Generalizing the Detection of Internal and External

Interactions in Clinical Guidelines

Veruska Zamborlini

1,2

, Rinke Hoekstra

1,3

, Marcos da Silveira

, Cedric Pruski

, Annette ten Teije

and Frank van Harmelen

Dept. of Computer Science, VU University Amsterdam, Amsterdam, The Netherlands

LIST Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

Faculty of Law, University of Amsterdam, Amsterdam, The Netherlands

Keywords:

Clinical Guidelines, Semantic Web, Knowledge Representation, Ontologies.

Abstract:

This paper presents a method for formally representing Computer-Interpretable Guidelines to deal with mul-

timorbidity. Although some approaches for merging guidelines exist, improvements are still required for

combining several sources of information and coping with possibly conﬂicting pieces of evidence coming

from clinical studies. Our main contribution is twofold: (i) we provide general models and rules for represent-

ing guidelines that expresses evidence as causation beliefs; (ii) we introduce a mechanism to exploit external

medical knowledge acquired from Linked Open Data (Drugbank, Sider, DIKB) to detect potential interactions

between recommendations. We apply this framework to merge three guidelines (Osteoarthritis, Diabetes, and

Hypertension) in order to illustrate the capability of this approach for detecting potential conﬂicts between

guidelines and eventually propose alternatives.

1 INTRODUCTION

Clinical Guidelines (CG) are developed for support-

ing physicians decision, e.g. specifying what treat-

ment work best in what situation (Peleg, 2013).

When possible, the recommendations provided by

CGs are based on evidence from clinical researches.

In this case, there is a direct mapping to the clini-

cal evidence that describes the effects (transitions)

of certain care action (e.g. do not administer aspirin

because of an increased risk of gastrointestinal bleed-

ing). Since an evidence is not a fact, a multitude of

evidence rating systems (Lohr, 2003) are adopted by

CGs authors. Epistemologically, an evidence reﬂects

a belief in the existence of a causal relation between

e.g. administering aspirin and gastrointestinal bleed-

ing. Furthermore, CGs are targeted to the treatment of

a speciﬁc illness. However, it is quite common to have

patients with multiple diseases (multi-morbidity) that

need to be addressed according to different CGs. For

example, according to (Barnett et al., 2012), around

40% of 55 years old patients suffer from at least 2 dis-

eases, and 20% of 70 years old patients suffer from

at least 4 diseases in Scotland. As with any large

volume of regulations, combined guidelines almost

inevitably involve intricate interactions between the

recommendations they describe. Finding interactions

(like potential conﬂicts) requires intensive collabora-

tion in multidisciplinary teams.

Computational support can be of great value for

supporting physicians to handle all this complexity.

Many languages have been proposed for representing

“computer interpretable” guidelines (CIG) and rea-

soning about it (Peleg, 2013). However, the con-

cepts here discussed are poorly or not addressed by

those approaches. The main reason is because much

has been devoted to executing guidelines within treat-

ments rather than other purposes such as combining

and updating CGs. In particular, regarding the issue

of multimorbidity, existing approaches for combin-

ing CGs are limited in their ability to automatically

detect the interactions, propose alternatives or com-

bining more than two guidelines (Zamborlini et al.,

2015b).

This work follows an incremental methodology.

We start by addressing realistic but simpliﬁed case

studies, and add more complexity according to the

lessons learned in each iteration. Therefore, this paper

is the continuation of earlier work reported in (Zam-

Zamborlini, V., Hoekstra, R., Silveira, M., Pruski, C., Teije, A. and Harmelen, F.

Generalizing the Detection of Internal and External Interactions in Clinical Guidelines.

DOI: 10.5220/0005704101050116

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 5: HEALTHINF, pages 105-116

ISBN: 978-989-758-170-0

105

borlini et al., 2014a; Zamborlini et al., 2014b; Zam-

borlini et al., 2015b; Zamborlini et al., 2015a). In this

series of work, we investigated (i) what knowledge

is required to represent and reason about CGs (rather

than how to acquire such knowledge), particularly for

supporting the multimorbidity issue; (ii) how it can

be formalized; (iii) how it can be implemented using

Semantic Web technologies, so that (iv) we can ex-

ploit the medical knowledge available as Linked Open

Data (LOD). As a consequence, the results we obtain

are limited to the current expressiveness of the model,

e.g. temporal aspects and related interactions will be

addressed in future iterations.

This paper reports on improvements to both the

models and the implementation to better address the

issue of multimorbidity. The contributions are (C1)

a more generic version of the models with respect to

recommendations, beliefs and event types. This in-

cludes (C2) a formalization of the improved models

and rules in FOL; and (C3) a Semantic Web frame-

work for representing and reasoning about recom-

mendations and beliefs using standard vocabularies.

This provides (C4) a ﬂexible mechanism for reusing

external knowledge bases to extend our ability to

detect interactions (showcased using DrugBank and

Sider).

The remainder of this paper is as follows: Sect. 2

presents a case study to illustrate the main concepts,

which are further deﬁned in the models and rules, fol-

lowed by their implementation. An experimental as-

sessment shows the results obtained for the referred

case study in Sect. 3. The related work is discussed

in Sect. 4 and the main contributions and future work

are discussed in Sect. 5.

2 THE MODELS & FRAMEWORK

This section describes our case study, as well as

the adapted version of the TMR (Transition-based

Medical Recommendation) models and their semantic

web-based implementation as a framework for repre-

senting norms (recommendations) in the clinical do-

main and reasoning about interactions among them.

2.1 Case Study

This case study is meant for illustrating the concepts

previously mentioned and further deﬁned in the next

section. It concerns the combination of three guide-

lines, namely Osteoarthritis (OA), Diabetes (DB) and

Hypertension (HT), and the detection of interactions

among them (adapted from (Jafarpour, 2013)). The

recommendations, depicted in Fig. 1, are:

Diabetes (DB)

1. Should adm. NSAID to reduce blood coagulation

2. Should adm. Tramadol to reduce blood coagulation

3. Should adm. Insulin to reduce blood sugar level

Osteoarthritis (OA)

1. Should NOT administer Aspirin to avoid increasing

the risk of gastrointestinal bleeding

2. Should administer Ibuprofen to reduce pain

Hypertension (HT)

1. Should adm. Thiazide to reduce the blood pressure

Among them some interactions can be identiﬁed:

Internal Interactions:

1. DB.1 and DB.2 are alternative recommendations

meant for promoting the same effect.

2. DB.1 and OA.1 are contradictory recommendations

since the ﬁrst might lead to the prescription of As-

pirin which is non-recommended by the later.

External Interactions (from external knowledge sources):

1. DB.1 and DB.2 both have as external alternative Ad-

minister Epoprostenol to achieve the desired effect

according to DrugBank.

2. DB.2 and OA.2 recommend incompatible actions ac-

cording to Drugbank

3. DB.3 and HT.1 interact since the latter might lead to

prescription of bendroﬂumethiazide, which has high

blood sugar level as side effect according to Sider,

as opposed to goal in the former.

4. HT.1 and OA.2 interact since the latter has high

blood pressure as side effect according to Sider, as

opposed to goal in the former.

5. (others)

In Fig. 1 the big rectangles in both left and right

sides represent beliefs regarding the care actions (ad-

ministering tramadol). The latter is represented as

dotted ellipses inside the beliefs. The causation be-

liefs are about a transition between situations (blood

coagulation goes from normal to low) that are be-

lieved to be promoted by executing a care action

type). The causation belief has a frequency e.g. ad-

miniter tramadol always reduce the blood coagula-

tion. For sake of simplicity, we consider in this work

only always as frequency for all causation beliefs.

They also have a strength associated, which corre-

sponds to the evidence level (e.g. high level), accord-

ing to the quality attributed to the sources (or stud-

ies) that provide such knowledge. The beliefs in gray

shade represent the knowledge imported from an ex-

ternal source described in the top left (e.g. Drug-

Bank). The strength in this case will depend on the

reliability of each data source. The external sources

here considered describe two types of beliefs: causa-

tion belief or incompatibility belief. The latter repre-

sents action types that should not be recommended to-

gether, e.g. Administer Aspirin is incompatible with

HEALTHINF 2016 - 9th International Conference on Health Informatics

106

OA+HT+DB Guideline Transitions by Care Actions

Avoid

Thrombi

should

Blood Coagulation

low

Administer

NSAID

normal

Reduce

Blood

Sugar Level

should

Blood Sugar Level

normal

Administer

Insulin

high

Pain

pain

Administer

Ibuprofen

pain

should

Heal Pain

Avoid

Thrombi

should

Blood Coagulation

low

Administer

Tramadol

normal

Transitions by Care Actions

alternative

extIncompEffect

extAlternative

Blood Coagulation

low

Administer

Epoprostenol

normal

Blood Pressure

high

Administer

Ibuprofen

Incompatibility

Administer

Aspirin

Administer

Ibuprofen

extIncompActions

[DrugBank]

[Sider]

Blood Sugar Level

high

Administer

bendroﬂume

thiazide

[Sider]

Blood Pressure

low

Administer

Thiziade

high

should

Reduce

Blood

Pressure

Gastrointestinal Bleeding

high

risk

Administer

Aspirin

low

risk

shouldn't

Avoid

Bleeding

contradiction

high

level

med.

level

med.

level

med.

level

high

level

low

level

always

low

level

med.

level

med.

level

low

level

Figure 1: Case study on combining guidelines for OA+HT+DB.

Administer Ibuprofen (the reason is not provided in

structured way from the sources).

The dotted rectangles in the middle represent the

guidelines. The more external one is the merge of

the three guidelines for OA+HT+DB. They comprise

both the recommendations (e.g. avoid thrombi) and

the interactions alternative among them. The former

is represented as rounded rectangles, and the latter is

depicted by labelled thin arrows connecting the inter-

acting recommendations and beliefs. Solid arrows are

for internal interactions and dotted arrows for exter-

nal ones. A positive (or negative) recommendation is

indicated by a thick arrow labeled with “should” (or

“should not”).

2.2 Conceptual Model & Rules

Figure 2 presents a UML class diagram for the

T MR

Event

model describing some relevant concepts

and relations regarding event types in the scope of

this work. The concepts introduced in previous ver-

sions of the models are depicted in gray-shade (same

for the next diagrams). This model is inspired in UFO

(Uniﬁed Foundational Ontology) (Guizzardi et al.,

2013) that is a formal theory describing some of the

general concepts used here, namely Type (Universal)

and Category, as well as Object, Event, Action and

Situation Types. The model regards mostly types of

things

since it is meant for modeling, for example,

the type of event that is expected as consequence of

another one, rather then the particular event that was

the consequence of another particular one. In other

words, we do not want to say that John’s pain was re-

For sake of simplicity we can omit the word ‘type’.

Figure 2: UML class diagram for the T MR

Event

Model.

lieved due to the administration of aspirin, but that ad-

ministering aspirin often relieves the pain of patients

While action types concern event types to be per-

formed by an intentional agent (omitted in the model),

transition types concern (deterministic) event types

for which pre and post situation types can be de-

ﬁned

. In other words, it represents the transforma-

tion of a situation type into another (transformable

& expected situations). An event type can be de-

ﬁned as the participation of a certain object type,

e.g. a DrugAdministration type is the administration

of (participation of) a Drug type. An event type can

cause another event type to happen (occurrence of

one causes the occurrence of the other). Moreover,

For a deeper explanation see (Zamborlini et al., 2014a).

We do consider some event types are non-deterministic

or non-intentional, but this is out of scope of this work.

Generalizing the Detection of Internal and External Interactions in Clinical Guidelines

107

an event type can also be incompatible with another

one when they can not or should not occur together.

In other words, either happening together is not pos-

sible or would bring about results/transitions that are

not the expected ones.

A category is a type that (transitively) subsumes

(or regroups) other types according to a grouping cri-

teria, e.g. ThiazideDrug is a category of drugs that

contains the molecule thiazide, e.g. bendroﬂumethi-

azide. In this case the grouping criteria regards a

structural property. However, it can also concern the

effect expected to be promoted, e.g. NSAID is the cat-

egory for the (non-steroidal) drugs expected to pro-

mote the transition reduce inﬂammation, e.g. Aspirin.

FOL rules are provided for deriving relations rel-

evant in the context of this work. Some relations

are deﬁned in terms of other relations, for example,

inverseTo between transition types is one transition

that ‘undo’ the effect of the other. These relations

are preceded by a slash in the models (previous and

forthcoming). Other relations can be propagated, e.g.

the grouping criteria of an object category (Analgesic

Drug) is the grouping criteria of the event category

of administering such object (Adm. Analgesic) and

vice-versa.

Inverse Transitions: one transition type t1 trans-

forms situation s1 into s2 while another transition

t2 transforms s2 into s1.

R.1 ∀t1, t2, s1, s2 TransitionType(t1)∧ TransitionType(t2)

∧ SituationType(s1) ∧ SituationType(s2) ∧ s1 6= s2

∧ hasTransformableSituation(t1,s1)

∧ hasExpectedSituation(t1,s2)

∧ hasTransformableSituation(t2,s2)

∧ hasExpectedSituation(t2,s1))

→ inverseTo(t1,t2)

Propagating grouping criteria via administra-

tionOf: if an action type a1 is the administration

of a drug type d1, a transition type t1 is the group-

ing criteria for d1 iff it is also for a1.

R.2 ∀a1, d1, t1 (ActionType(a1) ∧ DrugType(d1)

∧ TransitionType(t1) ∧ administrationOf(a1,d1))

→ ( hasGroupingCriteria(d1, t1)

↔ hasGroupingCriteria(a1, t1))

Other relations can be difﬁcult to be precisely de-

ﬁned either for epistemic or ontological issues. For

instance, some event types might not have a precise

deﬁnition of their consequences, e.g. administering

aspirin sometimes relieves the pain, sometimes it does

not. We address this issue in the T MR

Belie f

model

by representing those relations through beliefs, pre-

sented in a UML class diagram in Fig. 3. In this

work, beliefs allow to represent a ‘degree of truth’ for

assertions about things/entities according to a source.

Figure 3: UML class diagram for the T MR

Belie f

Model.

‘M1’ is used as short reference for T MR

Event

It allows for complementary beliefs, e.g. aspirin re-

lieves the pain in 80% of the cases (therefore it does

not in 20%), but also for inconsistent ones, e.g, ad-

ministering aspirin always relieves the pain and it

never relieves the pain. This is a desired feature since

for some assertions there can be no common agree-

ment from different sources (see (Zamborlini et al.,

2015a)). However, we consider that one (merged)

guideline that rely on incompatible beliefs is then in-

consistent. We also account for the certainty/quality

of the belief as its strength, such as the evidence level

classiﬁcation in clinical guidelines.

We are particularly interested in beliefs about

the relations causes, subsumes and incompatibleWith

between event types, for which we provide ‘epis-

temic/doxastic’ versions (represented as dotted lines

in the model). In other words, they are relations de-

pendent on the existence of a belief to ground their

truthfulness (in practice they have a belief as a third

argument). Therefore, they are not the same as the

ones in Fig. 2.

The causation belief between event types reﬂects

the likelihood/frequency (probability) of one causing

the another according to a source. In this work we fo-

cus on beliefs about action (hasAsCause Action Type)

causing transitions (hasAsEffect Transition Type) as

justiﬁcation for the clinical recommendations. More-

over, for the interaction rules we consider only the

positive causation beliefs, i.e., an action type always

cause a transition, since it is not on the scope of this

work both (i) the negative causation, which only ap-

pears as sub-justiﬁcations of recommendation (dis-

cussed in (Zamborlini et al., 2015a)) and (ii) the in-

termediate frequency values (often, rarely, etc)

. In its

turn, the incompatibility between event types is con-

sidered in this work to be given as an assertion. There-

fore it is represented as a belief, although it could be

explained/derived at a certain level of granularity. Fi-

This approach exclude endless assertions about all the

effects an event is not expected to produce since the beliefs

are deﬁned in CGs or scientiﬁc papers by a community of

experts, e.g. cancer is not an effect of a certain drug.

HEALTHINF 2016 - 9th International Conference on Health Informatics

108

nally the subsumption of event types due to expected

effect also relies on beliefs, namely the causation one.

Causation - an event type e1 causes another one e2

with a certain frequency f according to a belief cb:

R.3 ∀e1, e2, cb, f (EventType(e1) ∧ EventType(e2)

∧ CausationBelief(cb) ∧ hasAsCause(cb, e1)

∧ hasAsEffect(cb, e2)) ∧ frequency(cb, f)

→ causes(e1,e2, f, cb)

Similar Causation Beliefs: two beliefs cb1, cb2

about different event types e1, e2 promoting with

same frequency f another event type e3.

R.4 ∀cb1, cb2, e1, e2, e3, f causes(e1, e3, f, cb1)

∧ causes(e2, e3, f, cb2) ∧ cb1 6= cb2 ∧ e1 6= e2

→ similarTo(cb1,cb2)

Propagating causation via hasGroupingCriteria:

If an event type e1 has as grouping criteria a tran-

sition type t1 then e1 must cause t1

R.5 ∀e1, t1 (EventType(e1) ∧ TransitionType(t1)

∧ hasGroupingCriteria(e1, t1))

→ ∃cb causes(e1, t1, ‘always’, cb)

Deriving Subsumption via causation and group-

ing criteria: if an event type e1 causes a transi-

tion t1 that is the grouping criteria of another event

type e2 then e2 subsumes e1 according to the cau-

sation belief.

R.6 ∀e1, e2, t, cb1 (EventType(e1) ∧ EventType(e2)

∧ TransitionType(t) ∧ causes(e1, t, ‘always’, cb1)

∧ hasGroupingCriteria(e2, t) ∧ e1 6= e2)

→ ∃cb subsumes(e2, e1, cb1)

Incompatible Event Types: event type e1 is be-

lieved be incompatible with another one e2.

R.7 ∀e1, e2, ib (EventType(e1) ∧ EventType(e2)

∧ IncompatibilityBelief(ib) ∧ about(ib,e1)

∧ about(ib,e2))

→ incompatibleWith(e1, e2, ib)

In more general terms, a Regulation is composed

of a set of Norms, given by a source, about the ex-

ecution of action types based on a causation belief.

The norm strength can vary from obligation to prohi-

bition. For the speciﬁc case of clinical domain, norms

are specialized as Recommendations and regulations

as Clinical Guidelines. Since the clinical guidelines

are mostly considered a reference for best practices,

the strength of recommendations in this work will

be considered as ‘should’ (positive) and ‘should-not’

(negative), any other variation of strength is out of

scope. Finally, among norms there can be Interac-

tions of different types. In this work we formalize

internal interactions discussed in (Zamborlini et al.,

2015a) and we extend and formalize external inter-

actions (introduced in (Zamborlini et al., 2015b)).

Figure 4: UML class diagram for the T MR

Norm

Model.

‘M1’ and ‘M2’ are used as short references for T MR

Event

and T MR

Belie f

respectively.

Figure 4 presents the UML class diagram for the

T MR

Norm

model.

The following FOL rule deﬁnes the derivable re-

lation regulates while the interactions are deﬁned in

the next subsections. Some interaction types have

a cumulative behavior, like Repeated Action and Al-

ternative Actions (introduced in (Zamborlini et al.,

2014b)). For example, if three norms recommend

the administration of aspirin, there should be one sin-

gle interaction of type Repeated Action among them,

rather than three different interactions among pairs

of them. External interactions also accumulate, like

External-Alternative Action. Although the subsump-

tion relation in these rules can be also the epistemic

one, derivable through causation beliefs, we adopt the

simpliﬁed notation subsumes(a1, a2) since it does not

change the meaning of the rules.

Regulation - a norm n from a regulation r over an

action type a has strength st based on a causation

belief cb:

R.8 ∀r, n, a, st, cb (Regulation(r) ∧ Norm(n)

∧ partOf(n, r) ∧ ActionType(a) ∧ CausationBelief(cb)

∧ aboutExecutionOf(n, a) ∧ strength(n, st)

∧ basedOn(n, cb))

→ regulates(r, n, a, st, cb)

With respect to previous work, this section present

more generic version of the models with respect to

norms, beliefs and event types, (mentioned contribu-

tion C1). It allows, for instance, to better handle the

hierarchies of action types (or event types) possibly

deriving them from hierarchies of drug types, which

is commonly found in the existent datesets and ter-

minologies. Particularly the hierarchies concerning

effects believed to be promoted (e.g. Adm. Aspirin

specializes Adm. AntiInﬂammatory) are handled as

beliefs. This is indeed compatible with the discourse

of not of having certainty about causation relations.

FOL formulas are adapted/introduced for the deriv-

able relations. Furthermore, the incompatibility belief

is introduced in the T MR

Belie f

model, as well as the

strength of beliefs and causation frequency. Finally,

the recommendations strength is also introduced in

Generalizing the Detection of Internal and External Interactions in Clinical Guidelines

109

the T MR

Norm

model.

2.2.1 Internal Interaction Rules

Considering the modiﬁcations in the model, we pro-

pose in this section the corresponding adaptation of

the internal interaction rules presented in (Zambor-

lini et al., 2015a) (contribution C2). The following

types of interactions are deﬁned:

Repeated Action: two positive norms about the

same action or about actions in a subsuming re-

lation. The second rule is for the cumulative be-

havior, i.e. when two different interactions of this

type relate the same norm (n2) then those interac-

tions are the same.

(I.1.1) ∀r, n1, n2, a1, a2, cb1, cb2(

regulates(r, n1, a1, ’should’, cb1)

∧ regulates(r, n2, a2, ’should’, cb2)

∧ (a1 = a2 ∨ subsumes(a1,a2) ∨ subsumes(a2,a1)))

→ ∃i(RepeatedAction(i) ∧ relates(i,n1)

∧ relates(i,n2))

(I.1.2) ∀i1, i2, n1, n2, n3( RepeatedAction(i1)

∧ RepeatedAction(i2) ∧ relates(i1,n1)

∧ relates(i1,n2) ∧ relates(i2,n2) ∧ relates(i2,n3)

∧ r1 6= n3 ∧ n1 6= n2 ∧ n2 6= n3 )

→ i1 = i2

Alternative Actions: two positive norms about dif-

ferent actions for achieving the same transition,

i.e. they are based on similar causation beliefs for

different actions. The second rule is for the cumu-

lative behavior

(I.2.1) ∀r, n1, n2, a1, a2, cb1, cb2(

regulates(r, n1, a1, ’should’, cb1)

∧ regulates(r, n2, a2, ’should’, cb2)

∧ similarTo(cb1, cb2) ∧ a1 6= a2 )

→ ∃i(AlternativeActions(i) ∧ relates(i,n1)

∧ relates(i,n2) )

(I.2.2) ∀i1, i2, n1, n2, n3( AlternativeActions(i1)

∧ AlternativeActions(i2) ∧ relates(i1,n1)

∧ relates(i1,n2) ∧ relates(i2,n2) ∧ relates(i2,n3)

∧ n1 6= n3 ∧ n1 6= n2 ∧ n2 6= n3 )

→ i1 = i2

Contradictory Norms: (i) two norms, positive and

negative, about the execution of same action (or

actions in a subsuming relationship) or (ii) two

norms, positive and negative, about different ac-

tions promoting the same transition or (iii) two

positive regulations about different actions for

achieving inverse transitions.

(I.3) ∀r, n1, n2, a1, a2, cb1, cb2, t1, t2(

regulates(r, n1, a1, ’should’, cb1)

∧ regulates(r, n2, a2, str, cb2)

∧ causes(a1,t1,’always’,cb1)

∧ causes(a2,t2,’always’,cb2)

∧ (( str = ’should-not’

∧ (a1 = a2 ∨ subsumes(a1,a2) ∨ subsumes(a2,a1))

∨ (str = ’should-not’ ∧ a1 6= a2 ∧ t1 = t2 )

∨ (str = ’should’ ∧ a1 6= a2 ∧ inverseTo(t1, t2))))

→ ∃i(Contradiction(i) ∧ relates(i,n1) ∧ relates(i,n2) )

Repairable Transition: two norms, positive and

negative, about different actions that are believed

to cause inverse transitions, i.e. if the undesired

effect cannot be avoided, it can be repaired by an-

other action.

(I.4) ∀r, n1, n2, a1, a2, cb1, cb2, t1, t2(

regulates(r, n1, a1, ’should’, cb1)

∧ regulates(r, n2, a2, ’should-not’, cb2)

∧ causes(a1,t1,’always’,cb1)

∧ causes(a2,t2,’always’,cb2)

∧ a1 6= a2 ∧ inverseTo(t1, t2) )

→ ∃i(RepairableAction(i) ∧ relates(i,n1)

∧ relates(i,n2))

2.2.2 External Interaction Rules

Beliefs from other sources provide interesting infor-

mation to enrich the system, allowing for detection of

external interactionsdeﬁned as:

External-Alternative Actions: actions of which the

regulation system is not aware that might promote

a desired effect according to external sources. The

second rule is for the cumulative behavior, i.e. two

different interactions of this type relating the same

external causation belief are the same.

(E.1.1) ∀r, n1, a1, a2, cb1, cb2(

regulates(r, n1, a1, ’should’, cb1)

∧ similarTo(cb1, cb2) ∧ hasAsCause(cb2,a2)

∧ ¬(∃n2 regulates(r, n2, a2, ’should’, cb2))

∧ ¬ subsumes(a1, a2))

→ ∃i( ExternalAlternativeAction(i)

∧ relates(i,n1) ∧ relates(i,cb2) ∧ relates(i,a2) )

(E.1.2) ∀i1, i2, r, n1, n2, cb (ExternalAlternativeAction(i1)

∧ ExternalAlternativeAction(i2) ∧ CausationBelief(cb)

∧ relates(i1,cb) ∧ relates(i1,n1)

∧ relates(i2,cb) ∧ relates(i2,n2)

∧ Regulation(r) ∧ partOf(n1,r) ∧ partOf(n2,r))

→ i1 = i2

External-Incompatible Actions: two positive

norms about actions that the regulation system

is not aware of them being incompatible to each

other (or to a subsumed action), according to ex-

ternal sources.

(E.2) ∀r, n1, n2, a, a1, a2, cb1, cb2, ib1(

regulates(r, n1, a1, ’should’, cb1)

∧ regulates(r, n2, a2, ’should’, cb2)

∧ incompatibleWith(a, a2, ib1)

∧ (a1 = a ∨ subsumes(a1,a) ∨ subsumes(a,a1)))

HEALTHINF 2016 - 9th International Conference on Health Informatics

110

→ ∃i(ExternalIncompatibleActions(i)

∧ relates(i,n1) ∧ relates(i,n2) ∧ relates(i,ib11) )

External-Incompatible Effects: effects (situations)

recommended to be either avoided or changed, but

which are believed to be promoted by other rec-

ommended actions, according to external sources,

while the regulation system is not aware of it.

(E.3) ∀r, n1, n2, a, a1, a2, cb, cb1, cb2, s1(

regulates(r, n1, a1, st, cb1)

∧ causes(a1, t1, ’always’, cb1)

∧ ((st= ‘should’) ∧ hasTransformableSituation(t1,s1))

∨ (st= ‘should-not’) ∧ hasExpectedSituation(t1,s1)))

∧ causes(a, t, ’always’, cb) ∧ a 6= a1

∧ hasExpectedSituation(t, s1)

∧ regulates(r, n2, a2, ’should’, cb2) ∧ cb 6= cb2

∧ (a2 = a ∨ subsumes(a,a2) ∨ subsumes(a2,a)))

→ ∃i(ExternalIncompatibleEffects(i) ∧ relates(i,n1)

∧ relates(i, n2) ∧ relates(i,s1) ∧ relates(i,cb2) )

This section concludes the contribution C2 by pro-

viding generic rules for detecting external interac-

tions. In (Zamborlini et al., 2015b) the rules were

speciﬁc for a dataset (namely DrugBank). Now the

generic rules apply to beliefs imported from any

dataset. At this point its also important to observe

that both models and rules are deﬁned in a domain-

independent way. A SemWeb-based implementation

is provided in the next section.

2.3 SemWeb Implementation

This section presents a SemWeb implementation

for

the proposed approach. The proposed models have

a straighforward mapping to OWL2 (ommited in the

paper). However, for instantiating the models with the

clinical knowledge, we propose the use of a frame-

work as RDF graph structure based on the open for-

mats Nanopublication, Provenance and Open Anno-

tation (see section 4). It is applied for representing

the recommendations (norms) and beliefs as asser-

tions connected to their sources, besides other meta-

information. In the sequence, the implementation of

the FOL rules using SWI-Prolog is exempliﬁed, to-

gether with a procedure adopted to import clinical

knowledge from LOD (e.g. DrugBank and Sider) as

assertions via generic predicates (beliefs).

2.3.1 Framework

The framework, illustrated in Fig. 5, follows the

Nanopublication structure, which presupposes the use

of Prov vocabulary, and is enriched with (optional)

Open Annotation vocabulary. The latter is meant for

Accessible at http://rapgmsbgym.github.io.

Nanopublication

Assertion

nanopub:hasAssertion

nanopub:hasProvenance

Provenance

prov:wasDerivedFrom

oa:hasBody

oa:hasTarget

oa:hasSource

source

oa:Annotation

PublicationInfo

dateTime

prov:generatedAtTime

prov:wasAttributedTo

nanopub:hasPublicationInfo

Figure 5: Nanopublication Schema proposed for represent-

ing Beliefs & Recommendations.

representing assertions that are (somehow) extracted

from textual documents.

Rounded-dotted boxes represent named graphs

containing triples, which in turn are represented as

directed-named arrows among resources. The black

circles represent the named graphs themselves as sub-

ject/object, while the other resources are represented

as ellipses with a description inside (where underline

stands for blank nodes) or as an expected data-value

(e.g. source or date).

The more external named graph, called Nanopub-

lication, connects the following three named graphs:

The assertion is a named graph where some knowl-

edge is described using suitable vocabularies, in our

case the TMR models. The other named graphs are

meant to provide the meta-information about both

the assertion and its publication as rdf-data: (i) the

provenance graph can contain information such as

the source (prov:wasDerivedFrom) of the assertion

(e.g. clinical guideline, study or dataset), and text-

annotations (oa:Annotation) when the assertion is ex-

tracted from a piece of text; and (ii) the publication-

Info graph provides meta-information such as when

the publication was created and by whom. For sake of

readability, henceforth we omit from the ﬁgures part

of the framework that is not relevant for the discussion

here conducted.

Figure 6 illustrates the representation of both a

causation belief (at the top) and a recommendation

(at the bottom). The Assertion1 is a CausationBe-

lief with high strength level, about the action type

that Adm. Ibuprofen always causes the transition type

Pain relief according to OA-CIG-Description. The

Assertion2 is a Norm, part of OA-GIC that states

Adm. Ibuprofen should be executed based on the

evidence stated in Assertion1 according to OA-CIG-

Description. Beliefs taken from external sources are

similarly represented (see Fig. 7).

The framework favor data reusability as LOD,

Generalizing the Detection of Internal and External Interactions in Clinical Guidelines

111

Provenance1

prov:wasDerivedFrom

oa:hasBody

oa:hasTarget

oa:hasSource

OA-CIG-Descr.

oa:Annotation

Provenance2

prov:wasDerivedFrom

oa:hasBody

oa:hasTarget

oa:hasSource

OA-CIG-Descr

oa:Annotation

Assertion2

m3:Norm

"should"

m3:strength

Adm.

Ibuprofen

m3:aboutExecOf

m3:basedOn

OA-CIG

m3:partOf

Assertion1

m2:CausationBelief

"always"

m2:frequency

Adm.

Ibuprofen

Pain Relief

m2:causes

m2:strength"high"

Figure 6: Nanopublication Schema for Causation Beliefs &

Norms extracted from Guidelines. ‘M2’ and ‘M3’ are used

as preﬁxes for T MR

Belie f

and T MR

Norm

respectively.

since it is compatible with SemWeb standards pro-

posed for expressing and annotating knowledge ex-

tracted from (scientiﬁc) publications. It comprises

part of contribution C3 (SemWeb implementation of

model and rules).

2.3.2 Rules

The proposed FOL rules have the typical format of

Prolog rules, what makes its implementation very

straightforward. The implemented rules are here il-

lustrated as: function F.1 implements R.8 while func-

tions F.2.1, F.2.2 implement the rules I.1.1 and I.1.2

for interaction RepeatedAction. For the purpose of

this application, the existential quantiﬁer in the conse-

quent of interaction rules is implemented as a Prolog

function called existsInteraction. This function uses

the rdf

assertion built-in-function to insert the respec-

tive interaction in the dataset in case it does not exist.

(F.1) regulates(Reg, Norm, ActT, Str, CBelief) :-

instanceOf(Norm, m3:’Norm’),

rdf(Norm, m3:‘partOf’, Reg),

rdf(Norm, m3:‘aboutExecutionOf’, ActT),

rdf(Norm, m3:‘strength’, literal(type(xsd:string,Str))),

rdf(Norm, m3:‘basedOn’, Belief, Norm).

(F.2.1) forall( (regulation(Reg),

regulates(Reg, N1, ActionT1, ’should’, ),

regulates(Reg, N2, ActionT2, ‘should’, ),

different(N1,N2),

( same(ActionT1, ActionT2)

; subsumes(ActionT1, ActionT2)

; subsumes(ActionT2, ActionT1) )),

existsInteraction(‘RepeatedAction’, N1, N2)).

(F.2.2) forall( (interacts(‘RepeatedAction’, N1, N2, I1),

interacts(‘RepeatedAction’, N2, N3, I2),

different(N1,N3), different(I1, I2)),

rdf assert(I1, owl:sameAs, I2)).

This section comprises part of contribution C4

(ﬂexible mechanism for reusing LOD to detect in-

teractions) and together with the framework, it con-

cludes contribution C3.

Assertion4

m2:CausationBelief

"always"

m2:frequency

Adm.

Ibuprofen

Tr. High Blood

Pressure

m2:causes

m2:strength"low"

Assertion3

m2:CausationBelief

"always"

m2:frequency

Adm.

Epoprostenol

Tr. LowerBlood

Coagulation

m2:causes

m2:strength"medium"

Assertion5

m2:IncompatibilityBelief

Adm.

Ibuprofen

Adm. Aspirin

m2:incompatibleWith

"medium"

m2:strength

Provenance3

prov:wasDerivedFrom

Drugbank

db:Epopros.

db:catAC

prov:used

prov:was

GeneratedBy

prov:Activity

prov:used

Provenance4

prov:wasDerivedFrom

Sider

sd:Ibuprofen

sd:HighBP

prov:used

prov:was

GeneratedBy

prov:Activity

prov:used

Provenance

prov:wasDerivedFrom

Drugbank

db:DDI-A-I

prov:used

prov:was

GeneratedBy

prov:Activity

Figure 7: Nanopublication schema for representing beliefs

extracted from external knowledge sources. ‘M2’ is used as

preﬁx for T MR

Belie f

2.3.3 Using External Knowledge - LOD

For some external knowledge-bases available as

LOD, a procedure is adopted for importing the knowl-

edge as new beliefs. For example, knowledge about

the (side) effects of administering a certain drug can

be imported from Sider. Each LOD requires a proce-

dure of reinterpreting the original knowledge into the

format here proposed. Once it is done, the rules can

derive external interactions regardless to the source

The following SWI-Prolog rule is used for importing

causation beliefs from drugbank (other rules can be

found online, also comprising sider and dikb).

Drug Alternatives for all drugs belonging to a

drugCategory regarding an effect, the causa-

tion beliefs are asserted about the actions of

administering those drugs promoting the referred

effect/transition.

forall( rdf(DrugCat, model:’hasGroupingCriteria’,

Trans1),

same(DrugCat, DrugCatDB),

rdf(DrugDB, drugcategory:’category’, DrugCatDB),

rdf(Act1, model:’administrationOf’, DrugType),

same(DrugType, DrugDB))),

(assertCausation(Act1, Trans1, ‘always’, ‘drugbank’,

The Drug and Situation Types are mirrored and mapped

to the to the external knowledge sources via owl:sameAs.

HEALTHINF 2016 - 9th International Conference on Health Informatics

112

NanopubURI),

assertProvResourceUsed(NanopubURI, DrugDB),

assertProvResourceUsed(NanopubURI, DrugCatDB))).

Figure 7 depicts the representation of beliefs ex-

tracted from both Drugbank and Sider. The Asser-

tion3 at the top is a CausationBelief with medium

strength level, stating that the action type Adm.

Epoprostenol always causes the transition type Lower

Blood Coagulation according to Drugbank. The As-

sertion4 in the middle is another CausationBelief

with low strength level, stating that Adm. Ibuprofen

always causes to Higher Blood Pressure according to

Sider. Finally, Assertion5 is a IncompatibilityBelief

with medium strength level, stating that Adm. Ibupro-

fen and Adm. Aspirin are incompatible according to

Drugbank. For all of them, the provenance graphs

contain, besides the source dataset, the external re-

sources based on which the assertions were generated.

This section concludes contribution C4 together

with the formalisation and implementation of rules

for external interactions. Medical guidelines as well

as external clinical knowledge can be expressed by

means of the conceptual model and can be imple-

mented in a SemWeb-based Framework for automat-

ically detecting interactions. In the next section we

provide an experimental assessment by discussing the

implementation a case study on detecting recommen-

dations interactions enriched by external knowledge

sources.

3 EXPERIMENTAL

ASSESSMENT

This section presents the results obtained by imple-

menting the case study on combining OA+HT+DB

guidelines (Sect. 2.1). The following activities where

performed in the experiment: (i) the guideline knowl-

edge was (manually) introduced in a RDF dataset ac-

cording to the implementation here proposed; (ii) the

rules for importing LOD were ﬁred; (iii) the rules for

inferring relations and interactions were ﬁred. An ‘in-

teractive’ documentation describing the experiment

and the prolog code is available online

. Figure 8

summarize the obtained results. It describes the type

of interaction, the interaction and its source (derived

from internal or external knowledge). The ﬁrst six

lines are the interactions described in Sect. 2.1. The

last two lines illustrates that more external interac-

tions can be detected, actually much more given the

large volume of clinical LOD. However, since excess

of information can become a disadvantage, we intend

http://rapgmsbgym.github.io

to provide ﬁlters, such as the causation frequency or

the strength of the evidence.

Comparing to the previous implementation (Zam-

borlini et al., 2015b), the following explicit improve-

ments are observed: (i) reasoning over action type

hierarchy allows for detecting non-straightforward

interactions (e.g. DO administer NSAID and DO

NOT administer Aspirin are in contradiction because

Aspirin specializes NSAID); (ii) new datasets (e.g.

Sider) are added without need for writing speciﬁc

rules for detecting external interactions; and (iii) cau-

sation frequency, belief strength (evidence level) and

recommendation strength can be represented. Im-

plicit improvement are: (i) a more maintainable and

reusable implementation that will favor new features

and datasets to be introduced in future work; (ii) the

more reliable and/or relevant information can be se-

lect.

4 RELATED WORK

Formal languages proposed for representing clini-

cal guidelines as “computer interpretable” ones (An-

nette ten Teije, 2008; Peleg, 2013) were not designed

to handle the combination of multiple CIGs (Zam-

borlini et al., 2014a). An alternative solution is the

development of alert systems that are independent of

the CGs. Such Computerized Physician Order Entry

systems (CPOE), are used to alert physicians about

drug interactions (Ammenwerth et al., 2008). De-

spite the usefulness of these systems, a lot can be

gained by tackling interactions between general rec-

ommendations on the outset, rather than employing

drug-interaction alerts on the hospital ﬂoor.

We have investigated this issue in a series of work.

In (Zamborlini et al., 2014a) we analyzed related

work that addresses recommendation interactions in

different levels. Our research focuses on what we

called the CIG level, i.e. it accounts for the need

to combine guidelines and handle interactions be-

fore applying them to a speciﬁc patient. This is the

case when common co-occurring diseases are consid-

ered during guideline development, but could also be

needed for uncommon co-occurring diseases in the

practice setting. The related work (Jafarpour, 2013;

opez-Vallverd

u et al., 2013; Wilk and Michalowski,

2014) has as their main drawback the need for deﬁn-

ing speciﬁc rules for each interaction, e.g. give as-

pirin & don’t give aspirin requires a speciﬁc rule

and give ibuprofen & don’t give ibuprofen requires

another rule (a more detailed analysis in (Zambor-

lini et al., 2015b)). As a consequence, they do not

provide ‘scalabe’ support for combining guidelines,

particularly more than two. Piovesan et al. (Pi-

Generalizing the Detection of Internal and External Interactions in Clinical Guidelines

113

Interactions

Interaction Type

Interacting Recommendations/Beliefs

Source

AlternativeActions

['Avoid thrombi - by should Administer NSAID', 'Avoid thrombi - by should Administer

Tramadol']

Internal

Contradiction

['Avoid gastrointestinal bleeding - by should-not Administer Aspirin', 'Avoid thrombi - by should

Administer NSAID']

Internal

ExternalAlternativeAction

['Administer Epoprostenol always causes Low Blood Coagulation', 'Avoid thrombi - by should

Administer NSAID', 'Avoid thrombi - by should Administer Tramadol']

DrugBank

ExternalIncompatibleActions

['Administer Aspirin incompatible with Administer Ibuprofen', 'Avoid thrombi - by should

Administer NSAID', 'Reduce pain - by should Administer Ibuprofen']

DrugBank

ExternalIncompatibleEffects

['Administer Bendroﬂumethiazide always causes High Level Blood Sugar', 'Reduce blood pressure

- by should Administer Thiazide', 'Reduce blood sugar level - by should Administer Insulin']

Sider

ExternalIncompatibleEffects

['Administer Ibuprofen always causes High Blood Pressure', 'Reduce blood pressure - by should

Administer Thiazide', 'Reduce pain - by should Administer Ibuprofen']

Sider

ExternalAlternativeAction

['Administer Dipyridamole always causes Low Blood Coagulation', 'Avoid thrombi - by should

Administer NSAID', 'Avoid thrombi - by should Administer Tramadol']

DrugBank

ExternalIncompatibleEffects

['Administer Ibuprofen always causes High Level Blood Sugar', 'Avoid thrombi - by should

Administer NSAID', 'Reduce blood sugar level - by should Administer Insulin']

Sider

Figure 8: Case study on combining guidelines for OA+HT+DB.

ovesan et al., 2014) propose guideline-independent

algorithms based on ontologies for detecting interac-

tions, restricted to types “concordance” and “discor-

dance”. The use of intentions associated to recom-

mendations for detecting “intention interactions” is

close to our approach on verifying transitions related

to recommendations. To the best of our knowledge,

none of the related work provides means to express

negative norms, nor negative causation beliefs. More-

over, they do not explore action type hierarchies, nor

reuse clinical knowledge available online in order to

enrich the detection of interactions. However, they do

address other aspects that we do not address yet, such

as intentions, temporal aspects and qualitative transi-

tions.

Our earlier work highlighted the importance of

having the recommendations formally represented

with a high level of detail. Explicit description of

local constraints and impact of recommendations is

considered an important source of information for in-

creased reasoning capabilities and improved explana-

tion of conﬂicts in (Bonacin et al., 2013). The model

described in (Zamborlini et al., 2014a) introduced

clinical recommendations as governing care actions

that cause state transitions; an extended version of this

model presented in (Zamborlini et al., 2014b) deﬁnes

different ways in which recommendation can inter-

act according to the referred actions and transitions.

The implementation and evaluation of the model us-

ing Semantic Web languages was proposed by us in

(Zamborlini et al., 2015b). We argued that the detec-

tion of interactions using external knowledge sources

(in our case drug interactions modeled in the Linked

Data version of DrugBank (Law et al., 2014)) can pro-

vide more precise information. A Web-based applica-

tion for browsing the guideline interactions was made

available online

. Extending this model to introduce

the notion of causation beliefs (for evidence) and the

See http://guidelines.hoekstra.ops.few.vu.nl

subsumption relations among actions was presented

in (Zamborlini et al., 2015a). It was a ﬁrst formal ex-

ercise with the goal of providing a systematic view

on possible internal interactions among recommenda-

tions.

The emphasis on evidence means that care rec-

ommendations are ultimately grounded in domain

knowledge (generalizations over facts). The evidence

that underlies the recommendations is weighed de-

pending on the quality, depth and breadth of the study:

guidelines are part of a larger network of hypotheses,

claims and pieces of evidence that span across mul-

tiple publications (de Waard et al., 2009). However,

only few CIG languages offer means to link to evi-

dence (Peleg, 2013), and they generally are targeted

to very concrete and procedural guidelines, akin to

medical protocols. In (Hoekstra et al., 2012), the

authors describe a lightweight ontology that repre-

sents the relations between a guideline, its recom-

mendations, and underlying evidence, as annotations

on the guideline and evidence texts using a combina-

tion of the Open Annotation

and PROV

formats.

Huang et al. (Huang et al., 2014) propose an even

more lightweight semantic representation of evidence

based clinical guidelines, but automatically extract it

from guideline texts. It includes UMLS identiﬁers for

medical terms appearing in the text and use proximity,

and the types of terms to infer the type and strength

of the evidence that underlies recommendations. The

Nanopublication model (Mons et al., 2011) seems to

be a natural ﬁt to modeling the evidence that under-

lies guidelines. It represents a publication as three

RDF graphs, that respectively capture an assertion

(the ﬁnding or evidence), the provenance of the as-

sertion (e.g. an experiment) and publication informa-

tion about the nanopublication (when was the asser-

tion published and by whom).

See http://www.openannotation.org.

See http://www.w3.org/TR/prov-o.

HEALTHINF 2016 - 9th International Conference on Health Informatics

114

The work presented here combines the pragmatic

approaches of (Hoekstra et al., 2012; Huang et al.,

2014) and (Mons et al., 2011) in a model that takes the

epistemological stance that the evidence underlying a

recommendation expresses a belief that a care action

causes a certain state transition. This strategy allows

for using classical logic-based languages for handling

inconsistent knowledge, such as conﬂicting ﬁndings

published in different clinical studies.

5 DISCUSSION & CONCLUSION

The work reported on in this paper improves over our

previous work by offering a more generic and scal-

able way to represent clinical guidelines and detect-

ing interactions. This is done by adapting and ex-

tending both the conceptual model and the Semantic

Web-based implementation. The TMR models and

rules are made more generic so that they can be more

easily extended to incorporate new features such as

hierarchies of transition types and causal chains. In-

corporating the epistemological nuance of beliefs in

the Semantic Web representation, improves the abil-

ity to (i) handle knowledge from different sources and

(ii) select the reliable ones; (iii) to allow different, in-

compatible beliefs about the same event to co-exist;

and (iv) to provide reusable formal rules that are ap-

plicable regardless of speciﬁc regulations, guidelines

or external sources. This has a favorable effect on

reusability, maintainability, and scalability beyond the

guidelines we currently covered.

We furthermore show the power of using the ex-

tensive domain knowledge available on the Semantic

Web for enhancing the ability to automatically per-

form new tasks, such as suggesting alternative drugs.

Our use of open standards and vocabularies, such as

the nanopublicaiton format, makes that the knowl-

edge accumulated in our own models is shareable and

reusable in a similar fashion. We implement inferenc-

ing using expressive SWI-Prolog rules that execute

over RDF graphs. The adoption of SWI-Prolog was

an improvement over the implementation in (Zam-

borlini et al., 2015b), as it gave us a single environ-

ment for expressing our inference rules beneﬁting un-

derstandability and maintainability. This, of course,

at the cost of Semantic Web standards compliance

for that speciﬁc part of our model. In (Zamborlini

et al., 2015b) the limitations of OWL2 for detecting

the interactions, forced the use of multiple knowl-

edge representation languages. We had to resort to a

combination of expressive OWL2 inferencing, Star-

dog SPARQL rules (a SWRL dialect) and custom

SPARQL update queries to perform reasoning.

The experimental assessment shows that inter-

actions can be automatically detected among three

guidelines and enriched by knowledge from Drug-

Bank and Sider, from each of which the relevant

knowledge was imported as beliefs. Although the

case study comprises only drug administration as ac-

tion types, the approach is designed to address inter-

actions among other types of interventions, such as

surgeries and exercise therapy. More complex case

studies will be addressed in future work. We faced

some issues regarding the integration with these ex-

ternal knowledge sources, particularly on deciding

which identity criteria we should use to map to the

external datasets. For example, we could choose be-

tween PubChem ID, UMLS code, dbpedia and so on,

where each choice would bring about different cover-

age and reliability.

Although this work is applied to clinical guide-

lines, its potentially of more general application, since

both the model and the rules are deﬁned indepen-

dently of a particular domain. We plan to investi-

gate the applicability of the models and rules to other

domains such as disaster management. As ongoing

work, we plan to address four limitations: (i) tem-

poral validity for the assertions; (ii) quantiﬁcation of

beliefs and norms (i.e., frequency and strength); (iii)

qualiﬁcation of transitions (e.g. increasing or decreas-

ing a property value); and (iv) considering goals and

intentions;

ACKNOWLEDGEMENTS

The ﬁrst author would like to thank colleagues from

NEMO-UFES/Brazil for fruitful discussions about

transitions, causation beliefs and regulations, and also

prof. md. Saulo Bortolon for the nice discussions

about medical domain; Jan Wielemaker and Wouter

Beek from VU for helping with SWI-Prolog imple-

mentation; Wytze Vliestra (former VU) for fruitful

discussions about the biomedical domain; and Paul

Groth from Elsevier for fruitful discussions about

the potential generality of the model and the use of

nanopublications, This work was partially funded by

the Dutch National Programme COMMIT.

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