIs include:
- a lack of knowledge of the patient’s concurrent medications - inaccurate or inadequate knowledge of interactions by health care providers
Requirements: We (HCLS SciDiscourse group: Elsevier, DERI, Pittsburgh, EBI) will:- Manually mark up a diverse collection of content with DDIs - Develop/train NLP tools to recognize these- Create a triple store to maintain the relationships between drugs-DDIs-content
Use case #2: Updating Drug-Drug Interactions
Background: - Drug-drug interactions (DDIs) are a significant source of preventable adverse
effects- Factors contributing to the occurrence of preventable DDIs include:
- a lack of knowledge of the patient’s concurrent medications - inaccurate or inadequate knowledge of interactions by health care providers
Requirements: We (HCLS SciDiscourse group: Elsevier, DERI, Pittsburgh, EBI) will:- Manually mark up a diverse collection of content with DDIs - Develop/train NLP tools to recognize these- Create a triple store to maintain the relationships between drugs-DDIs-content
Components: To develop this system, we need:- Scientific discourse ontologies to mark up relevant statement and seed NLP- Natural language processing to identify relevant DDI- Linked Data architecture to enable storage and access to this information
Use case #2: Updating Drug-Drug Interactions
7
Use case #2: Updating Drug-Drug Interactions
Images from: Discovering drug–drug interactions: a text-mining and reasoningapproach based on properties of drug metabolism, Luis Tari∗, Saadat Anwar, Shanshan Liang, James Cai and Chitta Baral Vol. 26 ECCB 2010, pages i547–i553 doi:10.1093/bioinformatics/btq382
7
Use case #2: Updating Drug-Drug Interactions
Images from: Discovering drug–drug interactions: a text-mining and reasoningapproach based on properties of drug metabolism, Luis Tari∗, Saadat Anwar, Shanshan Liang, James Cai and Chitta Baral Vol. 26 ECCB 2010, pages i547–i553 doi:10.1093/bioinformatics/btq382
Step 1: Manually identify DDIs and drug names in wide collection of content sources
7
Use case #2: Updating Drug-Drug Interactions
Images from: Discovering drug–drug interactions: a text-mining and reasoningapproach based on properties of drug metabolism, Luis Tari∗, Saadat Anwar, Shanshan Liang, James Cai and Chitta Baral Vol. 26 ECCB 2010, pages i547–i553 doi:10.1093/bioinformatics/btq382
Step 1: Manually identify DDIs and drug names in wide collection of content sources
Step 2: Develop a model of Drug-Drug Interaction and define candidates
7
Use case #2: Updating Drug-Drug Interactions
Images from: Discovering drug–drug interactions: a text-mining and reasoningapproach based on properties of drug metabolism, Luis Tari∗, Saadat Anwar, Shanshan Liang, James Cai and Chitta Baral Vol. 26 ECCB 2010, pages i547–i553 doi:10.1093/bioinformatics/btq382
Step 1: Manually identify DDIs and drug names in wide collection of content sources
Step 2: Develop a model of Drug-Drug Interaction and define candidates
Step 3: Automate this process and store as Linked Data
Use Case #3: Review and share code
Background: - Core of computational papers is the software- If code is not part of the paper, hard to assess quality- Code reuse can reduce waste of time and (taxpayer’s) money
Use Case #3: Review and share code
Background: - Core of computational papers is the software- If code is not part of the paper, hard to assess quality- Code reuse can reduce waste of time and (taxpayer’s) money
Requirements: - Provide a way to create, share and review code- Integrate this with the research paper- Enable integration with publisher’s system
Use Case #3: Review and share code
Background: - Core of computational papers is the software- If code is not part of the paper, hard to assess quality- Code reuse can reduce waste of time and (taxpayer’s) money
Requirements: - Provide a way to create, share and review code- Integrate this with the research paper- Enable integration with publisher’s system
Components: - Integration between workflow and text authoring- Code authoring tools and standards that allow reuse- User environment that allows access to disparate results types
Use Case #3: Review and share code
9
Step 1: Develop Virtual Machine environment for creating code
Use Case #3: Review and share code
Pieter Van Gorp, Stefen Mazanek, SHARE: a web portal for creating and sharing executable research papersProcedia Computer Science 00 (2011) 1–6
9
Step 1: Develop Virtual Machine environment for creating code
Use Case #3: Review and share code
Step 2: Create authoring/review environment to allow VM evaluation
Pieter Van Gorp, Stefen Mazanek, SHARE: a web portal for creating and sharing executable research papersProcedia Computer Science 00 (2011) 1–6
9
Step 1: Develop Virtual Machine environment for creating code
Use Case #3: Review and share code
Step 2: Create authoring/review environment to allow VM evaluation
Step 3: Allow access to integrated environment through SciVerse App store
Pieter Van Gorp, Stefen Mazanek, SHARE: a web portal for creating and sharing executable research papersProcedia Computer Science 00 (2011) 1–6
Three Technologies
10
11
Technology #1: Discourse Annotation - at text level
11
Aristotle QuintilianQuintilian Scientific Paper
prooimionIntroduction/ exordium
The introduction of a speech, where one announces the subject and purpose of the discourse, and where one usually employs the persuasive appeal to ethos in order to establish credibility with the audience.
Introduction: positioning
prothesisStatement of Facts/narratio
The speaker here provides a narrative account of what has happened and generally explains the nature of the case.
Introduction: research question
Summary/ propostitio
The propositio provides a brief summary of what one is about to speak on, or concisely puts forth the charges or accusation.
Summary of contents
pistis Proof/ confirmatio
The main body of the speech where one offers logical arguments as proof. The appeal to logos is emphasized here.
Results
Refutation/ refutatio
As the name connotes, this section of a speech was devoted to answering the counterarguments of one's opponent. Related Work
epilogos peroratio Following the refutatio and concluding the classical oration, the peroratio conventionally employed appeals through pathos, and often included a summing up.
Discussion: summary, implications.
Technology #1: Discourse Annotation - at text level
11
Aristotle QuintilianQuintilian Scientific Paper
prooimionIntroduction/ exordium
The introduction of a speech, where one announces the subject and purpose of the discourse, and where one usually employs the persuasive appeal to ethos in order to establish credibility with the audience.
Introduction: positioning
prothesisStatement of Facts/narratio
The speaker here provides a narrative account of what has happened and generally explains the nature of the case.
Introduction: research question
Summary/ propostitio
The propositio provides a brief summary of what one is about to speak on, or concisely puts forth the charges or accusation.
Summary of contents
pistis Proof/ confirmatio
The main body of the speech where one offers logical arguments as proof. The appeal to logos is emphasized here.
Results
Refutation/ refutatio
As the name connotes, this section of a speech was devoted to answering the counterarguments of one's opponent. Related Work
epilogos peroratio Following the refutatio and concluding the classical oration, the peroratio conventionally employed appeals through pathos, and often included a summing up.
Discussion: summary, implications.
Technology #1: Discourse Annotation - at text level
12
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),
Technology #1: Discourse Annotation - at paragraph level
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.
Technology #1: Discourse Annotation - at clause level
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.
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.
Technology #1: Discourse Annotation - at clause level
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.
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
Technology #1: Discourse Annotation - at clause level
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.
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
Hypothesis
Technology #1: Discourse Annotation - at clause level
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.
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
Hypothesis
Method
Technology #1: Discourse Annotation - at clause level
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.
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
Hypothesis
Method
Result
Technology #1: Discourse Annotation - at clause level
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.
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
Hypothesis
Method
Result
Implication
Technology #1: Discourse Annotation - at clause level
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.
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
Hypothesis
Method
Result
Implication
Goal
Technology #1: Discourse Annotation - at clause level
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.
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
Hypothesis
Method
Result
Implication
Goal
Reg-Implication
Technology #1: Discourse Annotation - at clause level
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.
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
Hypothesis
Method
Result
Implication
Goal
Reg-Implication
Conceptual knowledge
Technology #1: Discourse Annotation - at clause level
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.
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
Hypothesis
Method
Result
Implication
Goal
Reg-Implication
Conceptual knowledge
ExperimentalEvidence
Technology #1: Discourse Annotation - at clause level
To investigate the possibility that miR-372 and miR-373 suppress the expression of LATS2, we...
Therefore, these results point to LATS2 as a mediator of the miR-372 and miR-373 effects on cell proliferation and tumorigenicity,
Voorhoeve et al, Cell, 2006:
Technology #1: Discourse Annotation - across texts
To investigate the possibility that miR-372 and miR-373 suppress the expression of LATS2, we...
Therefore, these results point to LATS2 as a mediator of the miR-372 and miR-373 effects on cell proliferation and tumorigenicity,
Voorhoeve et al, Cell, 2006:
Hypothesis
Technology #1: Discourse Annotation - across texts
To investigate the possibility that miR-372 and miR-373 suppress the expression of LATS2, we...
Therefore, these results point to LATS2 as a mediator of the miR-372 and miR-373 effects on cell proliferation and tumorigenicity,
Voorhoeve et al, Cell, 2006:
Hypothesis
Implication
Technology #1: Discourse Annotation - across texts
... two miRNAs, miRNA-372 and-373, function as potential novel oncogenes in testicular germ cell tumors by inhibition of LATS2 expression, which suggests that Lats2 is an important tumor suppressor (Voorhoeve et al., 2006).
Raver-Shapira et.al, JMolCell 2007
To investigate the possibility that miR-372 and miR-373 suppress the expression of LATS2, we...
Therefore, these results point to LATS2 as a mediator of the miR-372 and miR-373 effects on cell proliferation and tumorigenicity,
Voorhoeve et al, Cell, 2006:
Hypothesis
Implication
Technology #1: Discourse Annotation - across texts
... two miRNAs, miRNA-372 and-373, function as potential novel oncogenes in testicular germ cell tumors by inhibition of LATS2 expression, which suggests that Lats2 is an important tumor suppressor (Voorhoeve et al., 2006).
Raver-Shapira et.al, JMolCell 2007
To investigate the possibility that miR-372 and miR-373 suppress the expression of LATS2, we...
Therefore, these results point to LATS2 as a mediator of the miR-372 and miR-373 effects on cell proliferation and tumorigenicity,
Voorhoeve et al, Cell, 2006:
Hypothesis
Implication
Cited Implication
Technology #1: Discourse Annotation - across texts
... two miRNAs, miRNA-372 and-373, function as potential novel oncogenes in testicular germ cell tumors by inhibition of LATS2 expression, which suggests that Lats2 is an important tumor suppressor (Voorhoeve et al., 2006).
Raver-Shapira et.al, JMolCell 2007
miR-372 and miR-373 target the Lats2 tumor suppressor (Voorhoeve et al., 2006)
Yabuta, JBioChem 2007:
To investigate the possibility that miR-372 and miR-373 suppress the expression of LATS2, we...
Therefore, these results point to LATS2 as a mediator of the miR-372 and miR-373 effects on cell proliferation and tumorigenicity,
Voorhoeve et al, Cell, 2006:
Hypothesis
Implication
Cited Implication
Technology #1: Discourse Annotation - across texts
... two miRNAs, miRNA-372 and-373, function as potential novel oncogenes in testicular germ cell tumors by inhibition of LATS2 expression, which suggests that Lats2 is an important tumor suppressor (Voorhoeve et al., 2006).
Raver-Shapira et.al, JMolCell 2007
miR-372 and miR-373 target the Lats2 tumor suppressor (Voorhoeve et al., 2006)
Yabuta, JBioChem 2007:
To investigate the possibility that miR-372 and miR-373 suppress the expression of LATS2, we...
Therefore, these results point to LATS2 as a mediator of the miR-372 and miR-373 effects on cell proliferation and tumorigenicity,
Voorhoeve et al, Cell, 2006:
Hypothesis
Implication
Cited Implication
Fact
Technology #1: Discourse Annotation - across texts
Technology #1: Towards automated Discourse Annotation: CoreSC
Technology #1: Towards automated Discourse Annotation: CoreSC
Technology #1: Towards automated Discourse Annotation: CoreSC
- Classified with Support Vector Machines (SVM)
- Sequence labelling by Conditional Random Fields (CRF)
- F-score between 18% (motivation) and 76% (experimental methods)
- ‘We plan to use CoreSC annotated papers in biology to guide information extraction and retrieval, characterise extracted events and relations and facilitate inference from hypotheses to conclusions in scientific papers.’
Automatic recognition of conceptualisation zones in scientific articles to aid biological information extractionMaria Liakata,, Shyamasree Saha. Simon Dobnik,Colin Batchelor and Dietrich Rebholz-Schuhmann
Bioinformatics 2011 (Accepted)
Technology #2: Linked Data
Technology #2: Linked Data
1. Use URIs to name things
2. Use HTTP URIs so they can be looked up
3. Return useful data when things are looked up
4. Include links to other things in the returned data
Technology #2: Linked Data
1. Use URIs to name things
2. Use HTTP URIs so they can be looked up
3. Return useful data when things are looked up
4. Include links to other things in the returned data
“Linked data is just a term for how to publish data on the web while working with the web. And the web is the best architecture we know for publishing information in a hugely diverse and distributed environment, in a gradual and sustainable way.”
Tennison J, 2010. Why Linked Data for data.gov.uk? http://www.jenitennison.com/blog/node/140
A. de Waard, The Future of the Journal? Integrating research data with scientific discourse
http://precedings.nature.com/documents/4742/version/1
Technology # 3: Workflow integration
A. de Waard, The Future of the Journal? Integrating research data with scientific discourse
http://precedings.nature.com/documents/4742/version/1
1. Research: Each item in the system has metadata (including provenance) and relations to other data items added to it.
metadata
metadata
metadata
metadata
metadata
Technology # 3: Workflow integration
A. de Waard, The Future of the Journal? Integrating research data with scientific discourse
http://precedings.nature.com/documents/4742/version/1
1. Research: Each item in the system has metadata (including provenance) and relations to other data items added to it.
metadata
metadata
metadata
metadata
metadata
2. Workflow: All data items created in the lab are added to a (lab-owned) workflow system.
Technology # 3: Workflow integration
A. de Waard, The Future of the Journal? Integrating research data with scientific discourse
http://precedings.nature.com/documents/4742/version/1
1. Research: Each item in the system has metadata (including provenance) and relations to other data items added to it.
metadata
metadata
metadata
metadata
metadata
2. Workflow: All data items created in the lab are added to a (lab-owned) workflow system.
Rats were subjected to two grueling tests(click on fig 2 to see underlying data). These results suggest that the neurological pain pro-
3. Authoring: A paper is written in an authoring tool which can pull data with provenance from the workflow tool in the appropriate representation into the document.
Technology # 3: Workflow integration
A. de Waard, The Future of the Journal? Integrating research data with scientific discourse
http://precedings.nature.com/documents/4742/version/1
1. Research: Each item in the system has metadata (including provenance) and relations to other data items added to it.
metadata
metadata
metadata
metadata
metadata
2. Workflow: All data items created in the lab are added to a (lab-owned) workflow system.
4. Editing and review: Once the co-authors agree, the paper is ‘exposed’ to the editors, who in turn expose it to reviewers. Reports are stored in the authoring/editing system, the paper gets updated, until it is validated.
Review
Edit
Revise
Rats were subjected to two grueling tests(click on fig 2 to see underlying data). These results suggest that the neurological pain pro-
3. Authoring: A paper is written in an authoring tool which can pull data with provenance from the workflow tool in the appropriate representation into the document.
Technology # 3: Workflow integration
5. Publishing and distribution: When a paper is published, a collection of validated information is exposed to the world. It remains connected to its related data item, and its heritage can be traced.
A. de Waard, The Future of the Journal? Integrating research data with scientific discourse
http://precedings.nature.com/documents/4742/version/1
1. Research: Each item in the system has metadata (including provenance) and relations to other data items added to it.
metadata
metadata
metadata
metadata
metadata
2. Workflow: All data items created in the lab are added to a (lab-owned) workflow system.
4. Editing and review: Once the co-authors agree, the paper is ‘exposed’ to the editors, who in turn expose it to reviewers. Reports are stored in the authoring/editing system, the paper gets updated, until it is validated.
Review
Edit
Revise
Rats were subjected to two grueling tests(click on fig 2 to see underlying data). These results suggest that the neurological pain pro-
3. Authoring: A paper is written in an authoring tool which can pull data with provenance from the workflow tool in the appropriate representation into the document.
Technology # 3: Workflow integration
5. Publishing and distribution: When a paper is published, a collection of validated information is exposed to the world. It remains connected to its related data item, and its heritage can be traced.
Some other publisher
6. User applications: distributed applications run on this ‘exposed data’ universe.
A. de Waard, The Future of the Journal? Integrating research data with scientific discourse
http://precedings.nature.com/documents/4742/version/1
1. Research: Each item in the system has metadata (including provenance) and relations to other data items added to it.
metadata
metadata
metadata
metadata
metadata
2. Workflow: All data items created in the lab are added to a (lab-owned) workflow system.
4. Editing and review: Once the co-authors agree, the paper is ‘exposed’ to the editors, who in turn expose it to reviewers. Reports are stored in the authoring/editing system, the paper gets updated, until it is validated.
Review
Edit
Revise
Rats were subjected to two grueling tests(click on fig 2 to see underlying data). These results suggest that the neurological pain pro-
3. Authoring: A paper is written in an authoring tool which can pull data with provenance from the workflow tool in the appropriate representation into the document.
Technology # 3: Workflow integration
Results
Logs
Results
Metadata PaperSlides
Workflow 16
Workflow 13
Common pathways
QTL(C) Dave De Roure
Technology # 3: Workflow integration
Results
Logs
Results
Metadata PaperSlides
Workflow 16
Workflow 13
Common pathways
QTL(C) Dave De Roure
Technology # 3: Workflow integration
Results
Logs
Results
Metadata PaperSlides
Feeds into
produces
Included in
produces Published in
produces
Included in
Included in Included in
Published in
Workflow 16
Workflow 13
Common pathways
QTL(C) Dave De Roure
Technology # 3: Workflow integration
19
Three Tools
Tool # 1: DOMEO annotation toolhttp://purl.org/swan/af e.g. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2224208/?tool=pubmed
Paolo Ciccarese, Marco Ocana, Tim Clark, DOMEO: a web-based tool for semantic annotation of
online documents, Bioontologies, 2011
- Allows for manual and automated annotation, or both- Now linked to NCBO text mining tool, expanding to all UIMA- Standoff annotations in Annotation Ontology = RDF format, can be exported
Tool # 1: DOMEO annotation toolhttp://purl.org/swan/af e.g. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2224208/?tool=pubmed
Paolo Ciccarese, Marco Ocana, Tim Clark, DOMEO: a web-based tool for semantic annotation of
online documents, Bioontologies, 2011
- Allows for manual and automated annotation, or both- Now linked to NCBO text mining tool, expanding to all UIMA- Standoff annotations in Annotation Ontology = RDF format, can be exported
Tool # 1: DOMEO annotation toolhttp://purl.org/swan/af e.g. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2224208/?tool=pubmed
Paolo Ciccarese, Marco Ocana, Tim Clark, DOMEO: a web-based tool for semantic annotation of
online documents, Bioontologies, 2011
Tool # 2: Linked Data Repository
Dublin Core and SKOS
Tool # 2: Linked Data Repository
SWAN’s PAV (Provenance, Authoring and Versioning) ontology
Dublin Core and SKOS
Tool # 2: Linked Data Repository
Tool # 3: ScienceDirect app store
Tool # 3: ScienceDirect app store
- Eclipse SDK platform accessing all ScienceDirect/Scopus content
-Build applications on top of content-Offer to users in marketplace
What next?
23
Force11 http://force11.orgForce11 = Future of Research Communication and e-Scholarship, 2011 is a community of scholars, librarians, archivists, publishers and research funders that has arisen organically to help facilitate the change toward improved knowledge creation and sharing.
Force11 http://force11.orgForce11 = Future of Research Communication and e-Scholarship, 2011 is a community of scholars, librarians, archivists, publishers and research funders that has arisen organically to help facilitate the change toward improved knowledge creation and sharing.
Force11 http://force11.org
- Individually and collectively, we aim to bring about a change in scholarly communication through the effective use of information technologies
- Next step: work on these issues.- We need more publishers on
board!
Force11 = Future of Research Communication and e-Scholarship, 2011 is a community of scholars, librarians, archivists, publishers and research funders that has arisen organically to help facilitate the change toward improved knowledge creation and sharing.
Some thoughts about the future:
Some thoughts about the future:
- Let’s think in terms of use cases, not technologies:
- Identify where knowledge exists, within and outside of the article
- Identify what the information needs are, and which components need to be connected
- Only if our content plays well with others does it get to stay in the game!
Some thoughts about the future:
- Let’s think in terms of use cases, not technologies:
- Identify where knowledge exists, within and outside of the article
- Identify what the information needs are, and which components need to be connected
- Only if our content plays well with others does it get to stay in the game!
- Work with scientists, grant agencies, libraries, software developers big and small and.... each other!
Some thoughts about the future:
- Let’s think in terms of use cases, not technologies:
- Identify where knowledge exists, within and outside of the article
- Identify what the information needs are, and which components need to be connected
- Only if our content plays well with others does it get to stay in the game!
- Work with scientists, grant agencies, libraries, software developers big and small and.... each other!
- For instance, let’s collectively look at enabling:
- Standoff annotation formats
- Research data and workflow standards/integration
- Claim-evidence networks and discourse annotation:
- Which discourse annotation schemes are most portable? Can they be applied to both full papers and abstracts? Can they be applied to texts in different domains and different genres (research papers, reviews, patents, etc)?
- How can we compare annotations, and how can we decide which features, approaches or techniques work best? What are the most topical use cases? How can we evaluate performance and what are the most appropriate tasks?
- What corpora are currently available for comparing and contrasting discourse annotation, and how can we improve and increase these?
- How applicable are these efforts for improving methods of publishing, detecting and correcting author's errors at the discourse level, or summarizing scholarly text? How close are we to implementing them at a production scale?
Thank you!
More information:
- Data2Semantics: http://www.data2semantics.org
- W3C group on Discourse Structure: http://www.w3.org/wiki/HCLSIG/SWANSIOC
- Executable Paper Challenge: http://www.executablepapers.com
- Parsing rhetoric: http://elsatglabs.com/labs/anita/
- Sapienta: http://www.sapientaproject.com/
- SciVerse: http://developer.sciverse.com
- Force11: http://force11.org
- DSSD2012: http://www.nactem.ac.uk/dssd/
Or contact me: Anita de Waard, [email protected]
- Tim Clark, Paolo Ciccarese, Harvard, Cambridge, USA
- Eduard Hovy, Gully Burns, Cartic Ramakrishnan, ISI/USC, Los Angeles, USA
- Phil Bourne, Maryann Martone, UCSD, USA
- Sophia Ananiadou, NaCTeM, Manchester, UK
- Dave DeRoure, Oxford eScience Center, UK
- Maria Liakata, EBI, Cambridge, UK
- Paul Groth, Frank van Harmelen, Vrije Universiteit, Amsterdam, Netherlands
- Henk Pander Maat, Ted Sanders, Universiteit Utrecht, Netherlands
- The Force11 members