=Paper=
{{Paper
|id=Vol-3211/CR_096
|storemode=property
|title=A Diversity-Aware Domain Development Methodology
|pdfUrl=https://ceur-ws.org/Vol-3211/CR_096.pdf
|volume=Vol-3211
|authors=Mayukh Bagchi,Pooja Bassin,Syed Juned Ali
|dblpUrl=https://dblp.org/rec/conf/er/Bagchi22
}}
==A Diversity-Aware Domain Development Methodology==
https://ceur-ws.org/Vol-3211/CR_096.pdf
A Diversity-Aware Domain Development
Methodology
Mayukh Bagchi1
1
Department of Information Engineering and Computer Science (DISI), University of Trento, Via Sommarive , 9 I-38123
Povo (TN), Italy.
Abstract
The development of domain ontological models, though being a mature research arena backed by well-
established methodologies, still suffer from two key shortcomings. Firstly, the issues concerning the
semantic persistency of ontology concepts and their flexible reuse in domain development employing
existing approaches. Secondly, due to the difficulty in understanding and reusing top-level concepts in
existing foundational ontologies, the obfuscation regarding the semantic nature of domain representations.
The paper grounds the aforementioned shortcomings in representation diversity and proposes a three-fold
solution - (i) a pipeline for rendering concepts reuse-ready, (ii) a first characterization of a minimalistic
foundational knowledge model, named foundational teleology, semantically explicating foundational
distinctions enforcing the static as well as dynamic nature of domain representations, and (iii) a flexible,
reuse-native methodology for diversity-aware domain development exploiting solutions (i) and (ii). The
preliminary work reported validates the potentiality of the solution components.
Keywords
Representation Diversity, Ontology Concept Reuse, Teleology, Domain Development Methodology.
1. Introduction
The emphasis on shared in the definition of a formal ontology implies that it is (ideally) to be
reused by the community of practice which shares, albeit partially, a similar conceptualization. It
has, however, been corroborated in several established studies (e.g., [1]) that reusing ontologies
“does not seem to be widespread" and even if reused, it is “not a consolidated practice" [1] and is
decided on an ad-hoc basis without any shared best practice. The present research project is
positioned in this context of domain ontological model development grounded in reuse, wherein
domain is taken to be defined as “an ontological base that reveals an underlying teleology" [2].
Let us now concentrate, at a high level, on three specific research challenges afflicting the
aforementioned research context. Firstly, that of the status quo of concept reuse from general
purpose ontologies. Clearly, as shown in [1], existing ontology concept reuse approaches suffer
from mainly, but not only, the semantic heterogeneity arising out of the mismatch between the
intended and the available conceptualization(s), even within the same domain. Secondly, the
reality that foundational ontologies, despite having “a great potential for reuse", are practically
considered “hard to understand and, consequently, difficult to (re)use" [3], resulting in obfuscation
ER’2022 Forum and Symposium, October 17-20, 2022, Online.
$ mayukh.bagchi@unitn.it (M. Bagchi)
0000-0002-2946-5018 (M. Bagchi)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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of top-level distinctions especially while modelling (static and) dynamic domains (i.e., domains
modelling functions as well as actions). Thirdly, the fact that there is no single methodology
which, while modelling both static and dynamic domains, is founded in ontology concept reuse.
The proposal puts forth a representation diversity-aware approach [4] towards tackling
the above research challenges, and thus, grounds itself in the assertion that representation
diversity should be considered as a, quoting [5], “feature which must be maintained and exploited"
and not as a “defect that must be absorbed". Accordingly, the paper proposes a novel three-
fold solution proposal as an ordered response to the three research challenges. The first
solution component is to design and implement a pipeline rendering concepts from reusable
general purpose ontologies reuse-ready. The second solution component entails the design of a
minimalistic foundational knowledge model enforcing meaning to concepts in both static and
dynamic domain representations. Thirdly, incorporating the above two solution components in
a reuse-native, diversity-aware methodology for developing domain ontological representations.
The rest of the paper is organized as follows. Section 2 and 3 elaborates on the state-of-the-art
and research objectives. Section 4 discuses the solution proposal and validation strategy. Section
5 concludes the paper by enunciating the preliminary work done.
2. State Of The Art
As from the introduction, the three research challenges directly map to state-of-the-art in:
ontology concept reuse, foundational ontologies and ontology development methodologies.
The three types of ontology reuse - direct, indirect and hybrid - were exemplified in works
such as, e.g., [1]. The study in [6] examines in detail the feasibility of ontology reuse and posits
requirements for designing ontology reuse methodologies. The work on content ontology design
patterns [7] is also founded in reuse. Grounded in the above work on ontology reuse is the
specific aspect of ontology concept reuse, majorly observed with respect to highly domain-specific
ontologies such as in biomedicine, e.g., [8] and agriculture, e.g., [9]. The more recent empirical
work on BioPortal ontologies, e.g., [10] conclusively observes that both / reuse and reusability
is significantly low, barring few exceptions. The observations are similar for ontologies in
AgroPortal with the recent study [9] observing “most ontologies overlap with, reuse, or map less
than 5% (...) to other ontologies". Even concepts from general purpose ontologies in repositories
like LOV [11] are hardly reused with the exception of, however, the W3C endorsed ontologies.
Next, let us focus on the landscape of foundational ontologies (aka upper or top-level ontolo-
gies). The review in [12] analyze seven upper ontologies according to a select set of software
engineering criteria, amongst which we brief below few of the most used ones. DOLCE [13]
is a comprehensive upper “ontology of particulars" grounded in the fundamental distinction
between endurants and perdurants. It, however doesn’t model functions or roles in its core
taxonomy [14]. BFO [15] is a bi-ontological theory with two components - a Snap ontology
of endurants and a Span ontology of perdurants, and is, almost solely, employed within the
biomedical community. SUMO [16] acts a foundation for domain ontologies and contains about
thousand terms grounded in the distinction between physical and abstract entities. UFO [17] is
composed of three distinct ontology fragments - endurants, perdurants and social entities.
Finally, let us concentrate briefly on ontology development methodologies. The survey in
[18] provides an analysis of several early generation methodologies. METHONTOLOGY [19]
proposed a “life cycle to build ontologies based in evolving prototypes". Ontology Development 101
[20], instead, offered the flexibility of choosing top-down, bottom-up or middle-out approaches
in engineering ontologies. More recently, the NeOn methodology [21] offers a set of very generic
scenarios for reuse, re-engineering and merging of ontological resources. The eXtreme Design
(XD) methodology [22], on the other hand, is very specific, in the sense that it is grounded on
reusage of content ontology design patterns for modelling new ontologies.
3. Problem Statement
The current work is based on the stratified nature of representation diversity [4]1 . The starting
point is the issue of semantic heterogeneity in domain representations which, as from the
introduction, should be accommodated as a feature. To facilitate such an accommodation, the
novel approach in [4] restates semantic heterogeneity as a problem of representation diversity
stratified into four characteristically autonomous yet functionally linked representation layers:
• Concept [Diversity] (L1), arising out of the many-to-many mapping between real world
objects and their perceived representation as concepts [4, 23]
• Language [Diversity] (L2)2 , arising out of the many-to-many mapping between concepts
and the words employed for their linguistic rendering due to linguistic phenomena such
as polysemy and synonymy [24]
• Knowledge [Diversity] (L4), arising out of the many-to-many mapping between entity
types (etypes from now on) and the properties employed to model them [4, 25], and
• Data [Diversity] (L5)3 ., arising out of the many-to-many mapping between entities and
the property values employed to describe them [4].
Grounded in the above foundation, the research question stands: “How can concepts from
reusable general purpose ontologies in different languages be methodologically reused to model
a reusable and shareable domain (knowledge) representation for integrating data exhibiting
different genres of semantic heterogeneity?" It can be instantiated into the following independent
but related research objectives:
(O1) To design and implement a pipeline rendering concepts from reusable ‘general purpose’
ontologies reuse-ready (accommodating L1, L2 (aka L1,2) diversity)
(O2) To design a foundational knowledge model explicating foundational distinctions which ac-
commodate static and dynamic domain representations
(O3) To design and implement a diversity-aware methodology for modelling domain ontological
representations exploiting (O1) and (O2) (accommodating L4 diversity)
1
See [4]. Details omitted due to lack of space.
2
L3 out of scope for this work.
3
Detailed deliberation on L5 out of scope for the current proposal.
4. Solution Approach
The solution proposal introduces the three solution components, namely subsections: (4.1),
(4.2) and (4.3), corresponding to the three research objectives - (O1), (O2) and (O3) respectively.
Further, the validation strategy of the overall solution framework is also discussed.
4.1. Making Concepts Reuse-Ready (O1)
At the outset, let us explain the following terms which are crucial for understanding the curated
pipeline for making ontology concepts reuse-ready:
• Synset - Synsets [26] are sets of synonyms used to represent word senses
• Word Sense Rank (WSR) - The rank of a synonym in a synset, with the preferred term
having WSR 1.
UKC - UKC stands for Universal Knowledge Core [27, 24]. It is a multilingual lexical-semantic
resource composed of two components - Concept Core (CC) and Language Core (LC). The CC
is a semantic network where the nodes are alinguistic concepts structured employing semantic
relations (such as hypernym-hyponym), with each concept uniquely identified only by a Global
IDentifier (GID). In essence, the semantic network is a Directed Acyclic Graph (DAG) codifying
the space of possibilities of all existing and forthcoming concepts, and can be considered as
background knowledge. The LC, on the other hand, is a union of language-specific modules,
with each module being the set of “words, senses, synsets, glosses and examples" [27] in a specific
language existent or upcoming in the UKC. Semantically equivalent synsets across languages
are univocally interconnected and represented by a single GID.
Our proposed pipeline for rendering ontology concepts reuse-ready is sequentially enumerated
as follows (corresponding to each step in figure (1) -
Figure 1: Pipeline for Making Concepts Reuse-Ready
1. General Purpose Ontology Catalogue - We restrict ourselves to high quality reusable general
purpose ontologies from LOV [11].
2. Informal Ontology Selection - This step is to select an ontology from the the LOV. The
strategy is to select reusable ontologies, for a particular domain, based on the number of
incoming links for a certain ontology in LOV, which, being a popularity metric, provide
a measure of how many other ontologies have reused it (either partially or fully). The
selected ontology is termed as informal as the concepts it expresses are yet to be rendered
alinguistic.
3. L4 - L1,2 Annotation - The central step of the pipeline performed by a domain expert,
wherein each concept from the selected informal ontology is either annotated with its
GID if it is already present in the UKC CC, or a new concept with a new GID is created
in the CC if the concept is new with respect to the CC. Concretely, the sub-steps are as
follows -
a) Each concept from the informal ontology’s class hierarchy, object property hierarchy
and data property hierarchy is considered (sequentially; one hierarchy at a time)
in a top-down order, and its sense is understood from the gloss provided in the
annotation properties (mostly captured in rdfs:comment and/or rdfs:isDefinedBy).
b) The concept is semantically searched in the UKC CC via the LC interface matching
with the natural language in which the concept label is expressed. The search results
in one of the following two scenarios:-
i. (S1): Synonymous Match between the ontology concept and an existing concept
found in the UKC CC.
ii. (S2): No Synonymous Match between the ontology concept and any potential
concept in the UKC CC.
c) Concurrently with step 3(a) and 3(b), a UKC-compatible spreadsheet capturing
requisite information is generated. The principle information to be recorded in the
spreadsheet are as follows:-
i. (S1): In case of a Synonymous Match, the GID and the Word Sense Rank of the
concept have to be recorded in the spreadsheet, alongside its parent concept
(and its GID).
ii. (S2): In case of No Synonymous Match, a negative integer is recorded in place of
the concept’s GID. For all such concepts, the recorded negative integer should
follow a decremental (negative integer) sequence starting from ‘-1’. In addition,
the parent concept (and its GID) is also recorded. Further, for this scenario in
specific, the gloss of the concept should also be recorded in the spreadsheet
(respecting Genus-Differentia paradigm [28]).
4. UKC Domain Enrichment - The spreadsheet is imported into the UKC CC via the spread-
sheet importer API, wherein post import, two highlights are crucial - (i) all the new
concepts (annotated with negative integers) now have their own, unique GIDs, and
(ii) one more concept hierarchy gets formalized within the DAG of the CC, that of the
imported ontology. Thus, the concepts are finally reuse-ready.
4.2. Foundational Teleology (FT) (O2)
The solution that the proposal puts forth for (O2) is a foundational teleology where teleology
is an ontology that, quoting [29], “focus on function and on how a chosen representation fits a
certain purpose, this being the basis for a general model for the diversity of knowledge". An initial
characterization of the foundational teleology (FT) can be found in figure (2) above, where the
ovals stand for top-level concepts, the lines for subsumption relation and the dashed lines for
relations. The teleology is grounded in the theory of teleosemantics [29] and models ends and
Figure 2: Foundational Teleology (FT)
goals (via functions and actions). Further, we speak of a foundational teleology in the sense of a
teleology explicating the foundational distinctions and foundational relations which are central
for semantically grounding the static and dynamic nature of a ‘domain’ ontological model.
Let us now focus on the composition of the foundational teleology in terms of its foundational
distinctions and foundational relations. The root of the FT is Anything which we define as
the substratum [30] for modelling purpose-driven domain representations. It is a bearer of
properties but has its identity independent from them, thereby exhibiting permanence and
transcending space-time. Anything is specialized into Object, Function and Action - the core
foundational distinctions which model the function-focused and purpose-driven nature of
domains, according to the theory of teleosemantics [29]. Objects are defined as representations of
what is perceived (such as a person). Functions model the expected behavior of objects in terms of
it performing a certain set of actions, wherein Actions represent how objects change in time (i.e.,
functions and actions model the dynamics of a domain). For example, a person can have several
functions such as father, professor etc., each of which can be modelled as a set of (admissible)
actions (such as teach, evaluate etc. for professors). Further, functions, from teleosemantics,
are specialized into - Producer and Consumer, wherein producer is an object performing an
action affecting another object, where the ‘another’ object is the consumer. Thing, instead, is
the domain reference context. There are four foundational relations interconnecting the above
distinctions. The ObjectToObjectRelation captures the intra-relationship between objects. The
relation between object and function, function and action, and object and action are modelled
by ObjectFunction, FunctionAction, and ObjectAction repsectively.
4.3. The Diversity-Aware Domain Development Methodology (O3)
Finally, the domain development methodology (figure 3) is elucidated. It is made possible due
to the integration of the two previous solution components, namely (4.1) and (4.2), in the
framework of a single general methodology. Let us examine each step of the methodology
corresponding to the steps in figure 3.
(1) Competency Questions (CQs) - CQs [31] are input with reference to the methodology.
They are generated by a comprehensive process (detailed in [32]) elaborating the users of the
Figure 3: The Diversity-Aware Domain Development Methodology Methodology
domain model the potential questions they want to ask from the domain model in certain
scenarios.
(2) Concept Extraction - Concept extraction refers to the elicitation of concepts from CQs
(from step (1)) and as such is a semantics-intensive task. The study in [33] confirms that concept
elicitation from CQs, till date, is a heuristics-driven task and has no dedicated mechanism.
We propose below a (domain expert performed) mechanism, grounded in faceted knowledge
organization [34, 35], for eliciting concepts from CQs -
1. Raw CQ - CQ in natural language as obtained from step (1).
2. Kernel CQ - Raw CQ minus all the auxillary/apparatus words (such as stop words)
resulting in each term denoting a concept. Further, this step also elicits the latent concepts
[34], concepts which are explicitly hidden but are implied or suggestive.
3. Analyzed CQ - Each concept in Kernel CQ classified as common, core or contextual,
where: (i) common refers to concepts from space and time [34]; (ii) core refers to concepts
fundamental to the domain to be modelled (for e.g., tourist facilities), and (iii) contextual
refers to concepts which are highly localized with respect to the reference context (for e.g.,
malga as a tourist facility in Italy)
4. Classified CQ - Each concept in Analyzed CQ further classified as object, action or
function
5. Attributed CQ - Each concept in Classified CQ enriched with requisite object properties
and data properties.
(3) ER Modelling - Once concepts are extracted from CQs, we concentrate on modelling the
entity-relationship (ER) model following the two phases below -
Making Concepts Reuse-Ready - Here we exploit the pipeline for rendering concepts reuse-
ready as proposed in (4.1). From the LOV, we perform informal ontology selection resulting
in ontologies whose concepts can be partially reused to model the ER (for e.g., ontologies on
tourism and facilities for the domain ‘tourist facilities’). Next, we perform L4 - L1,2 Annotation
with respect to the selected informal ontologies and enrich the UKC, thus rendering the concepts
formal (with unique GIDs) and reuse-ready.
ER Development - Firstly, we specify the reference context - Thing - for which we want
to develop the ER model (e.g., tourist facilities in Trentino, Italy from 01.01.2020-01.01.2021).
We then instantiate the object hierarchy, the function hierarchy and the action hierarchy,
respectively, with respect to Thing. The next step is to Interrelate objects, functions and actions,
and subsequently with requisite object and data properties.
(4) ETG Formalization - In this step, we render the ER model as completely formal, by which
we mean all concepts structuring it can be uniquely expressed using a GID. For achieving this,
we implement the pipeline for rendering concepts reuse-ready for the second time, but, with
two key differences - (i) this time, we consider the ER model we developed and hence don’t
perform an informal ontology selection from any catalog, and (ii) the objective being different,
of rendering informal concepts as formal with GIDs. We call this formal ontological model as
Entity Type Graph (ETG) [4].
(5) FT Grounding - Once the ER model has been fully formalized into an ETG, the final step
is to ground the ETG to the foundational teleology (exploiting solution (4.2)). We propose to
do this by aligning the object, function and action hierarchy to their respective foundational
distinctions in the FT, and as a result, also grounding the relationships in the ETG into their
semantically appropriate foundational relations.
(6) Domain Model - Finally, we have the domain ontological model, which is the principal
output of the methodology. The proposal is to host the domain models developed in Liveschema4
[36], which is our own meta-catalog of ‘general purpose’ ontologies.
Validation - The validation strategy for the solution components are briefly as follows: (i) For
component (4.1), quantitative evaluation following metrics adapted from [1]; (ii) For component
(4.2), qualitative evaluation following tried and tested principles of concept hierarchies as
enunciated in [34], and (iii) For component (4.3), testing, implementation and potential tuning
of the methodology in KDI/KGE master degree course projects56 and EU projects.
5. Preliminary Results
5.1. Work Done on (4.1)
In collaboration with international domain annotation experts, L4 - L1,2 Annotation was carried
out on thirty most popular ‘general purpose’ ontologies, selected from the LOV catalog across
categories like Geography, Time, Academia etc. Additionally, concepts from ten such annotated
ontologies files were translated into the Hindi language.
5.2. Work Done on (4.2)
The emphasis of the preliminary work done regarding the solution component (4.2) was on
designing a first characterization of the foundational teleology starting from the foundations of
the theory of teleosemantics [29].
4
http://liveschema.eu/
5
http://knowdive.disi.unitn.it/teaching/kdi/
6
https://unitn-knowledge-graph-engineering.github.io/KGE2022-website/
5.3. Work Done on (4.3)
Firstly, the methodology was designed and the series of papers in [37, 38, 39, 40] developed the
stratified knowledge representation theory and the cognitive grounding behind the proposed
methodology. The second aspect, which is a consequence of the proposed stratified knowledge
representation theory, is the definition of a very compelling set of qualitative norms (see [37])
which allow for the construction of high quality domain models.
Acknowledgements
I am grateful to my advisor Prof. Fausto Giunchiglia for supervising my research and for
countless inspirational discussions. This PhD is funded by DELPhi project - MIUR (PRIN) 2017.
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