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Linked Science at ISWC 2014 
Riva del Garda, Trentino, Italy 
19 October 2014 
Jodi Schneider, Paolo Ciccarese, 
Tim Clark...
Goal of this project 
Construct & maintain 
a knowledge base linking to evidence 
i.e. data, methods, materials 
where: 
•...
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 ...
Application domain: medication safety 
• Potential drug-drug interactions 
– 2+ drugs, where interaction is known to be po...
Drug information sources 
• Evidence is selected & assessed by editorial boards 
– MICROMEDEX, First DataBank, Q-DIPS 
• E...
Part of a larger effort 
• “Addressing gaps in clinically useful evidence on 
drug-drug interactions” 
• 4-year project, U...
Build on 3 things 
• Drug Interaction Knowledge Base [Boyce2007, 
Boyce2009] 
• Open Annotation Data Model [W3C2013] 
• Mi...
Drug Interaction Knowledge Base 
(DIKB) 
– Hand-constructed knowledge base 
– Safety issues when 2 drugs are taken togethe...
Drug Interaction Knowledge Base 
(DIKB) - Boyce 2007-2009 
– Hand-constructed knowledge base 
– Safety issues when 2 drugs...
DIKB supports queries about 
assertions & evidence: 
• Get all assertions that are supported by a 
U.S. FDA regulatory gui...
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 ci...
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 fo...
Goal of this project 
Construct & maintain 
a knowledge base linking to evidence 
i.e. data, methods, materials 
where: 
•...
Modeling strategy 
Construct & maintain 
a knowledge base linking to evidence 
i.e. data, methods, materials 
where: 
• Ea...
Modeling strategy 
Construct & maintain 
a knowledge base linking to evidence 
i.e. data, methods, materials 
where: 
• Ea...
Quotes integrated (MP using OA) 
http://purl.org/mp 
Clark, Ciccarese, Goble (2014) Micropublications: a semantic model fo...
Enhancing the DIKB with MP and OA 
1. Represent the overall argument of the paper 
– Support & challenge relationships 
– ...
Quote stored in OA, with link to source 
ex:annotation-1 
ex:body-1 ex:target-1 
Predicate Object 
rdf:type mp:Method 
rdf...
Quote stored in OA, with link to source 
ex:annotation-1 
ex:body-1 ex:target-1 
Predicate Object 
rdf:type mp:Method 
rdf...
New competency questions to answer 
1. Finding assertions and evidence 
• List all assertions that are not supported by ev...
New competency questions to answer 
3. Assessing the evidence 
• Which research group conducted the study used for 
eviden...
Modeling challenges 
• To date, MP has not been used to represent 
both unstructured text claims 
("escitalopram does not ...
Future work 
• NLP support: Create a pipeline for extracting 
potential drug-drug interaction (PDDI) mentions 
from scient...
Acknowledgements 
• Funding 
– ERCIM Alain Bensoussan fellowship Program 
under FP7/2007-2013, grant agreement 246016 
– N...
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interacti...
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interacti...
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interacti...
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interacti...
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interacti...
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interacti...
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interacti...
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interacti...
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interacti...
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interacti...
Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interacti...
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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

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Presentation of a paper at the ISWC 2014 Workshop on Linked Science 2014— Making Sense Out of Data (LISC2014) - at ISWC 2014 Riva de Garda, Italy, October 19

“Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base.” by Jodi Schneider, Paolo Ciccarese, Tim Clark and Richard D. Boyce.

Paper: http://jodischneider.com/pubs/lisc2014.pdf
Event:http://linkedscience.org/events/lisc2014/

Abstract:
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.

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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. 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. 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. 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. 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. 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. 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. 7. Build on 3 things • Drug Interaction Knowledge Base [Boyce2007, Boyce2009] • Open Annotation Data Model [W3C2013] • Micropublications Ontology [Clark2014]
  8. 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. 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. 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. 11. Evidence Entry Interface (2008) [Boyce2007, Boyce2009]
  12. 12. Evidence Entry Interface (2008)
  13. 13. Evidence Entry Interface (2008)
  14. 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. 15. Open Annotation Data Model http://www.openannotation.org/spec/core/
  16. 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. 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. 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. 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. 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. 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
  22. 22. 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
  23. 23. 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)
  24. 24. 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
  25. 25. 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
  26. 26. 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.
  27. 27. 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
  28. 28. 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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