Addressing Entity Change in Procedural Ontologies

Tyler Johnson

, Mohammed Alliheedi

2 a

, Yetian Wang

3 b

and Robert E. Mercer

1 c

Department of Computer Science, The University of Western Ontario, London, Canada

Department of Computer Science, Al-Baha University, Al Bahah 65527, Saudi Arabia

David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada

Keywords:

Experimental Procedure, Procedural Steps, Biochemical Ontology, Time series, Forward-Backward Sequenc-

ing.

Abstract:

Ontologies model a domain by representing the entities, concepts, and the relations between them. The domain

of interest in this position paper is the biochemistry experimental procedure. These procedures are composed

of procedure steps. These steps represent actions. Actions cause change, a concept being implicitly modelled

in this type of ontology. We argue that entities undergoing change need to be properly captured in the ontology.

The biochemistry procedure Alkaline Agarose Gel Electrophoresis is used to demonstrate the generality of this

procedural ontology.

1 INTRODUCTION

The representational adequacy of an ontology, which

provides entities (also known as individuals), con-

cepts, and relations among them, is determined by its

ability to model any situation that can occur in the

domain being represented. Our focus is the biochem-

istry experimental procedure domain.

Descriptions of biochemistry experimental proce-

dures exist in scientiﬁc writing. These protocols,

which typically involve several steps, are described

in detail in manuals of standard biochemistry exper-

iment procedures (Boyer, 2012; Sambrook and Rus-

sell, 2001).

This position paper builds upon previous work by

(Alliheedi et al., 2020) which describes ontologies for

two experimental procedures that exist in the man-

ual of standard biochemistry experimental procedures

(Sambrook and Russell, 2001): Alkaline Agarose Gel

Electrophoresis and Southern Blotting. In this po-

sition paper, we provide evidence that to design an

ontology that is adequate for this domain, proper la-

belling of entities that change is required. In addition,

we make explicit the connection to a measurement

ontology and indicate that the modiﬁcations conform

to two other ontologies: the geospatial ontology and

https://orcid.org/0000-0002-8434-3048

https://orcid.org/0000-0002-6984-7256

https://orcid.org/0000-0002-0080-715X

BFO. To demonstrate these modiﬁcations, we show a

portion of one example of a biochemistry experimen-

tal procedure, Alkaline Agarose Gel Electrophoresis.

2 RELATED WORK

The OBI ontology has been the foundation for several

ontologies in the ﬁeld of biochemistry procedures,

including those proposed by (Courtot et al., 2008;

Brinkman et al., 2010; Zheng et al., 2013; Soldatova

et al., 2013; Dumontier et al., 2014). These ontologies

are of great interest to our research, and we provide a

brief description of them in this section.

Soldatova and King (Soldatova and King, 2006)

proposed EXPO, an ontology of scientiﬁc experi-

ments, which oﬀers a detailed description of vari-

ous aspects of scientiﬁc experiments and their re-

lationships. In contrast, OBI describes experimen-

tal processes and relationships, and Brinkman et al.

(Brinkman et al., 2010) discuss three real-world ap-

plications that provide relevant input/output relations

for our purpose. Similarly, Zheng et al. (Zheng et al.,

2013) introduced the beta cell genomics application

ontology (BCGO), which also uses OBI to describe

experimental processes but is primarily a descriptive

ontology. However, some of the relations in RO, the

relation ontology (Smith et al., 2005), that are used in

BCGO (e.g., produces, translate to) have an ordering

sense that aligns with our research.

280

Johnson, T., Alliheedi, M., Wang, Y. and Mercer, R.

Addressing Entity Change in Procedural Ontologies.

DOI: 10.5220/0012239900003598

In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) - Volume 2: KEOD, pages 280-287

ISBN: 978-989-758-671-2; ISSN: 2184-3228

Exact (Soldatova et al., 2013) and the Semantic-

science Integrated Ontology (Dumontier et al., 2014)

are the two ontologies that are most similar to our

work. While both aim to describe sets of actions in

scientiﬁc protocols, (Alliheedi et al., 2020) represent

sequences of actions. Therefore, relations that de-

scribe orderings of actions (e.g., ‘precedes’ (Dumon-

tier et al., 2014)) are not applicable to sequences since

these relations are transitive. In addition to these on-

tologies, the Molecular Methods Database (MolMeth)

(Klingstr

om et al., 2013) contains scientiﬁc protocol

ontologies that conform to a set of laboratory proto-

col standards. Other ontologies that are useful for a

biochemistry procedure-oriented ontology include the

ontologies for processes, such as (Lenat et al., 1985;

Schlenoﬀ et al., 2000), ontology for units of measure

(Rijgersberg et al., 2013), classiﬁcation of scenar-

ios and plans (CLASP) (Devanbu and Litman, 1996),

and materials ontology (Ashino, 2010). Foundational

theories, such as process calculus and regular gram-

mar, are essential for the formalization of procedure-

oriented ontologies. The current paper builds upon

previous work by (Alliheedi et al., 2020) and provides

a detailed representation of change and an explicit

connection to the measurement ontology (Rijgersberg

et al., 2013).

3 PROCEDURE-ORIENTED

ONTOLOGY

We have proposed in (Alliheedi et al., 2020) a frame-

work for procedure-oriented ontologies. This frame-

work explicitly identiﬁes all steps of an experimen-

tal procedure and provides a set of relations to de-

scribe the relationships between these steps. This

novel approach allows creating a sequence of events

(or steps in a procedure) using the ontological concept

of “something occurs before”. An ontological con-

cept of “sequence” is used to accomplish this. This

new concept is used to model events that happen step

by step in some sort of ordering.

This approach will be used to provide ontologies

representing experimental procedures for Knowledge

Base systems with the required knowledge about the

experimental processes used in biochemistry experi-

mental settings. There are manuals of standard pro-

cedures in biochemistry (Boyer, 2012; Sambrook and

Russell, 2001) which are being used to build exten-

sions of our ontology.

In the next sections, we summarize the current on-

tological framework found in (Alliheedi et al., 2020).

This enables us to connect the focus of this current

position paper, the representation of entity change, to

this framework.

3.1 Relations

In this section, we describe various properties to sat-

isfy the deﬁnition of an experimental procedure. An

experimental procedure consists of a series of events,

that is, steps in the procedure. These steps occur in

either partial or total order. Partial ordering allows

steps (more than one step) to precede or follow an-

other step. Total ordering means that a step precedes

or follows another step, and this relation is intransi-

tive. These relations are deﬁned for OWL (McGuin-

ness and van Harmelen, 2004)

We represent the choices of a subsequence of steps

from more than one possible subsequence. Since the

choices among subsequences would be “either” or

“or”, the relation ‘optionalStepOf’ needs to be de-

signed based on the diﬀerent choices of available sub-

sequences in that particular step. To illustrate, the

‘optionalStepOf’ relation is simply an ‘exclusive or’

if there are two choices available, or else it would

be a generalization of the exclusive or. We have im-

plemented the aforementioned relations to satisfy the

deﬁnition of “procedure”.

3.2 Classes and Properties

The ontology framework described in detail in (Alli-

heedi et al., 2020) consists of three core classes: Step,

State, and Action. These classes are described in the

following sections. Class names are indicated with

capitalized words, e.g., Step. Properties are indicated

with single quotes, e.g., ‘subStepOf’. Instance names

use typewriter font, e.g., step1. When referring to

the actual steps from the protocol, normal font is used,

e.g., step 1.1.

3.2.1 Step

Each step in a procedure is represented by instances

of the Step class (see Figure 1). We deﬁned object

properties such as ‘precedes’, ‘follows’, and ‘paral-

lel’ to represent the ordering relations of each step.

Note that the aforementioned object properties are

transitive. The properties ‘precedes’ and ‘follows’,

inverses of each other, indicate the chronological or-

der between two steps. The property ‘parallel’ is sym-

metrical which indicates steps may occur simultane-

ously. Figure 1 illustrates a scenario with parallel

Available at http://www.ontologydesignpatterns.org/

cp/owl/sequence.owl

Addressing Entity Change in Procedural Ontologies

281

Figure 1: Step class and example instances. (This ﬁgure is

from (Alliheedi et al., 2020)).

steps step1 and step2. Intransitive properties ‘di-

rectlyPrecedes’ and ‘directlyFollows’ are subproper-

ties of ‘precedes’ and ‘follows’ respectively. These

properties describe the order between steps in which

a step immediately precedes or follows another step.

Similar to ‘precedes’ and ‘follows’, they are also in-

verses of each other. Therefore, by stating step1.1

‘directlyPrecedes’ step1.2, and step1.2 ‘direct-

lyPrecedes’ step1.3, a reasoner will automatically

infer that step1.1 ‘precedes’ step1.2 as well as

step1.3. Also, step1.3 ‘directlyFollows’ step1.2

but only ‘follows’ step1.1, both being inferable by

a reasoner.

Procedures are often formed by an hierarchical

structure among steps, that is, a step may consist of a

number of sub-steps required to complete. The prop-

erty ‘subStepOf’ indicates that some step(s) must be

completed for the completion of the parent step. Fig-

ure 1 shows an example of a step called step1 which

consists of step1.1, step1.2, and step1.3. Then

step1.1, step1.2, and step1.3 must be completed

in order to state that step1 is complete. The sub-step

step1.1 must complete before step1.2 and thus be-

fore step1.3. The ﬁgure also shows the property

‘optionalStepOf’. In the case where one and only one

of step1.1.1 and step1.1.2 needs to be completed

in order to complete step1.1, the property ‘option-

alStepOf’ can be used to indicate that one of the steps

(not both) must be completed in order to complete the

parent step. Both domain and range of the properties

are the class Step.

3.2.2 State

The relations between instances of the class Step out-

line the structure of a procedure. Each instance of

Step is represented as a set of states and are associ-

ated to a set of actions. A step involves a transition

from state to state via a single or a series of actions,

Figure 2: State and Action classes. (This ﬁgure is from

(Alliheedi et al., 2020)).

represented by the classes State and Action (see Fig-

ure 2).

The State class is connected to the Step class via

the property ‘hasState’. It has three subclasses, Ini-

tialState, MidState, and FinalState. The subclasses

are connected via properties that include ‘precedes’

and ‘follows’. InitialState can only precede a Mid-

State or a FinalState. FinalState can only follow an

InitialState or a MidState. MidState can ‘follow’

an InitialState and ‘precede’ a FinalState, as well as

‘precede’ or ‘follow’ another MidState. The triple

(stateX, ‘precedes’, stateY) implies (stateY, ‘fol-

lows’, stateX), ‘follows’ being an inverse property

of ‘precedes’. Figures 1 and 2 omit ‘follows’ to keep

the ﬁgures clean. Note that a step has at most one in-

stance of InitialState or FinalState but may have mul-

tiple instances of MidState. For example, an instance

of Step, step1, may involve two instances of State,

i.e., step1 state1 and step1 state2, represented

by the following triples: (step1, hasState, state1),

(step1, ‘hasState’, state2), (state1, ‘precedes’,

state2), in which state1 and state2 are instances

of InitialState and FinalState respectively. The State

class also connects to classes Restriction and Enti-

ties, which are discussed in Section 3.2.4 and 3.2.5,

respectively.

3.2.3 Action

States are connected to the Action class via

‘beforeState’ and ‘afterState’, representing the states

before and after an action, respectively. In other

words, an instance of Action would transition an in-

stance of State to another. For example, when an ac-

tion action1 is performed in state1, state1 will

be modiﬁed and thus transitioned into a new state

state2. This would be represented by the triples

(action1, ‘beforeState’, state1) and (action1,

‘afterState’, state2).

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

282

Figure 3: Demonstration of Entities class. This ﬁgure is

from (Alliheedi et al., 2020).

3.2.4 Restriction

A Restriction class was created to represent cer-

tain limitations applied to a state in a step. It is

linked to the class State via ‘hasRestriction’. For

example, (state1,‘hasRestriction’,restriction1)

means that restriction1 will be checked against

state1. It may be suﬃcient to apply a restriction to

a FinalState but it is also possible to apply restrictions

to all states in a step.

3.2.5 Entities and Biochemistry Domain

Knowledge

The State class is connected to the Entities class (see

Figure 3) via the property ‘involves’. Domain knowl-

edge of biochemistry can be described by extending

the Entities class with subclasses such as Instruments,

Materials, and Devices involved in a speciﬁc state.

For demonstration purposes, we have included only

selected general concepts related to experimental pro-

cedures described in Section 4. The class Instrument

includes Container and Device where Container ‘con-

tains’ Material which is a class for Chemical and Non-

Chemical materials used in biochemistry experiment

procedures. Compound materials and assembled in-

struments are represented using the property ‘consist-

sOf’. Instrument and Material can be connected to

the class Measure which is a combination of numer-

ical values and Unit of Measure, e.g., 10m is a mea-

sure where the value is 10 with a unit of measure of

meter (Rijgersberg et al., 2013). The Measure class

was extended with subclasses to represent absolute

measures (e.g., 10m), range values (e.g., 5m-10m), and

ratio (e.g., 1/2).

4 ITEM LABELLING

The main contribution of this position paper is to pro-

vide direction in how to represent the changes in en-

tities during the experimental procedure in the ontol-

ogy. We use the Alkaline Agarose Gel Electrophore-

sis procedure (see Figure 4) to demonstrate our ideas.

We have generated a timeline to follow the changes in

all of the entities found in this procedure. A portion of

this timeline is given in Figure 5. We will now detail

the new items in this timeline.

4.1 Details of the Timeline

The timeline in Figure 5 shows our proposed method

for tracking changes to entities in the procedure on-

tology. First, entities have “identiﬁers” (e.g., item1).

The relation ‘alias’ is used when an entity changes

its identiﬁer. The relation ‘becomes’ is used to in-

dicate a change in some property of an entity. Ex-

amples of these properties are temperature (e.g., the

slurry in the ﬂask is heated) or a change in what

constitutes the entity (e.g., the empty ﬂask is ﬁlled

with slurry). We have adopted a naming scheme

for the entities. The name contains the item name,

the step in the procedure, and a short description of

the property change. An example of this naming

scheme is item1 step1.1 mixture which becomes

item1 step1.1 mixtureheated.

For entity A to “become” entity B, the following

must be satisﬁed:

• A and B must have the same identity (we follow

Sider’s view of identity, that is, an entity main-

tains its identity throughout time (Sider, 2003)).

• A measurable change has occurred with respect to

entity A and thus is transitioned to entity B.

• If entity B is a compound material or assembled

instrument based on entity A the ‘becomes’ rela-

tion also has the eﬀect of a ‘consistsOf’ relation

in the reverse direction.

Another relation in the timeline which helps to

track the changes in the entities is ‘consistsOf’. We

take the deﬁnition of this relation from the work sum-

marized in Section 3.2.5: this relation connects the

entity which is a compound material or assembled in-

strument with the components that comprise it.

It is important to note that the use of these re-

lations can be subjective (a matter of choice) or

objective (given by the steps in the procedure de-

scription). For example, there is an ‘alias’ relation

between glassbottle1 and Container1 and an-

other between flask1 and Container1. These re-

lations are objectively given in the procedure since

Addressing Entity Change in Procedural Ontologies

283

Alkaline Agarose Gel Electrophoresis

1. Prepare the agarose solution

1.1. Adding the appropriate amount of powdered agarose to a measured

quantity of H2O in either:

1.1.1. An Erlenmeyer ﬂask (Container 1)

1.1.1.1. Loosely plug the neck of the Erlenmeyer ﬂask with Kimwipes

1.1.2. OR a glass bottle (Container 1)

1.1.2.1. Make sure that the cap is loose

1.2. Heat the slurry (Item1) in (Container1) for the minimum time required

to allow all of the grains of agarose to dissolve using either:

1.2.1. A boiling-water bath

1.2.1.1. Check that the volume of the solution (Item 1) has not been

decreased by evaporation during boiling in (Container 1):

1.2.1.1.1. if yes: replenish with H2O in (Container 1)

1.2.1.1.2. If no: do not add H2O in (Container 1)

1.2.2. OR a microwave oven

1.2.2.1. Check that the volume of the solution (Item 1) has not been

decreased by evaporation during boiling in (Container 1):

1.2.2.1.1. if yes: replenish with H2O in (Container 1)

1.2.2.1.2. If no: do not add H2O in (Container 1)

1.3. Cool the clear solution (Item 1) to 55 C.

1.3.1. Add 0.1 volume of 10x alkaline agarose gel electrophoresis buﬀer in

(Container 1)

1.3.2. And immediately pour the gel (Item 1) into mold (Container 2)

1.4. After the gel (Item 1) is completely set

1.4.1. Mount it (Item 1) in the electrophoresis tank (Container 3)

1.4.2. Add freshly made 1x alkaline electrophoresis buﬀer until the gel

(Item 1) is just covered.

2. Prepare DNA samples

2.1. Collect the DNA samples (Item 2) by standard precipitation with

ethanol2

2.2. Dissolve the damp precipitates of DNA (Item 2) in 10-20 µl of 1x gel

buﬀer. (Item 3)

2.3. Add 0.2 volume of 6x alkaline gel-loading buﬀer

2.3.1. It is important to chelate all Mg2+ with EDTA before adjusting the

electrophoresis samples to alkaline conditions

3. Initiate the electrophoresis

3.1. Load the DNA samples dissolved in 6x alkaline gel-loading buﬀer into

the wells of the gel (Container 3)

3.2. Start the electrophoresis at < 3.5 V/cm when the bromocresol green has

migrated into the gel approx. 0.5-1 cm; Turn oﬀ the power supply, and

place a glass plate on top of the gel in (Container 3) and then continue

electrophoresis until the bromocresol green has migrated

approximately two thirds of the length of the gel in (Container 3).

4. Finalize the experiment

4.1. Process the gel according to one of the procedures either Southern

hybridization by:

4.1.1. Transfer the DNA either:

4.1.1.1. Directly (without soaking the gel) from the alkaline agarose gel to a

charged nylon membrane. Please see Southern Blotting: Capillary

Transfer of DNA to Membranes

4.1.1.2. OR after soaking the gel in neutralizing solution for 45 minutes at

room temperature to either:

4.1.1.2.1. An uncharged nitrocellulose as described in Southern Blotting:

Capillary Transfer of DNA to Membranes

4.1.1.2.2. OR nylon membrane as described in Southern Blotting: Capillary

Transfer of DNA to Membranes

4.1.2. Detect the target sequences in the immobilized DNA by hybridization

to an appropriate labeled probe. Please see Southern Hybridization of

Radiolabeled Probes to Nucleic Acids Immobilized on Membranes

4.2. OR Staining

4.2.1. Soak the gel in neutralizing solutions for 45 minutes at room

temperature

4.2.1.1. Stain the neutralized gel with 0.5 µg/ml ethidium bromide in 1x

TAE or with SYBR Gold

4.2.1.1.1. A band of interest can be sliced from the gel and subsequently

eluted by one of the procedures described Recovery of DNA from

Agarose Gels

Figure 4: The steps of Alkaline Agarose Gel Electrophoresis.

once the type of container is chosen, the proce-

dure continues and performs the same steps using

the container labelled as Container1. On occasion

the choice to use a ‘becomes’ relation is subjective.

For example, item1 step1.3 cooled ‘becomes’

item1 step1.3.1 with10xAgarose and ‘consist-

sOf’ 10xAgarose and item1 step1.3 cooled, by

virtue of the ‘becomes’ relation.

4.2 Measurement Ontology

The ontology presented in Section 3 uses the concept

of measurement. Here, we have made this precise by

incorporating the Ontology of Units of Measure and

Related Concepts (OM) (Rijgersberg et al., 2013).

In Figure 5 connections with the Measurement

Ontology are indicated by green relations and are la-

belled with a label preﬁxed with “OM:”. With these

explicit measurements, the ontology is able to com-

pare measurements of the changing entities. These

comparisons are indicated by red relations. Some of

the measurements are quantitative (e.g., 55

◦

C) and

some are qualitative (e.g., heated).

To reduce the number of lines in Figure 5, we as-

sume that measurements are transitive with respect to

the ‘becomes’ property. To add, properties will be

transitive unless they are overridden by new measure-

ments of the same type; however, in practice the mea-

surements are not transitive and thus need to be con-

nected to each one that they apply to. For example,

if the temperature does not change in an entity, it will

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

284

Figure 5: Proposed Timeline for the Alkaline Agarose Gel Electrophoresis Procedural Ontology.

Addressing Entity Change in Procedural Ontologies

285

need to be linked back up to the previous measure-

ment of the temperature.

At times, the ontology does not specify a speciﬁc

quantity for a measure but allows the use of the on-

tology to determine that quantity. For instance, in

Step 1.1 “appropriate amount of powdered agarose”

and “measured quantity of H2O” will give the desired

concentration to be used in the preparation of the gel

which is determined by the size of the protein being

studied (Lee et al., 2012).

4.3 Other Ontologies

There are other ontologies which seek to map out

a series of events. One ontology is from (Kaup-

pinen and Hyv

onen, 2007; Kauppinen et al., 2008)

where they seek to describe changes in an arbitrary

geospatial region. In particular, they create a ontol-

ogy out of diﬀerent areas and their changes of Fin-

land since the start of the 20th century. Using what

are called “change bridges”, they connect diﬀerent

regions as they grow and change throughout time.

In contrast to our work which uses object proper-

ties to link changes in our entities, (Kauppinen and

Hyv

onen, 2007; Kauppinen et al., 2008) uses individ-

uals of a certain type of “change bridge” to represent

what kind of change has occurred in the region(s).

The object properties “before” and “after” are con-

nected from the bridge to the regions to give direction

to the ﬂow of time. This works analogously to the

Action class in Section 3.2.3 with each state in time

linked to an instance of Action by ‘beforeState’ and

‘afterState’.

Another ontology which aims to represent series

of events is the Basic Formal Ontology (BFO) (Arp

et al., 2015). Unlike the previously mentioned ontol-

ogy, this is a top-level ontology which aims to sup-

port information retrieval and analysis of other sci-

entiﬁc ontologies across a myriad of individual do-

mains. In particular, we look at the classes of Contin-

uant and Occurant in BFO. The former represents the

concept of continuants. These entities will continue

or persist throughout time. In contrast, occurants are

entities which occur or happen. With respect to the

ontology in Section 3, there are continuants such as

a container or an instrument which can be la-

belled with the appropriate subclasses of Continuant.

Whereas, there are occurants such as the instances of

the Action class, which can be labelled with the ap-

propriate subclasses of Occurant.

5 CONCLUSIONS

In this position paper, evidence is provided showing

that to design an ontology that is adequate for the

biochemistry experimental procedure domain, entities

that change must be properly labelled. We also make

explicit the connection to a measurement ontology

(Rijgersberg et al., 2013) and indicate that the modiﬁ-

cations conform to two other ontologies: time (Kaup-

pinen and Hyv

onen, 2007; Kauppinen et al., 2008)

and BFO (Arp et al., 2015). We demonstrate these

modiﬁcations in Figure 5 with a portion of one exam-

ple of a timeline of entity changes in a biochemistry

experimental procedure, Alkaline Agarose Gel Elec-

trophoresis in Figure 4, and how the measurement on-

tology is integrated. We have also done a timeline for

another experimental procedure: Southern Blotting.

Going forward, we will incorporate the timeline

into the ontology classes that have been described in

Section 3. The class hierarchies provided in Section

4 indicate that the presented ontology will accept this

modiﬁcation. Details how to accomplish this will be

developed in the near future.

ACKNOWLEDGEMENTS

We would like to thank all reviewers for their com-

ments, which improved this paper. This research is

funded by an Undergraduate Student Research Intern-

ship (USRI) from the University of Western Ontario

to T. Johnson, and from The Natural Sciences and

Engineering Research Council of Canada (NSERC)

through a Discovery Grant to R. E. Mercer.

REFERENCES

Alliheedi, M., Wang, Y., and Mercer, R. E. (2020). De-

sign of a biochemistry procedure-oriented ontology.

In Knowledge Discovery, Knowledge Engineering and

Knowledge Management. IC3K 2019, volume 1297 of

Communications in Computer and Information Sci-

ence. Springer.

Arp, R., Smith, B., and Spear, A. D., editors (2015). Build-

ing Ontologies with Basic Formal Ontology. The MIT

Press.

Ashino, T. (2010). Materials Ontology: An infrastructure

for exchanging materials information and knowledge.

Data Science Journal, 9:54–61.

Boyer, R. F. (2012). Biochemistry Laboratory: Modern

Theory and Techniques. Prentice Hall.

Brinkman, R. R., Courtot, M., Derom, D., Fostel, J. M.,

He, Y., Lord, P., Malone, J., Parkinson, H., Peters,

B., Rocca-Serra, P., Ruttenberg, A., Sansone, S.-A.,

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

286

Soldatova, L. N., Jr., C. J. S., Turner, J. A., Zheng, J.,

and the OBI consortium (2010). Modeling biomedical

experimental processes with OBI. Journal of Biomed-

ical Semantics, 1 (Suppl 1):S7.

Courtot, M., Bug, W., Gibson, F., Lister, A. L., Malone,

J., Schober, D., Brinkman, R. R., and Ruttenberg, A.

(2008). The owl of biomedical investigations. In Pro-

ceedings of the Fifth OWLED Workshop on OWL: Ex-

periences and Directions, page 12pp.

Devanbu, P. T. and Litman, D. J. (1996). Taxonomic plan

reasoning. Artiﬁcial Intelligence, 84(1-2):1–35.

Dumontier, M., Baker, C. J., Baran, J., Callahan, A., Che-

pelev, L., Cruz-Toledo, J., Rio, N. R. D., Duck, G.,

Furlong, L. I., Keath, N., Klassen, D., McCusker,

J. P., Queralt-Rosinach, N., Samwald, M., Villanueva-

Rosales, N., Wilkinson, M. D., and Hoehndorf, R.

(2014). The Semanticscience Integrated Ontology

(SIO) for biomedical research and knowledge discov-

ery. Journal of Biomedical Semantics, 5(1).

Kauppinen, T. and Hyv

onen, E. (2007). Modeling and rea-

soning about changes in ontology time series. In Shar-

man, R., Kishore, R., and Ramesh, R., editors, On-

tologies, volume 14 of Integrated Series in Informa-

tion Systems, pages 319–338. Springer.

Kauppinen, T., Hyv

onen, E., and V

ainen, J. (2008). Cre-

ating and using geospatial ontology time series in a

semantic cultural heritage portal. In Bechhofer, S.,

Hauswirth, M., Hoﬀmann, J., and Koubarakis, M., ed-

itors, The Semantic Web: Research and Applications,

volume 5 of ESWC: European Semantic Web Confer-

ence, pages 110–123. Springer.

Klingstr

om, T., Soldatova, L., Stevens, R., Roos, T. E.,

Swertz, M. A., M

uller, K. M., Kala

s, M., Lambrix,

P., Taussig, M. J., Litton, J.-E., Landegren, U., and

Bongcam-Rudlof, E. (2013). Workshop on laboratory

protocol standards for the Molecular Methods Data-

base. New Biotechnology, 30(2):109–113.

Lee, P. Y., Costumbrado, J., Hsu, C. Y., and Kim, Y. H.

(2012). Agarose gel electrophoresis for the separation

of dna fragments. Journal of Visualized Experiments,

62.

Lenat, D. B., Prakash, M., and Shepherd, M. (1985). CYC:

Using common sense knowledge to overcome brittle-

ness and knowledge acquisition bottlenecks. AI Mag-

azine, 6(4):65–65.

McGuinness, D. L. and van Harmelen, F. (2004). OWL

web ontology language overview. W3C Rec-

ommendation, World Wide Web Consortium.

http://www.w3.org/TR/2004/REC-owl-features-

20040210/.

Rijgersberg, H., Van Assem, M., and Top, J. (2013). Ontol-

ogy of units of measure and related concepts. Seman-

tic Web, 4(1):3–13.

Sambrook, J. and Russell, D. W. (2001). Molecular

Cloning: A Laboratory Manual. Cold Spring Harbor

Laboratory Press.

Schlenoﬀ, C., Schlenoﬀ, C., Tissot, F., Valois, J., and Lee,

J. (2000). The Process Speciﬁcation Language (PSL)

Overview and Version 1.0 Speciﬁcation. NISTIR

6459, National Institute of Standards and Technology.

Sider, T. (2003). Four-Dimensionalism: An Ontology of

Persistence and Time. Clarendon Press.

Smith, B., Ceusters, W., Klagges, B., K

ohler, J., Kumar, A.,

Lomax, J., Mungall, C., Neuhaus, F., Rector, A. L., ,

and Rosse, C. (2005). Relations in biomedical ontolo-

gies. Genome Biology, 6(5):R46.

Soldatova, L., King, R., Basu, P., Haddi, E., and Saunders,

N. (2013). The representation of biomedical proto-

cols. EMBnet.journal, 19(B).

Soldatova, L. N. and King, R. D. (2006). An ontology of

scientiﬁc experiments. Journal of the Royal Society

Interface, 3(11).

Zheng, J., Manduchi, E., and Jr, C. J. S. (2013). Develop-

ment of an application ontology for beta cell genomics

based on the ontology for biomedical investigations.

In 4th International Conference on Biomedical Ontol-

ogy, pages 62–67.

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