=Paper= {{Paper |id=Vol-2807/paperD |storemode=property |title=Towards a Framework for Meaning Negotiation and Conflict Resolution in Ontology Authoring |pdfUrl=https://ceur-ws.org/Vol-2807/paperD.pdf |volume=Vol-2807 |authors=Rolf Grütter,C. Maria Keet |dblpUrl=https://dblp.org/rec/conf/icbo/GrutterK20 }} ==Towards a Framework for Meaning Negotiation and Conflict Resolution in Ontology Authoring== https://ceur-ws.org/Vol-2807/paperD.pdf

July 2020

Towards a Framework for Meaning
Negotiation and Conflict Resolution
in Ontology Authoring
Rolf GRÜTTER a,1 and C. Maria KEET b
a Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
b Department of Computer Science, University of Cape Town, South Africa

Abstract. Ontology authoring involves making choices about what subject domain
knowledge to include. This may concern sorting out ontological differences as well
as making choices of conflicting axioms due to limitations in the logic. Examples
are different foundational ontologies in ontology matching and OWL 2 DL’s transi-
tive object property versus qualified cardinality constraints. Such conflicts have to
be resolved. However, there is currently only isolated and fragmented guidance for
doing so, which therefore results in ad hoc decision-making. This work aims to ad-
dress this by working towards a framework dealing with the various types of mod-
eling conflicts through meaning negotiation and conflict resolution in a systematic
way. The approach was evaluated with an actual case of domain knowledge usage
in the context of epizootic disease outbreak.
Keywords. Meaning Negotiation, Conflict Resolution, Ontology Authoring,
Infectious Disease, Disease Control

1. Introduction

An increase in the use of ontologies brings with it the task of reusing existing ones, which
is already an aspect of the OBO Foundry approach [1] and incorporated in ontology de-
velopment methodologies such as NeON [2]. This may be as a single ontology, or im-
ported, merged, or integrated with another. It can become difficult to assess potential for
(re)use, as discussed in detail for, e.g., parthood theories [3,5] and deciding on a top-level
ontology [6]. Since one feature of ontologies is to tease out subtle differences in mean-
ing, a candidate ontology for use or import either may not have all the desired axioms,
have too many axioms, or upon import it may result in an inconsistent or incoherent on-
tology, be beyond the desired OWL species or otherwise incompatible with one’s pre-
ferred ontology language. Examples of overlap and reuse experiences vary widely also
in the biology domain; recent examples include practical reuse of the Infectious Diseases
Ontology for schistosomiasis knowledge [7], the modular design and many reuses of the
Gene Ontology [8], and subtle differences across disease ontologies [9], among many.
Issues may include, among others, merging two domain ontologies that are aligned to
1 Corresponding Author: Rolf Grütter, Swiss Federal Research Institute WSL, Zürcherstrasse 111, CH-8903

Birmensdorf, Switzerland; E-mail: rolf.gruetter@wsl.ch.

Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
R. Grütter and C.M. Keet /

two different foundational ontologies, a class Infection versus a property infected-by, or
one ontology has Virus as a living thing and the other does not.
How to proceed? While one could discard a relevant ontology and start afresh, we
assume that a user may wish to attempt to resolve any issues that may arise. A few tools
are available to assist with detecting and inspecting issues; e.g., the explanation features
in Protégé [10], assessing the differences in inferences [11], and the OWL Species Clas-
sifier2 that lists which axiom(s) violate which OWL species. Also, there are a priori
choices one can make in comprehensiveness vs. expressiveness [3] and testing for con-
flicts [16], as compared to a try-and-see approach. These methods and tools will not de-
tect all sources of conflicts, however, such as between fundamental assumptions about a
domain or preferred theories. For instance, choosing parthood or connection or both as
primitive for a mereotopological theory [13], and whether it is “better” for one’s domain
to declare parthood as transitive or use it in qualified number restrictions (since one can-
not have both in OWL 2 DL [14]). Consider, e.g., some ontology O1 about anatomy that
has declared that a biped is an animal that has part exactly two legs: when it is aligned
to DOLCE, it will clash with dolce:has-part that is declared transitive. What can the on-
tology engineer do? One could i) decide to not merge with DOLCE, ii) give up on the
qualified cardinality constraint and modify the definition of biped, iii) import DOLCE
separately and remove transitivity making it de facto incompatible with DOLCE, iv) ac-
cept to go beyond OWL 2 DL and use a different logic, or v) forsake automated rea-
soning over one’s ontology. The consequences of each choice would then still have to
be assessed somehow. This may leave the ontologist with ad hoc attempts of trial and
error, and therewith hampering redeployment of ontologies, also because the possible
consequences of possible solutions may not be clear.
This work aims to contribute to addressing these obstacles by devising a novel ap-
proach for meaning negotiation in ontology development and (re)use, and for conflict
resolution. We examine the possible principal sources of conflicts for both individual
ontologies and multiple ontologies. For each case, there is a fixed set of feasible solution
strategies, so that then explanatory implications may be automatically generated. Some
of the components can be computed automatically, whereas others require the human-in-
the-loop to make the final, but now well-informed, decision. The approach is illustrated
and evaluated with a case of domain knowledge usage to manage an epizootic disease
outbreak in Switzerland (avian influenza), which involved negotiation and resolving con-
flicts regarding the ‘appropriate’ mereotopological theory, its trade-off with the OWL
species, and exceeding OWL 2 DL when combining ontologies.
The remainder of the paper is structured as follows: An approach to meaning negoti-
ation and conflict resolution is introduced in Sections 2 and 3, respectively. The use case
is presented in Section 4. A conclusion is drawn in Section 5.

2. Characterizing Meaning Negotiation and Conflict Resolution

Negotiating the meaning of the knowledge to be represented in an ontology involves
reaching an agreement on: 1) the exact elements required, 2) the domain theory that
will provide these elements, and 3) the required ontology language to represent the for-
mer. This may involve meaning negotiation and conflict resolution, which will be disam-
2 https://github.com/muhummadPatel/OWL Classifier/
R. Grütter and C.M. Keet /

Figure 1. Sample scenario (summarized) of detecting and resolving conflicts in an ontology reuse scenario
where ontology Onto2 is being imported into ontology Onto1.

biguated and illustrated first. Potential sources of conflict are identified and the resolution
processes elaborated on in more detail.

2.1. Types and Sources of Conflicts

We define informally the notions of meaning negotiation and conflict resolution, illustrate
them, and then outline how they arise.

Meaning negotiation concerns deliberations to figure out the precise semantics one
wants to represent in the ontology. They are all positive choices in the sense of
‘which of the options is applicable? then we take that one’.
Conflict resolution concerns choosing one option among a set of two or more options,
where that choice is deemed the ‘lesser among evils’ for that scenario, necessarily
involves a compromise, and making it work requires reengineering something in
at least one of the ontologies or as a whole. Subtypes:
Language conflict resolution A conflict arises within the same family of lan-
guages or with a more distant one. This is a zero-sum game (i.e., with a win-
ner and a loser) or there may be a joint outside option.
Ontological conflict resolution The ontologies adhere to different theories. They
may be foundational philosophical decisions that affect the overall structure
of the ontology or subject domain arguments with competing theories. This
is likely a zero-sum game (no joint outside option).

They are illustrated in the following example.

Example 1 Meaning negotiation may include assistance with explanations, such as
when modelers are not sure whether they need a mereological theory with or without
atom, offering them a dialogue “if you add Atom to ground mereology, then you obtain
the following novel deductions [listing]; do you want that?” or frame negotiation of al-
ternative commitments as an imperative, e.g., “take either parthood or proper parthood
as primitive for your mereological theory, but not both.”.
R. Grütter and C.M. Keet /

Conflict resolution applies to many situations. For instance, there are several possible
types of language conflicts, of which a few common ones are:
• A typical example of a conflict within a language family, such as the Description
Logics-based OWL species, is the conflict of either transitivity or qualified cardi-
nality constraints, but not both, with the same object property, as illustrated in the
Introduction with biped and has-part.
• A syntax-level conflict, such as having to merge an ontology represented in CLIF
and another one represented in OWL, or OBO and OWL.
• A language’s semantics issue: e.g., ontologies represented in different languages
where one has a model-theoretic semantics and the other a graph-based one, and
open vs closed world assumption.
What to do then? Besides choosing either, there may be a so-called ‘joint outside op-
tion’ (a term from game theory) where neither wins, but there is an alternative option.
For instance, instead of debating over CLIF or OWL, one can keep both and move out-
side either setting and into the DOL framework [4], as illustrated in [3], and instead of
transitivity vs qualified cardinality, leave OWL to choose CLIF.
An example of an ontological conflict is a clash in the top-level organisation of the on-
tology, such as between BFO and GFO, and related philosophical differences, such as
whether concepts are allowed in the ontology. At the subject domain level, this may be,
e.g., whether a virus is a living thing or not, and, more generally, competing scientific
theories. In principle, they do not have a joint outside option, other than reverting it back
to the domain experts to resolve this by, e.g., conducting experiments in the lab. ♦

Where do such conflicts come from? The sources of issues arising can be manifold.
Six principal cases were discerned, which are non-exclusive and possibly not exhaustive:
1. Ontological differences between established theories; e.g., extensional mereology
(EM) vs. minimal mereology (MM) and DOLCE vs. BFO as top-level ontology.
2. Ontological differences at the axiom-level; e.g., whether part-of is antisymmetric.
3. Different modeling styles; e.g., foundational ontology-inspired or conceptual
model-influenced; e.g., reification or not [15], like the Infection/infected-by men-
tioned in the Introduction.
4. Logic limitations causing conflicts for an ontology, affecting the software ecosys-
tem; e.g., the biped’s has-part being either transitive or have it participate in ax-
ioms with qualified cardinality constraints in OWL 2 DL. Resolution options in-
clude considering tools outside the Semantic Web infrastructure.
5. Logic limitations by design for scalability; e.g., there are axioms in one’s ontology
that are beyond the desired OWL species, so that one has to choose to abandon the
preferred species or remove the axioms.
6. Certain deductions made by the reasoner (excluding modeling mistakes); e.g., an
unsatisfiable class due to signed but disjoint ancestors. While this may also have
as source an ontological difference at the axiom-level, it manifests either after
adding the axioms, during test-driven development (TDD) [16], or upon ontology
matching attempts.
The first three are, in principle, a priori negotiations by an ontologist, but may manifest
only upon ontology matching. Cases 4 and 5 emerge during ontology authoring. The last
one may or may not be a priori.
R. Grütter and C.M. Keet /

2.2. The Conflict Set

Conflict detection offers opportunities for automation and, even though there is no single
way of how conflicts can be detected, some tasks can be carried out with the aid of
state-of-the-art ontology development environments (ODEs) (see Section 3).
The data structure in which the detected conflicts are stored, and upon which the
resolution of conflicts operates, is called conflict set. A conflict set is generated in all
cases where a conflict is detected. We will illustrate this in Section 4. Without loss of
generality, it is assumed that, when matching more than two ontologies, a conflict set
is generated for every pair. Conflict sets can be described in a context-free grammar in
Backus-Naur Form as follows (the productions for most terminals are omitted):
::= []
::= [] {} []
::= "OWL DL" | "OWL Lite" | "OWL Full" | "OWL 2 EL" |
"OWL 2 QL" | "OWL 2 RL" | "OWL 2 DL" | "OWL 2 Full" |
"FOL" | "HOL"
::= [] [] {}
{}
::= | |
::= difference between the inferred axioms sets
of the two ontologies

Accordingly, there are two ontologies (or two fragments of the same ontology), each
identified by an IRI (or another identifier) and composed of a (possibly singleton) set of
axioms. An axiom may adhere to an ontologically well-founded theory, such as BFO or
ground mereology.

3. Resolving Conflicts

In practice, conflict resolution often starts with some issue raised by the ODE, specif-
ically, when an axiom is added, or an ontology is merged or integrated into the active
ontology. Examples of such issues are undecidability, language profile violation, and in-
coherence. They can be seen as cues indicating that something is wrong with the active
ontology. The author then has to find out what raised the issue. Thereby, they may be sup-
ported by the ODE. Proceeding that way is not as straightforward as one might expect,
because there is no one-to-one correspondence between conflict and issue. Examples of
such ‘causal investigations’ will be given in Section 3.2–3.5. For the rest of this section,
the following principal choices are presupposed:
(i) The author sticks to Occam’s razor when authoring an ontology for the case at hand:
the least expressive language in which the required axioms can be represented fully
is preferred over all more expressive ones.
(ii) The author wants to capture as much of the semantics of the domain theory as
possible.
(iii) The author prefers a decidable language over FOL or HOL for representing a do-
main theory and a coherent ontology over an incoherent one.
The first point is a general principle in almost every situation in life. The second point
assumes that the author prefers representing a full axiomatization over a partial axiomati-
zation and, by extension, a partial one is better than mere primitives without any axioms.
R. Grütter and C.M. Keet /

While the third point may not hold in all situations, we deemed it realistic to include,
since most software infrastructure caters for decidable ontology languages and coherent
ontologies, and Semantic Web and Knowledge Graph languages in particular.

Table 1. A sample of conflicts possibly emerging during ontology authoring
No. Conflict Description Examples
Conflicting theories at the top-level
1 foundational ontologies adhere to conflicting theories BFO, DOLCE, GFO, SUMO, UFO,
YAMATO
2 mereological conflicting mereological theories with vs. without Atom, whether part-
hood is antisymmetric or not, weak vs.
strong supplementation
Conflicting theories at the subject domain level
3 domain theory competing theories monotheism vs. polytheism, marxism
vs. leninism
4 status of an element competing (scientific) theories whether virus is a living thing or not
Axiom-level conflicts
5 ontological conflicting theories acting out on the see rows 1–3
axiom-level
undecidable violation of a language pro- some of the non-admissible axiom com-
within-language file binations in Example 2
6
family decidable violation of a language profile functional and transitive properties in
OWL 2 QL
Other conflicts
applied vs. foundational there is / there is no data property axiom
7 modeling style
class vs. object property Infection vs. infected-by

3.1. Conflicting Theories

If an ontologically well-founded theory underlying some axioms to add or an ontology
to integrate is in conflict with the ontology representing the desired theory (nos. 1–3 in
Table 1), the respective IRIs must be added to the conflict set. This presupposes that the
conflict is known and the pair of IRIs is listed somewhere, for instance, in a library of
common conflicts. To give an example, if one considers adding the part-whole relations
taxonomy that happens to be aligned with DOLCE to a BFO-aligned infectious disease
ontology (IDO), then the theory conflict (BFO vs. DOLCE) will be detected by looking
up the library of common conflicts. Conflict resolution, in this case, aims at preserving a
consistent theory. Since for conflicting theories there is no joint outside option, the ontol-
ogy author has to decide in favor of one theory and discard the other. Their decision may
be informed by the deliberations of what should be represented in the ontology made
during meaning negotiating.
State-of-the-art ODEs support the import of ontologies. After import, their IRIs can
be read from the metadata of the active ontology and looked up manually for common
conflicts in a library. Accordingly, this conflict detection approach is straightforward to
implement. Uncommon conflicts are harder to detect, hence to resolve. The use of on-
tology design patterns for a theory would be helpful in automating detection, as would
annotations. Further, the library of common conflicts may grow upon finding more con-
R. Grütter and C.M. Keet /

flicts, so that it can prevent the same or a similar conflict from emerging later on in the
project.

3.2. Conflicts Manifesting Themselves in an Undecidable Language

See nos. 5–6 in Table 1. Here, conflict resolution aims at preserving a decidable ontology
language or raising awareness of undecidability when opting for a joint outside option.
In the first case, this most often is a zero-sum game: the ontology author has to chose
which ones of some conflicting axioms in the conflict set to keep. For mereotopological
theories, these types of conflicts are well-investigated [3]. In most instances, incorporat-
ing a full axiomatization renders the active ontology at least undecidable, and possibly
also incoherent (i.e., with at least one unsatisfiable class [17]). To support the author’s
decision, some criteria can be established such as the following:
• Least number of axioms affected;
• preferred axiom type identified by assigning weights;
• least number of inferences lost.
The least number of axioms affected can be read from the conflict set. Assigning weights
to axiom types implies that certain types are a priori considered more valuable than oth-
ers. For instance, one may weigh existentials more than universals and unqualified car-
dinality more than qualified cardinality. The least number of inferences lost requires an
additional step at which the inferences of the ontologies are computed and recorded in
the conflict set. If undecidability is caused by an ontological conflict at the axiom-level
that was not resolved along with conflicting theories (e.g., weak vs. strong supplementa-
tion in Example 2), then also the decisions taken when negotiating meaning upfront may
serve as a criterion. The authors’ decision and the criteria upon which it is based should
be recorded, in case the same or a similar conflict emerges later on in the project.
The second case (opting for a joint outside option) requires that principal choice (iii)
(preferring a decidable language) is relaxed. Theories that are represented in different
logics can be dealt with by the DOL framework [4]. This includes cases where the re-
sulting logic is undecidable.
State-of-the-art ODEs provide some support for detecting and resolving these kinds
of conflicts. To give an example, the OWL API [18] of Protégé 5.2 [19] issues an error
message reporting the conflict arising from a violation of the expressive OWL 2 DL spec-
ification, caused by a non-admissible axiom combination such as those listed in Example
2. In addition, Protégé 5.2 is equipped with an OWL reasoner, and a diff tool for com-
puting the differences between OWL ontologies is available as a plug-in [20]. In order
to compute the number of inferences lost, the axioms inferred from the merged ontology
are first computed using the OWL reasoner. This requires that the merged ontology is
saved as two decidable versions by removing one conflicting axiom set in exchange for
the other. The difference between the sets of inferred axioms is then computed.

Example 2 Some mutually exclusive axiom combinations are as follows:
• Within language family: OWL 2 DL with transitivity or role chain excludes any
of minimum cardinality, maximum cardinality, exact cardinality, functionality, in-
verse functionality, reflexivity, irreflexivity, asymmetry, role disjointness.
• Mereology: weak supplementation (pp(x, y) → ∃z(p(z, y) ∧ ¬o(z, x))) in MM vs.
strong supplementation (¬p(y, x) → ∃z(p(z, y) ∧ ¬o(z, x))) in EM.
R. Grütter and C.M. Keet /

• Temporal logics: dense time (∀t,t 0 ∈ T,t < t 0 , ∃t 00 .t < t 00 < t 0 ) vs. discrete time
(there is a first and last time point t, and no time point between t and t + 1).

3.3. Conflicts Manifesting Themselves in a Language Profile Violation

See nos. 5–6 in Table 1. Here, the case where conflict resolution aims at preserving
the language to the extent that it is decidable again has to be distinguished from the
case where the original language profile should be preserved. Presupposing the princi-
pal choices above, conflict resolution in the first case aims at capturing as much of the
semantics of the domain theory as possible. Since the violated language profile is not
the most expressive one, there may be room for a (decidable) joint outside option. To
give an example, the axiom O(x, y) =de f ∃z(P(z, x) ∧ P(z, y)) (i.e., ‘overlaps’) cannot be
expressed in any decidable OWL species. While preserving decidability, the author may
still want to state that P− (x, z) ∧ P(z, y) is a sufficient condition for O(x, y). Or they may
want to state that O(x, y) is a reflexive and symmetric property. Doing so may violate the
original language profile. Whether the ontology language still is undecidable with the
modified axioms and conditions can be figured out in the same way as described in Sec-
tion 3.2. Weakening the theory step by step this way will end up in a decidable language,
since the representation of properties as mere primitives is always possible in OWL and
other ontology languages.
In the second case, conflict resolution aims at preserving the original language pro-
file at the expense of relaxing principal choice (ii), i.e., accepting that ‘as much seman-
tics as possible’ is less than anticipated. This applies to conflicts emerging from what
is called design for scalability in Section 2.1. Here, conflict resolution is likely to be a
zero-sum game similar to that described in Section 3.2 (undecidability). The OWL 2 QL
profile, for instance, is aimed at applications that use very large volumes of instance data,
such as conventional relational database systems, and where query answering is the most
important reasoning task. Violating this profile means accepting that query answering
may no longer be implementable by rewriting queries into a standard relational query
language [21].
In the first case, tool support is the same as that described in Section 3.2 (undecid-
ability). In the second case, the OWL Species Classifier supports authors of OWL on-
tologies by listing which axioms violate which OWL species (see footnote 2). The OWL
Species Classifier was also used to search through the 417 axioms of the CIDO ontology
for COVID-19 [12] to check for profile violations, as illustrated next.
Example 3 Let us assume that medical ontologies for information systems should not
exceed the OWL 2 EL profile, considering scalability and compatibility with typical OBO
Foundry ontologies and SNOMED CT. CIDO [12] is not in OWL 2 EL, however, since it
has a class expression with a universal quantifier on the right-hand side; more specifi-
cally, ‘Yale New Haven Hospital SARS-CoV-2 assay’ v ∀’FDA EUA-authorized orga-
nization’ is one of the multiple axioms that violate the OWL 2 EL expressiveness restric-
tions in the cido.owl of 14 June 2020, and is also present in the cido-base.owl of 18
June 2020.

3.4. Conflicts Manifesting Themselves in an Incoherent Ontology

Conflict resolution, in this case, aims at preserving a coherent ontology. Examples in-
clude ontological misspecifications at the axiom-level, such as disjoint ancestors, result-
R. Grütter and C.M. Keet /

ing in unsatisfiable classes (no. 5 in Table 1). Such conflicts manifest only when making
deductions by a reasoner. In the simplest case, they are resolved by keeping some of the
conflicting axioms and removing others in a way similar to that described in Section 3.2
(undecidability). In the example, either the disjointness axiom on the ancestors or the
subclass axioms on the class may be kept, but not both.
State-of-the-art ODEs allow for making deductions. After running the OWL rea-
sonser in Protégé 5.2, for instance, unsatisfiable classes and properties are displayed in
red color. In order to find out what made them unsatisfiable, justifications can be com-
puted using the respective plug-in. A justification is a set of axioms from an ontology that
is sufficient for an entailment to hold [10]. In the case of unsatisfiable classes and proper-
ties, justifications can be computed specifically for entailments with owl:Nothing and
owl:bottomObjectProperty on the right-hand side. In this way, the sources of inco-
herence can be identified.

3.5. Conflicting Modeling Styles

These conflicts arise from source 3 in Section 2.1. Resolving them aims at restructuring
(parts of) an ontology such that correspondences with entities of a different ontology can
be established. For instance, if the same notion is modeled in one ontology as a class and
in another as an object property (see Figure 1), or even in both ways in the same ontol-
ogy, and the ontology language does not permit heterogenous alignments, then either the
object property has to be reified or the class has to be recast as an object property (see no.
7 in Table 1). Typical examples are object properties such as o1:married-to and o1:has-
member and corresponding reifications as o2:Marriage and o2:Member, respectively. A
concrete difference is illustrated in Example 4.

Example 4 Consider again the CIDO ontology and now also the CODO ontology3 for
COVID-19: codo:‘laboratory test finding’ ≡ {positive, pending, negative}, i.e., the
outcomes are instances, whereas in CIDO, there is a cido:‘COVID-19 diagnosis’ class
with three subclasses [negative/positive/presumptive positive] COVID-19 diagnosis. This
is an example of class vs. instance modeling of the same idea.

What is recorded in the conflict set depends on the case at hand; for the class vs.
property example, these would be the respective axioms to match and the axioms they
are used in, which may be found by using an NLP-based algorithm with POS tagging
and stemming. Generally speaking, there are two different options of dealing with con-
flicting modeling styles. The first is to convert one modeling pattern into the other, and
the second option is to match patterns by a set of axioms, rather than by a single bridging
axiom, which is referred to as a heterogeneous TBox mapping [15].
The way how state-of-the-art ODEs deal with conflicting styles depends on
the kind of conflict and the ODE. For instance, Protégé 5.2 restrict alignments to
owl:equivalentClass statements. Simply put, it does not provide the necessary means
to bridge a class and an object property. A joint outside option may be the DOL frame-
work [4].

3 https://bioportal.bioontology.org/ontologies/CODO
R. Grütter and C.M. Keet /

4. Case Study

The approach proposed here is tested against a case of epizootic disease outbreak in the
Lemanic Arc (France, Switzerland) in 2006 [22]. To this end, case records of three oc-
currences of human-pathogenic avian influenza (H5N1) in wild birds were examined.
The measures taken by the Swiss authorities to prevent the virus from infecting, in a first
instance, domestic poultry consisted of establishing protection zones within a radius of
at least 3 kilometers and surveillance zones within a radius of at least 10 kilometers. In
these zones, regulations, such as ‘poultry must be kept in the henhouse’, were introduced.
The Swiss authorities had to decide which municipalities to include in the protection
zones and which in the surveillance zones (see Figure 2).

Figure 2. Avian influenza in the Lemanic Arc (adapted from [23]). National Map 1:200,000 c 2008 swisstopo

Assume the administrative division of Switzerland is represented in administra-
tive ontology O1 and the finds of infected birds as well as protection and surveillance
zones are represented in epidemiology ontology O2 (the ontologies can be downloaded
from https://www.envidat.ch/dataset/icbo2020). In order to construct a query
against a geodatabase to figure out which municipalities to include in which zones, the
two ontologies need to be merged. Both are OWL 2 DL ontologies with an expressiv-
ity of ALCRIF and SRIF, respectively. They have been implemented using Protégé
5.2 [19].
In order to represent the administrative division properly, every region occupied by
a municipality is assigned to exactly one region occupied by a district. Accordingly, the
object property partOf is functional in ontology O1 . For the finds of infected birds in on-
tology O2 , on the other hand, the same object property needs to be transitive: The (small)
regions occupied by the finds are contained in the regions occupied by the protection
zones. These are contained in the regions occupied by the surveillance zones. Merging
the two ontologies, thus, results in a conflict which is reported by the following conflict
set:
R. Grütter and C.M. Keet /

Ontology: O1 Ontology: O2
IRI: appl:administrative IRI: appl:epidemiology
No.: 1.17 No.: 2.32
Axiom: has 2D u has 2D inv u Axiom: Tr(partOf)
located in u partOf v ⊥ Description: transitivity of roles
Description: disjointness of roles Theory: M
Theory: n/a DL: S, R
DL: (¬), R Inference: (O1 + O2 − 1.17 − 1.22) |= O20

No.: 1.22 Diff: (O10 - O20 ) = 0/
Axiom: > v (≤ 1 partOf)
Description: functionality
Theory: n/a
DL: F, Q
Inference: (O1 + O2 − 2.32) |= O10

The conflict is resolved by trading transitivity of appl:epidemiology#partOf for
functionality of appl:administrative#partOf and disjointness of roles in the admin-
istrative ontology. Doing so affects less, but equally preferred, axioms than the other way
round, namely, one axiom vs. seven axioms (please note: ‘axiom’ 1.17 is a shorthand
notation for six individual axioms omitted due to space limitations). It loses exactly the
same inferences (diff is empty) as trading in the opposite direction.

5. Conclusion

First steps towards a framework dealing with the various types of modeling conflicts
through meaning negotiation and conflict resolution in a systematic way have been pro-
posed. We introduced and specified the notions of meaning negotiation and conflict res-
olution, made clear what their components are, and took a first step towards conceiving a
library of conflicts. The notion of conflict set was introduced as a minimal data structure
in which the detected conflicts can be stored and upon which a software-mediated con-
flict resolution can operate. This approach was evaluated with an actual case of domain
knowledge usage in the context of epizootic disease outbreak.
As has been shown, there is no single way of detecting and resolving conflicts. A
number of common cases were described by distinguishing (1) ontology from language,
(2) theory-level from axiom-level, and (3) upfront negotiation from resolution upon man-
ifestation. While there are some tools and plugins that can assist with meaning negoti-
ation and conflict resolution, no integrated support is currently provided. Future work
includes refining the framework such as to specify software design requirements for au-
tomating conflict detection, based on which algorithms for conflict resolution can be de-
vloped or fine-tuned, as well as establishing the conflict library in more concrete terms.

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