=Paper=
{{Paper
|id=Vol-1428/BDM2I_2015_paper_10
|storemode=property
|title=Formalizing Knowledge and Evidence about Potential Drug-drug Interactions
|pdfUrl=https://ceur-ws.org/Vol-1428/BDM2I_2015_paper_10.pdf
|volume=Vol-1428
|dblpUrl=https://dblp.org/rec/conf/semweb/SchneiderBRCHMN15
}}
==Formalizing Knowledge and Evidence about Potential Drug-drug Interactions==
<pdf width="1500px">https://ceur-ws.org/Vol-1428/BDM2I_2015_paper_10.pdf</pdf>
<pre>
       Formalizing knowledge and evidence about
           potential drug-drug interactions

    Jodi Schneider1 , Mathias Brochhausen2 , Samuel Rosko1 , Paolo Ciccarese36 ,
        William R. Hogan4 , Daniel Malone5 , Yifan Ning1 , Tim Clark6 , and
                                Richard D. Boyce1
           1
              Department of Biomedical Informatics, University of Pittsburgh
                         jos188, scr25, yin2, rdb20@pitt.edu
2
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences
                                mbrochhausen@uams.edu
            3
               Innovation Lab at PerkinElmer, paolo.ciccarese@gmail.com
                        4
                          University of Florida, hoganwr@ufl.edu
  5
     College of Pharmacy, The University of Arizona, malone@pharmacy.arizona.edu
             6
                Massachusetts General Hospital and Harvard Medical School
                                tim clark@harvard.edu



        Abstract. Potential drug-drug interactions (PDDI) are a significant
        source of preventable drug-related harm. One contributing factor is that
        there is no standard way to represent PDDI knowledge claims and asso-
        ciated evidence in a computable form. The research we present in this
        paper addresses this problem by creating a new version of the Drug In-
        teraction Knowledge Base, with scalable, interlinkable repositories for
        PDDI evidence and PDDI knowledge claims.

        Keywords: Linked Data, drug-drug interactions, evidence bases, Mi-
        cropublications, Nanopublications, knowledge bases


1     Introduction
A challenging area of focus for patient safety is the management of potential
drug-drug interactions (PDDIs). These are defined as co-prescription or co-
administration of two drugs known to interact, which potentially exposes the
patient to adverse drug events [9]. PDDIs are a significant source of preventable
drug-related harm: according to a recent review, clinically important events at-
tributable to PDDI exposure occur in 5.3% to 14.3% of inpatients, and are re-
sponsible for 0.02% to 0.17% of the 129 million emergency department visits that
occur each year [12].1 Unfortunately, most drug information sources disagree sub-
stantially in their guidance about specific PDDIs [1, 16, 13, 2]. Addressing this
is urgent as United States healthcare organizations consider PDDI screening in
their strategies to achieve the effective use of electronic health records.


1
    http://www.cdc.gov/nchs/fastats/ervisits.htm
    There are both technical and social factors underlying the disagreement that
exists across drug information sources [14]. As Figure 1 shows, evidence that
might be relevant for establishing PDDI knowledge claims is distributed across
several sources including product labeling, the scientific literature, and case re-
ports. Each source provides complementary evidence that editors of drug infor-
mation resources (public sources [2] or proprietary sources such as Micromedex,
Epocrates, and Medscape) must synthesize. A major social factor underlying dis-
agreement is that drug information editors have different criteria for assessing
evidence. Fortunately, two different conference series have brought leading drug
information editors to discuss a standard set of methods for assessing evidence
[14, 10].




         Pre$market)studies                        Post$market)studies                 Clinical) experience

                              Rarely(reported(in

        Reported(in                            Reported(in        Rarely(reported(in


                                                   Scientific) literature
                                                                               Rarely(reported(in

           Product)labeling
                                                              Source(for
                      Source(for


                                 Drug)Compendia) )synthesize)PDDI)evidence)into)
                                 knowledge)claims but
                                 • May)fail)to)include) important) evidence
                                 • Disagree)if)specific) evidence)items) can)support)
                                   or)refute)PDDI)knowledge)claims


Fig. 1. Editors of drug information resources might seek evidence for or against poten-
tial drug-drug interactions from numerous sources. Different information is reported in
each type of source, making synthesis necessary.




    A major technical factor yet to be addressed is that there currently does
not exist a standard way to represent PDDI knowledge claims and associated
evidence in a computable form. As a result, drug information editors resort to
ad hoc information retrieval methods that can yield different sets of evidence
to assess [10]. The research we present in this paper addresses this problem by
creating scalable, interlinkable repositories for both PDDI evidence and PDDI
knowledge claims. This paper describes our new approach. In Section 2, we
outline requirements. In Section 3, we discuss the technical details. In Section 4,
we present a benchmark analysis that tests the ability of the new approach to
scale. After discussion, we conclude the paper.
2   Background and requirements

In prior work, we created the original Drug Interaction Knowledge Base (DIKB-
old) [3, 4]. The DIKB2 is an evidence-focused knowledge base designed to support
pharmacoepidemiology and clinical decision support. It contains quantitative
and qualitative knowledge claims about drug mechanisms and pharmacokinetic
drug-drug interactions for over 60 drugs.
    Prior work on the DIKB-old focused on development of an evidential ap-
proach representing the evidence associated with a scientific claim. The system
considers the evidence board as a socio-technical reasoning system that manages
both a knowledge base and an evidence base. The knowledge base holds PDDI
knowledge claims while the evidence base stores information artifacts that can
be used to support or challenge those claims. PDDI knowledge claims may be
direct (e.g., “drug X interacts with drug Y”); or inferred from pharmacological
properties (e.g.,“drug X inhibits enzyme Q which is important for the clearance
of drug Y from the body”).
    In prior work [3, 4], all evidence was collected and entered by an evidence
board consisting of an informaticist and a minimum of two drug-experts. The
board used the following process to manage the evidence and knowledge base
components:

1. All members of the board select drugs of interest. This determines the set
   of PDDI knowledge claims to be investigated.
2. The informaticist conducts a systematic search for evidence that might sup-
   port or refute the pre-determined PDDI knowledge claims.
3. Retrieved items are filtered by applying study inclusion criteria.
4. Evidence items that meet inclusion criteria are entered into the system where
   they are linked to specific PDDI knowledge claims and any evidence use
   assumptions (knowledge claims that must be true for the evidence to hold).
5. A truth value for each knowledge claim is determined based on belief criteria.

    Experience with the DIKB-old revealed a great need for improvements to the
system that would make this process more efficient. First, a substantial amount
of time was spent on reconciling and integrating information from various sources
(Figure 1). Decision rationales were not recorded in a computable form and the
evidence board did not have a process in place to keep up with relevant new
evidence. Furthermore, the DIKB-old was ontologically informal, failed to adopt
common biomedical ontology terms3 , and did not distinguish drug and enzyme
classes from individuals. This hindered automated reasoning that integrated ex-
ternal knowledge sources and resulted in treating PDDIs the same as observed
2
  When we do not need to distinguish between the old (‘DIKB-old’) and new (‘new
  DIKB’) versions, we simply mention ‘DIKB’.
3
  For example, the DIKB-old used the predicate ‘substrate of’ to represent the
  metabolic process of xenobiotic catalysis. However, this predicate was defined with-
  out reference to the formally defined biological process (e.g, such as that provided
  by the Gene Ontology).
           drug-drug interactions. In the new system we wished to resolve these issues. We
           also wished to retain the ability to compute with a logical representation of drug
           mechanism knowledge claims, using a rule-based theory of how to infer PDDIs
           from metabolic mechanistic knowledge of how drugs interact [4].
               We summarize these as three requirements for the new DIKB:

           R1 Create a maintainable structure that supports evidence entry of data, meth-
              ods, and materials from multiple sources on an ongoing basis.
           R2 Create computable, logical representations of drug mechanism knowledge
              claims.
           R3 Link to biological processes while also carefully distinguishing between a
              drug drug interaction (an actual occurrence in a patient) and a potential
              drug drug interaction (an information content entity that may exist because
              of an observation or inference).

                 Our approach to addressing these criteria are as follows:

Addressing R1 We adopt the emerging Micropublications (MP) [6] model for literature in-
              tegration using ‘argument graphs’ to represent published claims as formal
              assertions linked to primary data and resources.
Addressing R2 We extended the MP ontology to add two new properties,
              MP:formalizedAs/MP:formalizes, to enable natural language claims
              to be linked to useful logical formalizations.
Addressing R3 To stress that potential drug drug interactions are information artifacts, we
              use a new ontology called DIDEO [5] which has several advantages. DIDEO:
              (a) Reuses identifiers from existing ontologies (e.g., CHEBI, PRO) that rep-
                  resent biological entities and processes;
              (b) Differentiates between the representation (statements about drugs and
                  drug-drug interactions) and the represented (actual drug-drug interac-
                  tions);
              (c) Prevents unwanted existential import (further explained in Section 3.3
                  below); and
              (d) Distinguishes between the type of a drug or enzyme and portions of a
                  specific drug or enzyme, by using punning.


           3      Technical implementation

           3.1     Create a maintainable structure that supports evidence entry
                   of data, methods, and materials from multiple sources

           We used micropublications to create a structure that supports evidence entry of
           data, methods, and materials from multiple sources [15]. We now represent PDDI
           knowledge claims and supporting evidence as queryable RDF statements4 con-
           structed using the Micropublication ontology (MP) [6]. PDDI knowledge claims
            4
                Queryable at http://purl.org/net/nlprepository/swat-4-med-safety-sparql-
                endpoint
and evidence were transformed from the DIKB-old model into the new one using
Python scripts. Drug identifiers were converted to ChEBI identifiers to enable
the use of DIDEO. So far, the mapping has been completed for 70% of the drugs
that had data from clinical studies or mechanistic experiments. We envision
that additional DIKB micropublications could be created by multiple parties,
including evidence boards, and potentially the original authors, as we describe
in Section 5.
    Figure 2 shows the generic form of a DIKB micropublication graph using
the example erythromycin - simvastatin interaction. Notice that MP has rigor-
ously defined ontology classes that support the DIKB evidence curation process
discussed above. In MP, the primary object of interest is the claim. A claim is
supported by methods, materials, and data:
 – MP:Claim, a text string representing a scientific claim.
 – MP:Method, representing a scientific method.
 – MP:Materials, for materials, such as study participants and drugs.
 – MP:Data, such as the area under the concentration curve (AUC).
These are used for entering evidence and later, the evidence is used to determine
truth values for the claims that the evidence supports.


           obo:CHEBI_48923            obo:CHEBI_9150                obo:DIDEO_00000000

                  mp:qualifiedBy      mp:qualifiedBy       mp:qualifiedBy



            MP     mp:argues
             1                       Claim
                                                 erythromycin increases the AUC of simvastatin
                                       1

                       mp:supports

                                       Data
                                        1
                                                                      mp:supports
                               mp:supports
                                                                                      http://dx.doi.org/
                                                 Method
                                                                                      10.1016/
                                                   1
                                                                                      S0009-9236(98)90151-5
                                         mp:supports
                                                              Materials
                                                                 1



Fig. 2. DIKB micropublication graph for the erythromycin - simvastatin interaction.


    The process for managing the evidence base and knowledge base described
in Section 2 includes assessing the truth value of each PDDI knowledge claim us-
ing belief criteria. Operationally, the evidence board uses labels from a taxonomy
of evidence types5 to tag each evidence item as it is entered into the evidence
base. The board then decides which evidence types are credible for specific types
of PDDI knowledge claims: this specifies a belief criterion.
    As an example, the evidence board might decide that, to support a claim that
a drug is a substrate of an enzyme, only clinical drug-drug interaction studies
5
    http://purl.org/net/drug-interaction-knowledge-base/evidence-types-
    and-inclusion-criteria
are admissible. This would become a belief criterion for all ‘substrate of’ claims.
To implement a belief criterion in the new DIKB, the evidence base is queried
to find all PDDI knowledge claims that have at least one supporting evidence
item meeting the criterion. The resulting claims are assigned the value of ‘True’.

3.2    Create computable, logical representations
PDDI knowledge claims mention specific entities such as drugs, drug metabolites,
enzymes, and biological pathways whose relationships with each other are more
generally modeled in a rule-based theory that infers PDDIs [4]. Sources external
to the DIKB provide additional formalized knowledge about these entities. For
example, the Gene Ontology provides cellular location and molecular function for
the enzyme CYP3A4; this is relevant when the evidence board seeks information
about gene expression and about enzyme metabolization.
    The spans of unstructured text in MP:Claim are not inherently computable
entities, and the semantic qualifiers (MP:qualifiedBy) cannot specify the order
(i.e. separate the object drug from the precipitant drug). Therefore, we extended
the MP ontology to add two new properties, MP:formalizedAs/MP:formalizes,
that enable natural language claims represented as MP:Claim to be linked to their
logical representation.
    RDF is also the language chosen for the formalization of MP:Claim resources,
so that a single query language (i.e., SPARQL) can be used to retrieve infor-
mation from the whole evidence base. We chose to represent the logical form of
knowledge claims using OWL for two reasons. First, OWL provides classes and
properties that enable the representation of logical statements in RDF. Second,
logical statements written in OWL can be checked for logical consistency and
new inferences by a reasoner such as Hermit [7].
    We chose to formalize claims using the Nanopublication (NP) [8] ontology
because:
 1. NP provides a class called NP:Assertion that can hold any RDF graph,
    including logical statements written in OWL.
 2. OWL logical statements stored as an NP:Assertion can be integrated into
    full nanopublications that combine the NP:Assertion, the provenance of the
    assertion, and the provenance of the nanopublication into a single publishable
    and citable entity.
    A nanopublication represents the logical structure of a claim as an RDF
graph. Like micropublications, nanopublications are publishable and citable enti-
ties. Their citability and use of provenance enable us to make the evidence review
process transparent and auditable. The uptake of nanopublications by the wider
community suggests that nanopublication is a relevant publishing mechanism for
reconsumption by others.6 Unlike micropublications, nanopublications have no
6
    One measure of uptake is the variety of authors of papers using nanopublications;
    see the bibliography at http://nanopub.org/wordpress/?page_id=638. Another is
    the size and geographic distribution of current nanopublication datasets: see [11]
    Table 1 and Figure 3, respectively.
explicit evidence structure and do not support claim conflict. They are therefore
complementary to micropublications, which provide these missing features.


3.3    Handling reasonable extrapolation

Reasonable extrapolation is an important way to infer a PDDI. In contrast to
drug-drug interactions (DDIs) that are based on observing an actual drug-drug
interaction in some patient, inferred PDDIs based on reasonable extrapolation
might not actually occur in reality. Since we do not know whether a PDDI occurs,
we cannot assume the existence of any instance of a drug interaction. To model
this correctly, we differentiate between actual drug interactions and statements
about PDDIs using the DIDEO ontology [5].


                                                                 catalyzes6a6Phase6I6or6
                   molecularly6                                  Phase6II6enzymatic6
   erythromycin    decreases6activity         CYP3A4             reaction6involving          simvastatin

 obo:CHEBI_48923   obo:RO_0002449       obo:PR_000006130         obo:DIDEO_00000096        obo:CHEBI_9150




                                        inhibits<catalyzes6 metabolism6 of
                                             obo:DIDEO_00000090


Fig. 3. OWL inference (dashed red arrow) of a potential drug drug interaction between
erythromycin and simvastatin based on assertions in the evidence base.


    Let us again consider erythromycin and simvastatin. Previously (Figure 2) we
described a clinical study establishing this PDDI. We now reconsider this drug
pair (e.g. in the absence of that clinical evidence), to show how a computable
structure and OWL inferencing allow the new DIKB to compute a PDDI by
reasonable extrapolation, as shown in Figure 3.
    Assume that credible evidence also establishes that erythromycin inhibits
the catalysis of CYP3A4 which is important to the metabolic clearance of sim-
vastatin. We represent this relationship in the evidence base by creating OWL
statements representing the following two knowledge claims (Figure 3): “ery-
thromycin moleculary decreases activity of CYP3A4” and “CYP3A4 catalyzes
a Phase I or Phase II enzymatic reaction involving simvastatin”. These claims
are linked to the relevant evidence support using MP as discussed above, and
formalized by two different NP:Assertion resources using RDF/OWL. If the
evidence support meets the belief criteria specified by the evidence board, full
nanopublications are created for the formalized claims. These are then passed to
an OWL reasoner that would infer a relation between erythromycin and simvas-
tatin called inhibits-catalyses metabolism. This inference generates a new entity,
namely an individual PDDI, based on the inferred statement, as shown on the left
side of Figure 4. This individual resides in the knowledge base. The knowledge
base contains other individuals based on inferred statements; it also contains
entities that represent individual PDDIs based on data items created by clinical
studies, clinical observation or physiological experiments. The key point here is
that the inferred individuals do not refer to any class of specific drug-drug in-
teractions, and so they do not imply the existence of at least one member of the
class (known as existential import).


                   Evidence(Base                                                                        Knowledge(Base




                                                }
          "erythromycin,inhibits,CYP3A4."                                                    "Statement, A,is,about,
                                                                                             {erythromycin}."
          "CYP3A4,catalyzes, a,Phase, I,or,                           OWL                    "Statement, A,is,about,{simvastatin}."
          Phase, II,enzymatic,reaction,                               inference
          involving,simvastatin."                                                            "Statement, A,is,about,{CYP3A4}."
                                                                                             "Statement, A,is,a,PDDI,statement."




                                              "erythromycin,                                             Statement,A
                                                    ‘inhibits=catalyzes,metabolism,of’,
                                                                             simvastatin."




                                                                                    transformation




Fig. 4. Each OWL-inferred assertion in the evidence base generates a new individual
in the knowledge base. Braces indicate punned entities, referring to types, not classes.



    We also distinguish between references to a type and references to a class of
collection of things. As an example, consider the two statements: “simvastatin
treats dyslipidemia.” and “simvastatin was used in the treatment of patient John
Doe.” The latter sentence refers to a collection of specific portions of simvastatin,
whereas the former sentence refers to the entire type. In the context of PDDIs
this allows us to differentiate specific portions of a drug used in for example a
case study from the drug as a type. Traditionally, OWL represents classes and
individuals, but not types. However, OWL 2 enables the representation of types
using ‘punning’.7
    Punning means that we use a URI that is assigned to a class (for instance the
class ‘simvastatin’) and also assign it to an individual. For instance, the ChEBI
and PRO URIs are used to pun individuals that are intended to represent the
type of drug or enzyme. To represent the result of the inference shown in Figure 3,
we create a new individual which we will be able to link to both the drugs and
the enzyme involved. As is shown in Figure 4, we use the punned URI for the
ingredients, drug products and enzymes involved, to express that we are talking
about the types, not about actual portions of them.

7
    http://www.w3.org/TR/owl2-new-features/#F12:_Punning
4   Results and Evaluation

To evaluate the feasibility of using the model in practice, we investigated how
long querying will take. As a benchmark, we execute a set of 31 queries8 across
7 different sizes of MP graphs9 , from a single Virtuoso endpoint. Results are
shown in Figure 5.




Fig. 5. Range of performance: minimum and maximum run times for all 31 queries.


    To generate G0 , the first MP graph, the original DDI data is extracted from
the DIKB-old and reshaped into the MP model, along with its supporting ev-
idence. We include DIKB-old data with assertions that are typed as ‘inhibits’,
‘substrate of’, ‘increase AUC’, making G0 2.0 MB with 16,670 triples and 3498
entities. We also generated 6 additional graphs, with Gi containing 2i (i = 1 to 6)
copies of the G0 graph. Claims, methods, materials, and data in these MP graphs
have the same relationships as in G0 so that all items are evenly distributed in
the testing graphs. One limitation is that, although this is a real-world graph,
we scale by making multiple copies of the same entities and relationships.


5   Discussion

Drug information resources are created by groups, which may include an in-
formation specialist, drug specialists, etc. Currently, multiple different editorial
8
  http://purl.org/net/drug-interaction-knowledge-base/micropublication-
  queries
9
  http://purl.org/net/drug-interaction-knowledge-base/published-NP-and-
  MP-graphs
boards undertake this process entirely independently. Evidence from scientific
studies, new drug applications, and product labeling is first searched for, and
then evaluated. Complex cases are reviewed by multiple people in order to resolve
issues, such as to determine whether inclusion criteria are met.
    Currently, each group must independently spend a substantial amount of time
on reconciling and integrating information from various sources. This slows the
process, because multiple different systems, including literature alerts, spread-
sheets, and external databases, must be combined in order to evaluate whether a
PDDI should be recorded. One new potential is for some of the work of central-
izing information to be shared, even across different proprietary sources. Even
for commercial entities, shared data resources can be beneficial: For instance,
the Open PHACTS public-private partnership has received commercial funding
to pre-process and integrate certain aspects of pharmaceutical data into nanop-
ublications [17], which saves individual drug companies time and resources in
preparing proprietary data.

Maintainability Maintainability will also be a key factor in ensuring the success
of the new DIKB. We have already transformed existing annotations into the
new format. We are starting to construct a user-friendly annotation process,
so that evidence board members and their delegates can annotate knowledge
claims and primary data. Our ongoing research will suggest optimal approaches
for publishing PDDI claims and evidence in a structured form.


                                 Annotating4
                               while4publishing
                                                  Primary4data                  Micropublication
                                                                                evidence4 item
                                                        Argument4graphs


                                        Authors4 annotate44
                                                                             Claim      • Drug4X4interacts4with4drug4Y
                                                                                        • Drug4X4inhibits4enzyme4Q

                                  during4 the4publication4 process4 444
• Scientific4literature                                                                      • Data
• Product4label                                                              Support         • Materials
• Other…                                                                                     • Methods

                                     Evidence4 board4 annotates                              • Scientific4literature
                          while4 creating4a4new4drug4information4 sources    Reference • Product4label
                                                                                             • Other…

                                   Annotating4
                                   published4
                                   documents        Primary4data


                                                           Argument4graphs




Fig. 6. Structured publications such as micropublications could make the evidence
search and synthesis process more efficient and scalable. Two routes for creating PDDI
knowledge claims and evidence: authors could annotate micropublications for their own
papers (pre-publication) or an evidence board could create them (post-publication).


  Structured publishing is of increasing interest, and the two main sources of
PDDI evidence (product labels and scientific papers) are already moving toward
more structured representations. All drug product labels in the United States are
written by drug companies and submitted to the U.S. Food and Drug Admin-
istration using the Structured Product Labeling (SPLs) standard10 . Scientific
publishers may in the future deeply interlink scientific papers and data; this
is a proposed service of Elsevier, the largest scientific publisher11 . For authors
to annotate their own work with DIKB-compatible micropublications, and for
publishers to redistribute micropublications, thus seems conceivable.
    If either authors or drug editorial boards share DIKB-compatible micropub-
lications, other groups who need to process the same evidence would also benefit
because they would not need to separately integrate and reconcile the infor-
mation artifacts that support PDDI knowledge claims. This greatly improves
maintainability.

Shared Representation In the contested area of drug compendium information,
centralizing collective information has another benefit: it can help make visible
disagreements and discrepancies between these sources, which can be difficult to
resolve. Currently, there is no way to answer questions such as: did compendium
A and compendium B consider the same evidence? Only the final assertions–
lists and severity rankings of drug-drug interactions–are exposed; the underlying
evidence is not retrievable or comparable, without directly approaching evidence
boards, one by one.
    We see the potential for deeper structures, that represent intermediary steps
of the process in a shareable format. The evidence base of the new DIKB is
a shared representation to centralize and unify the information, and to support
tasks such as commenting on evidence, rejecting evidence, and asking for opinions
about evidence. This would also allow, for instance, citing a particular statement,
indicating a disagreement with it, or marking some evidence as more credible
than others. We believe that a computable, shared representation could enable
compendium editors to more easily integrate and cross-check different sources of
information – as well as to compare different potential interpretations, amongst
different editorial groups.


6    Conclusions & Future work
As we have discussed, the new DIKB has several advantages compared to the
DIKB-old. It is designed to meet 3 key requirements: maintainability, com-
putability, and the ability to link to biological processes while retaining logical
consistency.
    In future work we will test whether the DIKB does in fact simplify the
editor’s task of searching and synthesizing clinically relevant PDDI information.
In particular, we aim to show how clinicians and others interested in PDDIs
10
   http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/
   Guidances/ucm072317.pdf
11
   http://libraryconnect.elsevier.com/articles/best-practices/2013-
   02/research-data-driving-new-services
can use the DIKB to quickly identify the rationale for any given assertion of
knowledge. We plan to compare existing tools to new search tools derived from
the new DIKB, in terms of the completeness of information retrieval by drug
experts using the tools.
    One major advantage of the DIKB is that its evidence base serves as a shared
representation, recording the evidence assessed and the results taken into ac-
count. To further test the relevance of recording this information explicitly, in
the future we will explore an architecture enabling multiple ‘possible worlds’ to
be described: multiple knowledge bases, generated from different belief criteria.
    We hope that in the future some of the bookkeeping work of compedium cre-
ation can be shared–ideally amongst authors annotating their own materials–and
certainly amongst editors of different compendia. We will advocate for authors to
annotate publications of various sorts with DIKB-compatible micropublications
once appropriate tools can be made available. This route has great potential to
reduce much of the bookkeeping work conducted by the evidence board because
PDDI knowledge claims and the data supporting those claims would be anno-
tated by the individuals who wrote the submission. It would also help evidence
avoid becoming ‘stale’ because the new evidence would be linked directly to
knowledge claims as they are published.
    Since our approach is compatible with other ontologies in the Open Biomed-
ical Ontologies (OBO) Foundry through the reuse of identifiers, the import of
external information is simplified compared to the previous approach. With the
new DIKB architecture, as gene and protein knowledge is formalized in external
sources, that knowledge can be pulled in and queried, in ways that may meet
future use cases for other audiences.


Acknowledgments
The research leading to these results has received funding from the European
Union Seventh Framework Programme (FP7/2007-2013) under grant agreement
no 246016 for the ERCIM “Alain Bensoussan” Fellowship Programme; from a
grant from the National Library of Medicine (1R01LM011838-01); and training
grant 5T15LM007059-29 from the National Library of Medicine/National Insti-
tute of Dental and Craniofacial Research. We thank Carol Collins, Amy Grizzle,
Lisa Hines, Harry Hochheiser, and John R Horn.


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