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Linked Science at ISWC 2014 
Riva del Garda, Trentino, Italy 
19 October 2014 
Jodi Schneider, Paolo Ciccarese, 
Tim Clark and Richard D. Boyce
Goal of this project 
Construct & maintain 
a knowledge base linking to evidence 
i.e. data, methods, materials 
where: 
• Each ASSERTION in the knowledge base 
has a SUPPORT GRAPH of claims and evidence 
• Each SUPPORT GRAPH element (claims, data, methods, materials) 
is dynamically linked to specific QUOTED 
ELEMENTS in source documents on the Web
Why? It's time-consuming to find 
the state of the art in a field! 
• What do we know about field F? assertion X? 
• What evidence supports assertion X? 
• What assumptions are used in research 
supporting assertion X?
Application domain: medication safety 
• Potential drug-drug interactions 
– 2+ drugs, where interaction is known to be possible 
• Adverse drug event 
– Harm caused by medication 
– Huge public health issue 
> 1.5 million preventable adverse drug events/year 
(USA) 
• Post-market safety issues
Drug information sources 
• Evidence is selected & assessed by editorial boards 
– MICROMEDEX, First DataBank, Q-DIPS 
• E.g. MICROMEDEX: 
– "In-house team of 90+ clinically-trained editorial staff" 
(physicians, clinical pharmacists, nurses, medical librarians) 
– "Content is reviewed for clinical accuracy and relevance." 
– "Critical content areas may undergo an additional review by 
members of our Editorial Board." 
• Potential problems 
– a time-consuming (i.e. expensive), collaborative, process 
– maintaining internal and external inconsistency is non-trivial
Part of a larger effort 
• “Addressing gaps in clinically useful evidence on 
drug-drug interactions” 
• 4-year project, U.S. National Library of Medicine 
R01 grant (PI, Richard Boyce) 
• Evidence panel of domain experts 
(Carol Collins, Lisa Hines, John R Horn, Phil Empey) 
& informaticists 
(Tim Clark, Paolo Ciccarese, Jodi Schneider) 
• Programmer: Yifan Ning
Build on 3 things 
• Drug Interaction Knowledge Base [Boyce2007, 
Boyce2009] 
• Open Annotation Data Model [W3C2013] 
• Micropublications Ontology [Clark2014]
Drug Interaction Knowledge Base 
(DIKB) 
– Hand-constructed knowledge base 
– Safety issues when 2 drugs are taken together 
– Focus is on EVIDENCE 
[Boyce2007, Boyce2009]
Drug Interaction Knowledge Base 
(DIKB) - Boyce 2007-2009 
– Hand-constructed knowledge base 
– Safety issues when 2 drugs are taken together 
– Focus is on EVIDENCE 
All assumptions are linked to evidence 
Enables the [Boyce2007, system Boyce2009] 
to identify when 
assumptions are no longer valid
DIKB supports queries about 
assertions & evidence: 
• Get all assertions that are supported by a 
U.S. FDA regulatory guidance statement 
• Are the evidence use assumptions are 
concordant, unique, and non-ambiguous? 
• Which assertions are supported/refuted by 
just one type of evidence? 
[Boyce2007, Boyce2009]
Evidence Entry Interface (2008) 
[Boyce2007, Boyce2009]
Evidence Entry Interface (2008)
Evidence Entry Interface (2008)
Limitations of DIKB v1.2 
• Cannot link quotes dynamically to source text 
– Document-level citation 
– Quote & section citation preferable 
• Level of detail 
– Want more detail on data, methods, materials 
• Minimal argumentation model 
– swanco:citesAsSupportingEvidence 
– swanco:citesAsRefutingEvidence 
[Boyce2007, Boyce2009]
Open Annotation Data Model 
http://www.openannotation.org/spec/core/
Micropublications Ontology (MP) 
http://purl.org/mp 
Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications
Goal of this project 
Construct & maintain 
a knowledge base linking to evidence 
i.e. data, methods, materials 
where: 
• Each ASSERTION in the knowledge base 
has a SUPPORT GRAPH of claims and evidence 
• Each SUPPORT GRAPH element (claims, data, methods, materials) 
is dynamically linked to specific QUOTED 
ELEMENTS in source documents on the Web
Modeling strategy 
Construct & maintain 
a knowledge base linking to evidence 
i.e. data, methods, materials 
where: 
• Each ASSERTION in the knowledge base 
has a SUPPORT GRAPH of claims and evidence: MP 
• Each SUPPORT GRAPH element (claims, data, methods, materials) 
is dynamically linked to specific QUOTED 
ELEMENTS in source documents on the Web
Modeling strategy 
Construct & maintain 
a knowledge base linking to evidence 
i.e. data, methods, materials 
where: 
• Each ASSERTION in the knowledge base 
has a SUPPORT GRAPH of claims and evidence: MP 
• Each SUPPORT GRAPH element (claims, data, methods, materials) 
is dynamically linked to specific QUOTED 
ELEMENTS in source documents on the Web: OA
Quotes integrated (MP using OA) 
http://purl.org/mp 
Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications
Enhancing the DIKB with MP and OA 
1. Represent the overall argument of the paper 
– Support & challenge relationships 
– Data, methods, materials 
2. Semantic tagging, so drugs & proteins can be 
queried using knowledge from other sources 
3. Make quotes actionable (highlight in orig doc) 
4. Handle new competency questions
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base--LISC2014--2014-10-19
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base--LISC2014--2014-10-19
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base--LISC2014--2014-10-19
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base--LISC2014--2014-10-19
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base--LISC2014--2014-10-19
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base--LISC2014--2014-10-19
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base--LISC2014--2014-10-19
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base--LISC2014--2014-10-19
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base--LISC2014--2014-10-19
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base--LISC2014--2014-10-19
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base--LISC2014--2014-10-19
Quote stored in OA, with link to source 
ex:annotation-1 
ex:body-1 ex:target-1 
Predicate Object 
rdf:type mp:Method 
rdf:value (exact text) 
about 
Predicate Object 
rdf:type oa:SpecificResource 
oa:hasSource <http://dailymed…> 
oa:hasSelector ex:selector-1
Quote stored in OA, with link to source 
ex:annotation-1 
ex:body-1 ex:target-1 
Predicate Object 
rdf:type mp:Method 
rdf:value (exact text) 
about 
Predicate Object 
rdf:type oa:SpecificResource 
oa:hasSource <http://dailymed…> 
oa:hasSelector ex:selector-1 
ex:selector-1 
Predicate Object 
oa:prefix (preceding text) 
oa:exact (exact text) 
oa:postfix (following text)
New competency questions to answer 
1. Finding assertions and evidence 
• List all assertions that are not supported by evidence 
– By data, by methods, by materials 
• What is the in vitro evidence for assertion X? the in vivo 
evidence? 
– With provenance: Give me back the original data tables 
2. Enabling updates 
• List all evidence that has been flagged as rejected from 
entry into the knowledge base 
– By data, by methods, by materials
New competency questions to answer 
3. Assessing the evidence 
• Which research group conducted the study used for 
evidence item X? 
• What are the assumptions required for use of this 
evidence item to support/refute assertion X? 
– Without directly entering them 
4. Statistics for analytics/KB maintenance 
• Number of evidence items for and against each 
assertion type 
– By data, by methods, by materials
Modeling challenges 
• To date, MP has not been used to represent 
both unstructured text claims 
("escitalopram does not inhibit CYP2D6") 
and logical representation of text as 
normalized subject-predicate-object 
(nanopublication of statement) 
• Efficient querying will be needed, even when 
the evidence base scales. We are using an 
iterative design-and-test approach.
Future work 
• NLP support: Create a pipeline for extracting 
potential drug-drug interaction (PDDI) mentions 
from scientific & clinical literature 
• Usability tests: Tools usable by domain experts 
• NLP + "crowdsourcing" (distributed annotation) 
• Resolving links to paywalled PDFs
Acknowledgements 
• Funding 
– ERCIM Alain Bensoussan fellowship Program 
under FP7/2007-2013, grant agreement 246016 
– National Library of Medicine (1R01LM011838-01) 
• Thanks to the Evidence Panel of Addressing 
PDDI Evidence Gaps: Carol Collins, Lisa Hines, 
and John R Horn, Phil Empey 
• Thanks to programmer Yifan Ning

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Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base--LISC2014--2014-10-19

  • 1. Linked Science at ISWC 2014 Riva del Garda, Trentino, Italy 19 October 2014 Jodi Schneider, Paolo Ciccarese, Tim Clark and Richard D. Boyce
  • 2. Goal of this project Construct & maintain a knowledge base linking to evidence i.e. data, methods, materials where: • Each ASSERTION in the knowledge base has a SUPPORT GRAPH of claims and evidence • Each SUPPORT GRAPH element (claims, data, methods, materials) is dynamically linked to specific QUOTED ELEMENTS in source documents on the Web
  • 3. Why? It's time-consuming to find the state of the art in a field! • What do we know about field F? assertion X? • What evidence supports assertion X? • What assumptions are used in research supporting assertion X?
  • 4. Application domain: medication safety • Potential drug-drug interactions – 2+ drugs, where interaction is known to be possible • Adverse drug event – Harm caused by medication – Huge public health issue > 1.5 million preventable adverse drug events/year (USA) • Post-market safety issues
  • 5. Drug information sources • Evidence is selected & assessed by editorial boards – MICROMEDEX, First DataBank, Q-DIPS • E.g. MICROMEDEX: – "In-house team of 90+ clinically-trained editorial staff" (physicians, clinical pharmacists, nurses, medical librarians) – "Content is reviewed for clinical accuracy and relevance." – "Critical content areas may undergo an additional review by members of our Editorial Board." • Potential problems – a time-consuming (i.e. expensive), collaborative, process – maintaining internal and external inconsistency is non-trivial
  • 6. Part of a larger effort • “Addressing gaps in clinically useful evidence on drug-drug interactions” • 4-year project, U.S. National Library of Medicine R01 grant (PI, Richard Boyce) • Evidence panel of domain experts (Carol Collins, Lisa Hines, John R Horn, Phil Empey) & informaticists (Tim Clark, Paolo Ciccarese, Jodi Schneider) • Programmer: Yifan Ning
  • 7. Build on 3 things • Drug Interaction Knowledge Base [Boyce2007, Boyce2009] • Open Annotation Data Model [W3C2013] • Micropublications Ontology [Clark2014]
  • 8. Drug Interaction Knowledge Base (DIKB) – Hand-constructed knowledge base – Safety issues when 2 drugs are taken together – Focus is on EVIDENCE [Boyce2007, Boyce2009]
  • 9. Drug Interaction Knowledge Base (DIKB) - Boyce 2007-2009 – Hand-constructed knowledge base – Safety issues when 2 drugs are taken together – Focus is on EVIDENCE All assumptions are linked to evidence Enables the [Boyce2007, system Boyce2009] to identify when assumptions are no longer valid
  • 10. DIKB supports queries about assertions & evidence: • Get all assertions that are supported by a U.S. FDA regulatory guidance statement • Are the evidence use assumptions are concordant, unique, and non-ambiguous? • Which assertions are supported/refuted by just one type of evidence? [Boyce2007, Boyce2009]
  • 11. Evidence Entry Interface (2008) [Boyce2007, Boyce2009]
  • 14. Limitations of DIKB v1.2 • Cannot link quotes dynamically to source text – Document-level citation – Quote & section citation preferable • Level of detail – Want more detail on data, methods, materials • Minimal argumentation model – swanco:citesAsSupportingEvidence – swanco:citesAsRefutingEvidence [Boyce2007, Boyce2009]
  • 15. Open Annotation Data Model http://www.openannotation.org/spec/core/
  • 16. Micropublications Ontology (MP) http://purl.org/mp Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications
  • 17. Goal of this project Construct & maintain a knowledge base linking to evidence i.e. data, methods, materials where: • Each ASSERTION in the knowledge base has a SUPPORT GRAPH of claims and evidence • Each SUPPORT GRAPH element (claims, data, methods, materials) is dynamically linked to specific QUOTED ELEMENTS in source documents on the Web
  • 18. Modeling strategy Construct & maintain a knowledge base linking to evidence i.e. data, methods, materials where: • Each ASSERTION in the knowledge base has a SUPPORT GRAPH of claims and evidence: MP • Each SUPPORT GRAPH element (claims, data, methods, materials) is dynamically linked to specific QUOTED ELEMENTS in source documents on the Web
  • 19. Modeling strategy Construct & maintain a knowledge base linking to evidence i.e. data, methods, materials where: • Each ASSERTION in the knowledge base has a SUPPORT GRAPH of claims and evidence: MP • Each SUPPORT GRAPH element (claims, data, methods, materials) is dynamically linked to specific QUOTED ELEMENTS in source documents on the Web: OA
  • 20. Quotes integrated (MP using OA) http://purl.org/mp Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications
  • 21. Enhancing the DIKB with MP and OA 1. Represent the overall argument of the paper – Support & challenge relationships – Data, methods, materials 2. Semantic tagging, so drugs & proteins can be queried using knowledge from other sources 3. Make quotes actionable (highlight in orig doc) 4. Handle new competency questions
  • 33. Quote stored in OA, with link to source ex:annotation-1 ex:body-1 ex:target-1 Predicate Object rdf:type mp:Method rdf:value (exact text) about Predicate Object rdf:type oa:SpecificResource oa:hasSource <http://dailymed…> oa:hasSelector ex:selector-1
  • 34. Quote stored in OA, with link to source ex:annotation-1 ex:body-1 ex:target-1 Predicate Object rdf:type mp:Method rdf:value (exact text) about Predicate Object rdf:type oa:SpecificResource oa:hasSource <http://dailymed…> oa:hasSelector ex:selector-1 ex:selector-1 Predicate Object oa:prefix (preceding text) oa:exact (exact text) oa:postfix (following text)
  • 35. New competency questions to answer 1. Finding assertions and evidence • List all assertions that are not supported by evidence – By data, by methods, by materials • What is the in vitro evidence for assertion X? the in vivo evidence? – With provenance: Give me back the original data tables 2. Enabling updates • List all evidence that has been flagged as rejected from entry into the knowledge base – By data, by methods, by materials
  • 36. New competency questions to answer 3. Assessing the evidence • Which research group conducted the study used for evidence item X? • What are the assumptions required for use of this evidence item to support/refute assertion X? – Without directly entering them 4. Statistics for analytics/KB maintenance • Number of evidence items for and against each assertion type – By data, by methods, by materials
  • 37. Modeling challenges • To date, MP has not been used to represent both unstructured text claims ("escitalopram does not inhibit CYP2D6") and logical representation of text as normalized subject-predicate-object (nanopublication of statement) • Efficient querying will be needed, even when the evidence base scales. We are using an iterative design-and-test approach.
  • 38. Future work • NLP support: Create a pipeline for extracting potential drug-drug interaction (PDDI) mentions from scientific & clinical literature • Usability tests: Tools usable by domain experts • NLP + "crowdsourcing" (distributed annotation) • Resolving links to paywalled PDFs
  • 39. Acknowledgements • Funding – ERCIM Alain Bensoussan fellowship Program under FP7/2007-2013, grant agreement 246016 – National Library of Medicine (1R01LM011838-01) • Thanks to the Evidence Panel of Addressing PDDI Evidence Gaps: Carol Collins, Lisa Hines, and John R Horn, Phil Empey • Thanks to programmer Yifan Ning

Notes de l'éditeur

  1. 20 min including questions http://linkedscience.org/events/lisc2014/ Paper: http://jodischneider.com/pubs/lisc2014.pdf Semantic web technologies can support the rapid and transparent validation of scientific claims by interconnecting the assumptions and evidence used to support or challenge assertions. One important application domain is medication safety, where more efficient acquisition, representation, and synthesis of evidence about potential drug-drug interactions is needed. Exposure to potential drug-drug interactions (PDDIs), defined as two or more drugs for which an interaction is known to be possible, is a significant source of preventable drug-related harm. The combination of poor quality evidence on PDDIs, and a general lack of PDDI knowledge by prescribers, results in many thousands of preventable medication errors each year. While many sources of PDDI evidence exist to help improve prescriber knowledge, they are not concordant in their coverage, accuracy, and agreement. The goal of this project is to research and develop core components of a new model that supports more efficient acquisition, representation, and synthesis of evidence about potential drug-drug interactions. Two Semantic Web models—the Micropublications Ontology and the Open Annotation Data Model—have great potential to provide linkages from PDDI assertions to their supporting evidence: statements in source documents that mention data, materials, and methods. In this paper, we describe the context and goals of our work, propose competency questions for a dynamic PDDI evidence base, outline our new knowledge representation model for PDDIs, and discuss the challenges and potential of our approach.
  2. Create an audit trail between assertions, evidence, and source documents, so that assertions and evidence can be flagged for update in flexible and intelligent ways.
  3. Statistics from Institute of Medicine Report Brief, July 2006 on Preventing Medication Errors http://www.iom.edu/~/media/Files/Report%20Files/2006/Preventing-Medication-Errors-Quality-Chasm-Series/medicationerrorsnew.pdf
  4. http://micromedex.com/trusted-evidence http://micromedex.com/Portals/1/Assets/Brochures/EditorialGovernance_2013.pdf
  5. http://dbmi-icode-01.dbmi.pitt.edu/dikb-evidence/front-page.html
  6. http://dbmi-icode-01.dbmi.pitt.edu/dikb-evidence/front-page.html
  7. # How many pharmacokinetic studies in the DIKB could be used to support or refute # an increases AUC assertion? # How many AUC studies are in the DIKB that are based on data from the # product label? # Are the evidence use assumptions are concordant, unique, and non-ambiguous # Get all assertions that are supported by an FDA guidance statement # SHOW THE DISTRIBUTION OF THE LEVELS OF EVIDENCE FOR MECHANISTIC ASSERTIONS # WHAT ASSERTIONS ARE SUPPORTED/REFUTED BY JUST ONE TYPE OF EVIDENCE? ## which items in the DIKB have evidence for and against that is both ## from product labeling? # number of assertions in the system # number of evidence items for and against ## find assertions that are not supported by evidence # what single evidence items act as as support or rebuttal for multiple substrate_of assertions? #remove a older version of a redundant evidence item ## change evidence for to evidence against # has this evidence item been rejected # what other assertions are being supported/challeged by this evidnece item?
  8. Annotations in the data model are a set of RDF resources that connect some target to a set of resources that are in some way about it.
  9. Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications. J. Biomedical Semantics 5: 28 (2014)
  10. Create an audit trail between assertions, evidence, and source documents, so that assertions and evidence can be flagged for update in flexible and intelligent ways.
  11. Create an audit trail between assertions, evidence, and source documents, so that assertions and evidence can be flagged for update in flexible and intelligent ways.
  12. Create an audit trail between assertions, evidence, and source documents, so that assertions and evidence can be flagged for update in flexible and intelligent ways.
  13. Clark, Ciccarese, Goble (2014) Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications. J. Biomedical Semantics 5: 28 (2014)
  14. New competency questions handled: Assertions that are not supported by evidence already in the evidence base, the evidence that should be checked most thoroughly (e.g. evidence that by itself supports multiple assertions), and the data, methods, and materials associated with a given evidence item as described in source documents. 1. Finding assertions: (a)  List all assertions that are not supported by evidence (b)  Which assertions are supported (or refuted) by just one type of evidence? (c)  Which assertions have evidence from source X (e.g., product labeling) (d)  Which assertions have both evidence for and evidence against from a single source X? 2. Finding evidence: (a) List all evidence for or against assertion X (by evidence type, drug, drug 4.2 pair, transporter, metabolic enzyme, etc.) (b)  What is the in vitro evidence for assertion X? the in vivo evidence? (c)  List all evidence that has been flagged as rejected from entry into the the knowledge base (d)  Which single evidence items act as support or rebuttal for multiple as- sertions of type X (e.g., substrate of assertions)? Assessing the evidence: 3. Understanding evidence coming from a given study: (a)  What data, methods, materials, are reported in evidence item X? (b)  Which evidence items are related to and follow-up on evidence item X? (c)  Which research group conducted the study used for evidence item X? (d)  AretheevidenceuseassumptionsforevidenceitemXconcordant?unique? non-ambiguous? 4.3 Verifying plausibility of an evidence item: (a)  Has evidence item X been rejected for assertion Y? If so, why and by whom? (b)  Which other assertions are being supported/challenged by this evidence item? (c)  What are the assumptions required for use of this evidence item to sup- port/refute assertion X? Checking assertions about pharmacokinetic parameters (i.e., area under the concentration time curve (AUC)) (a)  How many pharmacokinetic studies used for evidence items in the DIKB could be used to support or refute an assertion about pharmacokinetic paramater X (e.g., ‘X increases AUC’)? (b)  How many pharmacokinetic studies in the DIKB used for evidence items for assertion X are based on data from the product label? (c)  What is the result of averaging (or applying some other statistical oper- ation) to the values for pharmacokinetic parameter X across all relevant studies used for evidence items? Checking for differences in the product labeling: (a)  Are there differences in the evidence items that were identified across different versions of product labeling for the same drug? (b)  What version of product labeling was used for evidence item X? Original manufacturer or repackager? Most current label or outdated? Is the drug on market in country X or not? American or country X? Supporting updates to evidence and assertions 1. Changing status of redundant and refuted evidence: (a)  Remove a older version of a redundant evidence item (b)  Change the modality of a supporting evidence item to be a refuting evidence item 2. Updating when key sources change: (a) Get all assertions that are supported by evidence items identified from an FDA guidance or other source document just released as an updated version. 4.4 Understanding the evidence base 1. Statistical information about the evidence base: (a)  Number of assertions in the system (b)  Number of evidence items for and against each assertion type (c)  Show the distribution of the levels of evidence for various assertion types (e.g., pharmacokinetic assertions)
  15. strength
  16. Software responsive
  17. For adding annotations: Existing MP plugin for Domeo For viewing annotations: Want them highlighted in a web-based interface BUT Resolving annotations requires a method for pointing to paywalled/subscription PDF & HTML An existing Micropublication plugin for Domeo [Ciccarese2014] is being mod- ified as part of the project. Our plan is to use the revised plugin to support the evidence board with the collection of the evidence and associated annotation data. It will also enable the broader community to access and view annotations of PDDIs highlighted in a web-based interface. We anticipate that this approach will enable a broader community of experts to review each PDDI recorded in the DIKB and examine the underlying research study to confirm its appropriateness and relevance to the evidence base. The usability of the annotation plug-in is critically important so that the panel of domain experts will not face barriers to annotating and entering ev- idence. This will require usability studies of the new PDDI Micropublication plugin. Another issue is that many PDDI evidence items can be found only in PDF documents. Currently, the tool chain for PDF annotation is relatively weak: compared to text and HTML, PDF annotation tools are not as widely available and not as familiar to end-users. Suitable tools will have to be integrated into the revised plugin. PDF documents may be in proprietary portals or academic library systems