=Paper=
{{Paper
|id=Vol-3365/short1
|storemode=property
|title=Disentangling Domain Ontologies
|pdfUrl=https://ceur-ws.org/Vol-3365/short1.pdf
|volume=Vol-3365
|authors=Mayukh Bagchi,Subhashis Das
|dblpUrl=https://dblp.org/rec/conf/ircdl/BagchiD23
}}
==Disentangling Domain Ontologies==
https://ceur-ws.org/Vol-3365/short1.pdf
Disentangling Domain Ontologies
Mayukh Bagchi1,2,* , Subhashis Das3
1
DISI, University of Trento, Via Sommarive 9, 38123 Povo, Trento (TN), Italy.
2
Institute for Globally Distributed Open Research and Education (IGDORE).
3
CeIC, ADAPT, School of Computing, Dublin City University (DCU), Dublin 9, Ireland.
Abstract
In this paper, we introduce and illustrate the novel phenomenon of Conceptual Entanglement which
emerges due to the representational manifoldness immanent while incrementally modelling domain
ontologies step-by-step across the following five levels: perception, labelling, semantic alignment,
hierarchical modelling and intensional definition. In turn, we propose Conceptual Disentanglement, a
multi-level conceptual modelling strategy which enforces and explicates, via guiding principles, semantic
bijections with respect to each level of conceptual entanglement (across all the above five levels) paving
the way for engineering conceptually disentangled domain ontologies. We also briefly argue why state-
of-the-art ontology development methodologies and approaches are insufficient with respect to our
characterization.
Keywords
Conceptual Entanglement, Conceptual Disentanglement, Ontological Analysis, Domain Ontologies
1. Introduction
Consider the motivating example of the Burj Khalifa1 , the tallest building in the entire world. It
is variously modelled as, for instance, a skyscraper in a cadastral database, a hotel in a tourist
database, a corporate complex in an event management database and a vertical obstruction in an
air traffic database. Such heterogeneity, whether in the aforementioned example of Burj Khalifa,
or, in general, for any real-world entity, are ubiquitous and are considered as instantiations of
the phenomenon of semantic heterogeneity [1]. The central tenet behind such heterogeneity in
conceptual modelling remains the fact that representations are fundamentally cognitive constructs
[2, 3] and are, non-trivially, grounded in the very way in which (human) conceptualizations are
causally generated from (human) experientiality [4, 5, 6]. Amongst the many socio-technical
ramifications of such heterogeneity of semantic representations, we are particularly interested
in the problem of harmonizing such diverse conceptual representations into a unified ontological
schema which can be exploited later, for instance, to classify and integrate open heterogeneous
data [7], e.g., about the Burj Khalifa.
19th IRCDL (The Conference on Information and Research science Connecting to Digital and Library science), February
23–24, 2023, Bari, Italy
*
Corresponding author.
$ mayukh.bagchi@igdore.org (M. Bagchi); subhashis.das@dcu.ie (S. Das)
0000-0002-2946-5018 (M. Bagchi); 0000-0001-9663-9009 (S. Das)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
CEUR Workshop Proceedings (CEUR-WS.org)
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1
https://www.burjkhalifa.ae/en/
We model the aforementioned instantiation of the heterogeneity of conceptual representa-
tions to be essentially that of the phenomenon of Conceptual Entanglement, viz., an ordered
manifoldness (‘entanglement’) from the (perceptual) generation of concepts to their logical
formalization, immanent across (every) conceptualizations. To that end, we outline five char-
acteristically autonomous yet functionally linked levels into which Conceptual Entanglement
distributes. The first level of entanglement is generated due to the different ways in which
different real-world entities can be perceived. The second level of entanglement arises due
to the different ways in which different concepts perceived can be named. The third level of
entanglement pertains to the different top-level ontological distinctions [8, 9] into which each
of such named concepts can be semantically aligned and constrained to. The fourth level of
entanglement, given the top-level semantic alignment, instantiates as the different ways in
which different lexicalized concepts can be hierarchically modelled. The fifth and the final level
of entanglement occurs due to the different ways in which different concepts in the lexical
hierarchy can be intensionally characterized.
Our solution approach has the fundamental assumption that there is “only one ‘real world’
but many different descriptions of this world depending on the aims, methodology and terminology
of the observer" [10]. We commit to the thesis that such semantically heterogeneous conceptual
representations across levels should constitute the foundation of ontology-driven conceptual
models [11]. Based on the aforementioned premise, we propose Conceptual Disentanglement as
a multi-level conceptual modelling strategy grounded in guiding normative principles, following
which the conceptual entanglements in each level (and subsequently, in its entirety) can be
disentangled to semantic bijections towards developing disentangled domain ontologies (such
as in healthcare [12, 13] etc.).
The chief novelty of the proposed solution are two-fold. Firstly, the explicit characterization
of the ordered layering of conceptual entanglement which, though immanent, remains implicit
in mainstream semantic heterogeneity, e.g., [14, 15] and ontology-driven conceptual modelling
(ODCM) literature, e.g., [16, 17]. Secondly, as a consequence of the streamlined accommodation
of the layered heterogeneity of conceptualization in the form of conceptual disentanglement, the
key alignment amongst its mental model (which is language agnostic), domain ontological model
(expressed, for instance, in an ontology language like OWL2 ) and its logical axiomatization
(expressed in any flavour of first order logic, e.g., Description Logics [18]) is achieved.
The remainder of the paper is organized as follows: Section (2) describes in detail the notion
of conceptual entanglement instantiated with respect to each of the aforementioned five levels.
Section (3) elucidates the guiding norms based conceptual modelling strategy which can facilitate
conceptual disentanglement across the five levels. Section (4) concludes the paper by briefly
skimming through the research implications of conceptual (dis)entanglement.
2. Conceptual Entanglement
It is unanimously agreed that a conceptual representation is “an abstract, simplified view of the
world that we wish to represent for some purpose" [19] and is fundamentally mental in nature [20].
2
https://www.w3.org/TR/owl2-overview/
We adhere to an extended, spectrum-like notion3 of conceptual representation which models
concepts via a stratification across five levels viz.:
1. Perception: dealing with the mental internalization of what is the case in the target reality;
2. Labelling: dealing with how the perceived concepts should be named;
3. Semantic Alignment: dealing with how the named concepts should be aligned and con-
strained to top-level ontological distinctions;
4. Hierarchical Modelling: dealing with how the semantically aligned concepts should be
modelled in a taxonomical hierarchy;
5. Intensional Characterization: dealing with how each concept in the hierarchy should be
defined via its (object and data) properties.
Founded in the aforementioned stratification, we construe Conceptual Entanglement to be the
manifoldness between the source reality and the target conceptual representation that is innate
and unavoidable with respect to the aforementioned levels (individually for each level and
cumulatively across levels). We now exemplify and elucidate the (conceptual) entanglement
occurrent in each of the levels.
Perception (Entanglement): Concepts are “building blocks of thoughts" [22] aggregated and
abstracted via perceiving4 what is the case in a target reality. Notice that the mental construct of
(each of) the real-world referrent as well as (each of) its possible properties are concepts in our
view. We further state two universally agreed observations about concepts. Firstly, the fact that
they are fundamentally mental representations and are intensional by nature, or, in other words,
quoting [23], “they refer to, or are about something". On top of that, secondly, the fact that the
same referrent or property can be perceived differently by different agents depending on their
purpose (viewpoints), leading to the premise that concepts are cognitive filters [23]. We posit
that the entanglement at the perception level occurs precisely due to the two aforementioned
observations.
To take an example, the real-world geospatial entity Burj Khalifa is perceived as a skyscraper
by a real-estate agent because his or her purpose might be to rent out an office suite in the
building and consequently, he or she concentrates only on cadastral-relevant properties. The
same entity, however, is perceived, via a (partially overlapping) different set of properties, as a
vertical obstruction by an aviation administration agent given his or her purpose of facilitating
a safe air route for all aviation users. Thus, the same entity Burj Khalifa can be mentally
internalized as different concepts based on the purpose5 at hand. Similarly, the same concept of
a skyscraper or a vertical obstruction can be equally internalized, for instance, with respect to
the Shanghai Tower, the second tallest building in the world.
Labelling (Entanglement): Given the internalization of concepts, the second level concen-
trates on naming or labelling them for human and machine interaction purposes. The very act
of labelling, contrary to mainstream interpretation, is non-trivial due to the intimate way in
which thought and language interoperate. We mention two highlights. The first highlight is
the fact that languages are “itemized inventories" of the target reality [25]. Secondly, the fact
3
See [21] for the psycho-cognitive underpinnings of our characterization.
4
Notice that (the nature of) perception, per se, is out of scope with respect to our characterization.
5
The state-of-the-art ontology-driven conceptual modelling approaches employed to codify purpose are not theoreti-
cally well-founded [24] and are at best approximate in codifying different perceptions.
that each of such inventories generate a similar but not the same lexicalization of the (same)
perceived concept given their different cultural grounding [26].
We exemplify how the aforementioned interoperation between thought and language gen-
erates the many-to-many entanglement. Firstly, we show the case within the same language,
for instance, for English. The same concept of Burj Khalifa as a hotel can be referred to as a
hotel or an auberge or a hospice, each of which can further be an equally valid name for the
Shanghai Tower. Secondly, we present the case of multiple languages wherein the entanglement
is two-fold. Firstly, the same concept of Burj Khalifa can be referred to as a hotel in English or
as a Fremdenzimmer in German, each of which can be equally valid for the Shanghai Tower.
Further, even for describing the same hotel Burj Khalifa, English and German hoteliers might
use a partially non-overlapping set of labels, each of which might describe the Shanghai Tower
equally well.
Semantic Alignment (Entanglement): Given the naming of concepts, the third level con-
centrates on semantically aligning the lexicalized concepts to top-level (aka foundational) onto-
logical distinctions (themselves being grounded in distinct philosophical theories of existence).
For instance, whether a specific instance of modelling a concept is semantically constrained to
be an endurant or a perdurant [27], or, an independent or a dependent conceptual entity [27], or,
for instance, a mental object, a process or an event. This is crucial given the well-established fact
that the same (perceived and subsequently named) concept can be modelled in terms of different
foundational distinctions, and equally, a specific foundational distinction might semantically
instantiate into different named concepts in a particular domain.
We now briefly exemplify how the aforementioned semantic alignment raises the possibility
of many-to-many entanglements at this level. For example, let us consider the case of modelling
an independent versus a dependent conceptual entity. Depending on the purpose, the building
Burj Khalifa can be conceptually modelled as a building which is an independent conceptual
entity, when the purpose for modelling is, for instance, to model a generic classification ontology
of a city. The same building, however, can equally be modelled as a dependent conceptual entity
in the form of a corporate complex when it necessarily participates in (the event of) hosting a
corporate management conference or a hotel when it participates in the (the event of) sojourn
of the conference’s guests. Further, the same characterization of an independent or a dependent
conceptual entity can be semantically attributed to other buildings such as the Shanghai Tower,
thereby, generating the manifoldness.
Hierarchical Modelling (Entanglement): Given the conformance of concepts to top-level
semantic distinctions, the fourth level concentrates on organizing the named concepts in a
taxonomical hierarchy. It usually involves four broad phases [4, 6]. Firstly, the fact that with
respect to a specific depth in the taxonomic tree, there are always many different aspects which
can be employed to taxonomically classify concepts of that depth into (many) subordinate
concepts. Secondly, the successive application of such aspects across the entire taxonomy
(with the possibility of many classificatory aspects at each depth) leads to potentially infinite
classifications. Thirdly and fourthly, the many ways in which concepts can be organized
horizontally across a specific taxonomic depth and vertically across a specific taxonomic path,
respectively. We argue that the ordered application of the four aforementioned phases results
in infinitely many hierarchies, thereby, generating an entanglement.
For example, let us consider the case of a building. The aspect of classifying a building
can well be its purpose (generating children like hotel, theatre etc.) or can equally be its color
(generating children like red-colored building, blue-colored building etc.). Further, each of these
aspects can equally be applied to classify entities other than a building. Given the category
hotel, the second classification aspect, amongst infinitely many possible options, can be, for
instance, number of stars. Now, the combinatorial possibilities of the succession of aspects also
definitively determine the many modelling possibilities of sibling concepts at a single level in
the hierarchy (for instance, library, secretariat, lab, sports arena as siblings or academic buildings,
non-academic buildings as siblings). Finally, for the very same reason of the the combinatorial
possibilities of the succession of aspects, there can be infinitely many vertical paths which can
be modelled in a taxonomical hierarchy.
Intensional Definition (Entanglement): Given the hierarchical modelling of concepts, the
fifth and the final level concentrates on modelling the concepts at an intensional level, or, in
other words, defining each individual concept in the taxonomic hierarchy via their appropriate
relations (object properties) and attributes (data properties), thereby rendering the hierarchical
model as a formal ontological schema. The manifoldness at the intensional level is generated
when, each concept, in the different ontological hierarchies modelled out of the same target
reality, can be differently defined via a distinct set of relations and attributes.
For example, the notion of Burj Khalifa as a hotel can be characterized differently via the
following two sets of attributes: {number of rooms, number of VIP suites} or {year of establishment,
latitude, longitude}. Further, each of the above attribute set can also be an equally well-defined
intensional characterization for, say, the Shanghai Tower as a hotel. Considering also, for instance,
the modelling of the relation between two concepts such as a hotel and a conference, there can
be potentially a many-to-many entanglement, e.g., a hotel can be the main venue of a conference
and/or it can be the official entertainment junction for a conference. Each of such relations can
further hold between several pairs of concepts within the same domain of discourse.
There are three important observations concerning the aforementioned stratification of
conceptual entanglement. Firstly, from an ablationary perspective, a specific (domain) ontology
development project team, depending on their purpose, might choose to factor in all or only some
of the aforementioned levels which contribute to the conceptual entanglement. Secondly, the fact
that the entanglement rooted in each of the aforementioned layers of (human) conceptualization
is ultimately instantiated as semantic heterogeneity in the (different) database representations
of the same target reality. This leads to a fractured crosswalk and ultimately the lack of
semantic interoperability [28] amongst them resulting in the lack of a unified classification,
integration and analysis of the target data which is heterogeneous in nature. Last but not the
least, the lack of methodological support to tackle the entanglement across the different layers
of conceptualization also results in an incorrespondence amongst its mental model (which is
language-agnostic), domain ontological model (which, almost always, is expressed in a formal
ontology language) and their underlying logical axiomatization (expressed in a flavour of first
order logic such as description logics [29]).
3. Conceptual Disentanglement
We propose Conceptual Disentanglement as a multi-level conceptual modelling strategy to tackle
the five-fold manifoldness of conceptual entanglement that instantiates in modelling ontologies
for knowledge-based information systems (such as healthcare systems [30]) due to the nature of
(human) conceptual representations as discussed in the previous sections. Conceptual disentan-
glement essentially refers to a set of guiding normative principles which, if considered as best
practice for each of the five levels, can enforce semantic bijections disentangling the conceptual
entanglements while at the same time, accommodating the (extent of the) heterogeneity of
target reality that requires to be modelled. In other words, conceptual disentanglement can
provide the conceptual modelling foundations based on which a methodology for harmonizing
diverse conceptual representations into a dynamic single ontological schema can be developed.
We now elucidate and exemplify conceptual disentanglement strategy specific to each level.
Perception (Disentanglement): Let us first concentrate on the norms which tackle the
manifoldness instantiated due to the very nature of the Perception level. We recommend the
sequential fixation of the following:
• Firstly, the target reality should be precisely delineated with respect to their spatio-
temporal extent. In the case of a distribution of several smaller component realities, the
target reality should be modelled as a disjoint union of the component realities (i.e., of
component spatio-temporal extents). Notice that such a spatio-temporal delineation can be
as general or specific as possible depending upon the ontology development requirements
elucidated, for instance, via Competency Questions (CQs) [31].
• Given the delineation of the target reality, the second activity should determine the
concepts which requires to be modelled within the chosen target reality and the viewpoints
to be considered while modelling them.
The two aforementioned norms facilitate selection of the intended ontological commitment of
the target reality very precisely and thus avoids instances of overcommitment and undercom-
mitment which frequently arise in every domain (see [32] for related exemplifications). This, in
effect, disentangles the conceptual entanglement at the perception layer to a semantic bijection.
For example, let us consider the purpose of modelling an ontology targeted at integration
of cadastral data of Burj Khalifa and Shanghai Tower which are the only components of our
target reality. We fix the spatial extent of our target area as the disjoint union of the latitude and
longitude of Burj Khalifa (25.1972° N, 55.2744° E) and Shanghai Tower (31.2335° N,
121.5056° E) respectively. Further, for instance, we fix the temporal extent from 2022-01-01
00:00:00 to 2022-02-01 00:00:00. Given the spatio-temporal delineation of the target
reality, we fix the viewpoint to be that agreed to by real estate agents. This entails perceiving
both the Burj Khalifa and Shanghai Tower as a skyscraper filtered out via relevant properties.
Labelling (Disentanglement): Given the enforcement of the semantic bijection at the
perception level, we lay down, as follows, the guiding norms for disentangling the conceptual
entanglement occurring in the Labelling level due to language related issues:
• Fixation of the underlying natural languages(s) and the controlled vocabulary [33], the
terminology of which has wide inter-labeller agreement [34] and can be used to uniquely
name the concepts. International terminological standards and conventions for various
domains (e.g., RESO Data Dictionary6 for real-estate domain) can be exploited for this
purpose. Such a choice forces a semantic bijection out of the multiplicity of possible
labellings in the selected language(s) on one hand, and absolves the effect of linguistically-
grounded labelling conflicts such as endonym and exonym [35] on the other hand.
• Optionally, given the fixation of the terminology, the next step, especially key in scenarios
like multilingual and cross-border data integration is to further disambiguate the uniquely
named via associating to each of such concepts a unique alphanumeric identifer such as
the ones provided by general purpose knowledge graphs like Wikidata [36]. Notice that
Wikidata also provide the service of adding a new concept (with, consequently, a new
alphanumeric identifier) if a certain concept is absent in it7 .
Let us exemplify the above norms. Consider, for the sake of simplicity of exemplification, the
underlying natural language is English in case of modelling cadastral data from both the Burj
Khalifa and the Shanghai Tower. Given the fixation of English as well as the earlier fixation of
the viewpoint as a Skyscraper, the next key activity is to uniquely label them (i.e. more non-
generally than that of a ‘skyscraper’). If we commit to RESO as the relevant terminological (data)
standard, there can, in turn, be at least three non-synonymous labels such as Residential Income,
Commercial Lease or Commercial Sale which can be employed to label such a concept. The key
is to select the one which encodes the concept most uniquely in the face of challenging labelling
conflicts such as the phenomenon of endonym-exonym which are key in geospatial labelling
decisions. Additionally, such a label can be alphanumerically disambiguated by associating to it
a unique identifier (such as from Wikidata) which will render it adept for a potential multilingual
label-linkage later on.
Semantic Alignment (Disentanglement): Once the concepts are encoded via a label, the
next key step is to perform an ontological analysis with respect to each of the labelled concepts
(including both referrents and their relevant attributes) from the previous level, this being the
guiding norm to disobfuscate the manifoldness with respect to top-level semantic alignment.
Ontological analysis employs a set of metaproperties to “characterize relevant aspects of the
intended meaning of the properties, classes, and relations that make up an ontology"[37]. To take an
example, in the widely used OntoClean framework [37, 38], the notion of essence and its special
case of rigidity is crucial in ascertaining the ontological stance of geospatial entities. Similarly,
analysis of concepts from the perspective of ontological identity, unity, endurance, perdurance
etc. facilitates, at a later stage, development of ontologically well-founded conceptual models.
The cumulative target of the ontological analysis should be to determine the exact ontological
nature of each labelled concept by semantically constraining them to a specific top-level ontological
distinction grounded in the precise perceptual viewpoint.
For example, the geospatial entity Burj Khalifa should be constrained to be an independent
conceptual entity, when modelled as a building and when the greater purpose for modelling is to
model a classification ontology [7] of a city. This is precisely because a building is ontologically
rigid in the aforementioned sense (i.e., a building is always a building). The same entity, however,
should be strictly modelled as a dependent conceptual entity in the form of a corporate complex
6
See: https://www.reso.org/data-dictionary/
7
See: https://www.wikidata.org/wiki/Help:Items
when it necessarily participates in (the event of) hosting a corporate management conference
because the concept itself is ontologically non-rigid (i.e., a corporate complex is only contingently
so).
Hierarchical Modelling (Disentanglement): In the context of building a disentangled taxo-
nomical hierarchy out of ontologically analysed concepts, we ground our hierarchy modelling
design in the four-step epistemologically well-founded classification theory by Ranganathan
[39, 40]. The first step concerns the selection of a differentiating characteristic for classification
at a single level in the hierarchy. The second step involves the succession of characteristics, or,
in other words, the selection of characteristics for classifying at each successive level in the
hierarchy. The third step involves how sibling concepts should be modelled within a single
level in the hierarchy (termed array in [39]). The fourth and the final step concentrates on the
ontological consistency of (each of) the single path in the ontological hierarchy (termed chain
in [39]). We now elucidate the guiding norms for each step:
• In the first step, we eliminate the manifoldness in the selection of the differentiating
characteristic by exploiting the canons of relevance (stating that such a characteristic
should be relevant to the purpose at hand) and ascertainability (stating that such a
characteristic should be perceptually ascertainable). For example, in the case of building
a classificatory ontology for buildings for government surveys, we fix legal nature of
building as the first classification characteristic.
• The entanglement in the second step of choosing the succession of characteristics is
disentangled by employing the canon of relevant succession which enforces that the
selection of successive differentiating characteristics across the depths of a taxonomy
should be founded solely on purpose. For instance, the second characteristic for the
classificatory ontology on buildings could be year of establishment given the purpose is
to aggregate timeseries data on real estate by a local government body.
• The canon of exhaustiveness is employed to eliminate the entanglement in arrays by en-
suring that all the concepts at a specific depth in the taxonomic hierarchy are exhaustively
classified at the next depth and thereby, additionally ensuring the exclusivity of chosen
purpose-driven differentiating characteristic(s).
• Finally, the entanglement for modelling a chain is eliminated via the canon of modulation
which ensures that there are no missing conceptual links in any possible path of a taxon-
omy. For example, this canon ensures that all the paths in the classification of a domain
of buildings are populated by concepts at all depths and rules out missing links (which
otherwise hints at a many-to-many crossover in the succession of characteristics).
Intensional Definition (Disentanglement): Given the norm-based disentanglement of the
hierarchical taxonomic model, the final entanglement at the intensional definition level [41] is
fixated by precisely determining the relations (object properties) and attributes (data properties)
out of the manifoldness that ought to be encoded by the (developed) ontology for each concept
in its hierarchy.
For example, we conceptualize Burj Khalifa as a hotel characterized via the following set of
attributes: {number of rooms, number of VIP suites} given the modelling purpose of classifying
and capturing the internal infrastructural details of entities in the hospitality sector. Further, for
instance, we model the relation between the concepts of a hotel and a conference in terms of the
hotel being the main venue of the conference thus again ruling out many-to-many possibilities
with respect to relations.
As a summative observation, notice that the conceptual disentanglement across the different
levels of conceptual representation enforces a novel correspondence amongst its mental model
(which is language-agnostic), ontology (expressed in a formal ontology language) and its
underlying logical axiomatization (expressed as a decidable fragment of first order logic).
4. Conclusive Research Implications
Given the explication of the phenomenon of Conceptual Entanglement in domain ontologies
and a multi-level conceptual modelling foundation in the form of Conceptual Disentanglement,
the next key question becomes the development of a dedicated methodology for designing as
well re-engineering conceptually disentangled domain ontologies. The state-of-the-art ontology
development methodologies (see [42, 43, 4]) are not founded in (dis)entanglement and thus are
not suitable to be exploited in our case. Firstly, none of the above methodologies recognize, in
an entirety, the five-layered ordered phenomenon of conceptual entanglement (perhaps, due
to their difference in methodological focus). Secondly, none of the above methodologies are
tailor-made for engineering conceptually disentanged classificatory ontologies [44], a special
form of domain ontologies which are pivotal for knowledge organization and representation in
digital libraries and repositories. Moreover, the same reasons also hold for ontology development
methodologies developed in the context of engineering ontologies in different domains, e.g.,
see [45, 46] for knowledge-graph based ontology engineering, [47] for life sciences, [48] for
chatbots, [49] for industries, [50] for geospatial domain etc.
In conclusion, the paper introduced the novel (general) phenomenon of Conceptual Entan-
glement which is unavoidable while developing ontologically grounded conceptual models. It
also proposed and exemplified the multi-level conceptual modelling strategy of Conceptual
Disentanglement as a solution to conceptual entanglement towards developing conceptually
disentangled domain ontologies.
Our ongoing work, thus, involve developing a full fledged methodology with concrete con-
ceptual and engineering tools for developing conceptually disentangled domain ontologies.
Acknowledgement
Supported by MF No: 222879, the EU H2020 ELITE-S MSC Grant Agreement No. 801522, SFI
and the ERDF through the ADAPT CDCT Grant Number 13/RC/2106_P2 and DAVRA Networks.
References
[1] R. Hull, Managing semantic heterogeneity in databases: a theoretical prospective, in:
Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles
of database systems, 1997, pp. 51–61.
[2] W. M. Ramsey, Representation reconsidered, Cambridge University Press, Cambridge,
2007.
[3] M. Bagchi, X. Ma, X. Pengzhen, N. Matta, Q. Chen, S. Choemprayong, C. Siridhara, Knowl-
edge organization, Official Journal of the International Society for Knowledge Organization
ISSN 943 (2021) 7444.
[4] M. Bagchi, A large scale, knowledge intensive domain development methodology, Knowl-
edge Organization 48 (2021) 8–23.
[5] M. Bagchi, Towards knowledge organization ecosystem (koe), Cataloging & Classification
Quarterly 59 (2021) 740–756.
[6] M. Bagchi, A diversity-aware domain development methodology, PhD Symposium, 41st
International Conference on Conceptual Modelling (ER 2022); arXiv:2208.13064 (2022).
[7] S. Das, Domain Modeling Theory and Practice, Ph.D. thesis, University of Trento, 2018.
[8] A. Gangemi, N. Guarino, C. Masolo, A. Oltramari, L. Schneider, Sweetening ontologies with
dolce, in: International conference on knowledge engineering and knowledge management,
Springer, 2002, pp. 166–181.
[9] B. Smith, A. Kumar, T. Bittner, Basic formal ontology for bioinformatics (2005).
[10] INSPIRE, D2.8.ii.2 data specification on land cover – technical guidelines,
https://inspire.ec.europa.eu/id/document/tg/lc (2013).
[11] G. Guizzardi, T. Halpin, Ontological foundations for conceptual modelling, Applied
Ontology 3 (2008) 1–12.
[12] S. Das, S. Roy, Faceted ontological model for brain tumour study., Knowledge Organization
43 (2016).
[13] S. Das, P. Hussey, Contsonto: a formal ontology for continuity of care, in: pHealth 2021,
IOS Press, 2021, pp. 82–87.
[14] V. Ventrone, Semantic heterogeneity as a result of domain evolution, ACM SIGMOD
Record 20 (1991) 16–20.
[15] F. Hakimpour, A. Geppert, Resolving semantic heterogeneity in schema integration, in:
Proceedings of the international conference on Formal Ontology in Information Systems-
Volume 2001, 2001, pp. 297–308.
[16] M. Verdonck, F. Gailly, S. de Cesare, G. Poels, Ontology-driven conceptual modeling: A
systematic literature mapping and review, Applied Ontology 10 (2015) 197–227.
[17] H. C. Mayr, B. Thalheim, The triptych of conceptual modeling, Software and Systems
Modeling 20 (2021) 7–24.
[18] F. Baader, I. Horrocks, U. Sattler, Description logics, in: Handbook on ontologies, Springer,
Berlin, Heidelberg, 2004, pp. 3–28.
[19] M. R. Genesereth, N. J. Nilsson, Logical foundations of artificial intelligence, Morgan
Kaufmann, Massachusetts, 2012.
[20] N. Guarino, D. Oberle, S. Staab, What is an ontology?, in: Handbook on ontologies,
Springer, Berlin, Heidelberg, 2009, pp. 1–17.
[21] D. Reisberg, The Oxford handbook of cognitive psychology, Oxford University Press,
Oxford, 2013.
[22] E. Margolis, S. Laurence, Concepts, in: E. N. Zalta (Ed.), The Stanford Encyclopedia of
Philosophy, Spring 2021 ed., Metaphysics Research Lab, Stanford University, Stanford,
2021.
[23] N. Guarino, G. Guizzardi, J. Mylopoulos, On the philosophical foundations of conceptual
models, Information Modelling and Knowledge Bases 31 (2020) 1.
[24] D. Wiśniewski, J. Potoniec, A. Ławrynowicz, C. M. Keet, Analysis of ontology competency
questions and their formalizations in sparql-owl, Journal of Web Semantics 59 (2019)
100534.
[25] R. W. Brown, E. H. Lenneberg, A study in language and cognition., The Journal of
Abnormal and Social Psychology 49 (1954) 454.
[26] L. Boroditsky, How language shapes thought, Scientific American 304 (2011) 62–65.
[27] S. Borgo, R. Ferrario, A. Gangemi, N. Guarino, C. Masolo, D. Porello, E. M. Sanfilippo,
L. Vieu, Dolce: A descriptive ontology for linguistic and cognitive engineering, Applied
ontology (????) 1–25.
[28] M. L. Zeng, Interoperability, KO Knowledge Organization 46 (2019) 122–146.
[29] M. Krötzsch, F. Simancik, I. Horrocks, A description logic primer, arXiv preprint
arXiv:1201.4089 (2012).
[30] P. Hussey, S. Das, S. Farrell, L. Ledger, A. Spencer, A knowledge graph to understand
nursing big data: case example for guidance, Journal of Nursing Scholarship 53 (2021)
323–332.
[31] M. Grüninger, M. S. Fox, The role of competency questions in enterprise engineering, in:
Benchmarking—Theory and practice, Springer, 1995, pp. 22–31.
[32] D. M. Pisanelli, A. Gangemi, M. Battaglia, C. Catenacci, Coping with medical polysemy in
the semantic web: the role of ontologies, in: MEDINFO 2004, IOS Press, 2004, pp. 416–419.
[33] R. Fidel, Who needs controlled vocabulary, Special libraries 83 (1992) 1–9.
[34] R. Artstein, Inter-annotator agreement, Handbook of linguistic annotation (2017) 297–313.
[35] D. Perko, P. Jordan, B. Komac, Exonyms and other geographical names, Acta geographica
Slovenica 57 (2017) 99–107.
[36] D. Vrandečić, M. Krötzsch, Wikidata: a free collaborative knowledgebase, Communications
of the ACM 57 (2014) 78–85.
[37] N. Guarino, C. Welty, Evaluating ontological decisions with ontoclean, Communications
of the ACM 45 (2002) 61–65.
[38] C. Welty, W. Andersen, Towards ontoclean 2.0: A framework for rigidity, Applied Ontology
1 (2005) 107–116.
[39] S. R. Ranganathan, Prolegomena to Library Classification, Asia Publishing House, New
York, 1967.
[40] S. R. Ranganathan, Philosophy of library classification, Sarada Ranganathan Endowment
for Library Science, Bangalore, India, 1989.
[41] H. E. Thomsen, International standard iso 704: 2009: Terminology work-principles and
methods (2009).
[42] M. Fernández-López, A. Gómez-Pérez, N. Juristo, Methontology: from ontological art
towards ontological engineering (1997).
[43] M. C. Suárez-Figueroa, A. Gómez-Pérez, M. Fernández-López, The neon methodology for
ontology engineering, in: Ontology engineering in a networked world, Springer, 2012, pp.
9–34.
[44] M. Bagchi, S. Das, Conceptually disentangled classificatory ontologies, in: 15th Seminar
on Ontology Research in Brazil (ONTOBRAS), Virtual, 2022.
[45] M. Bagchi, A Knowledge Architecture using Knowledge Graphs, Master’s thesis, Indian
Statistical Institute, Bangalore, 2019.
[46] M. Bagchi, D. Madalli, Domain visualization using knowledge cartography in
the big data era: A knowledge graph based alternative, in: International Con-
ference on Future of Libraries: Jointly organized by Indian Institute of Man-
agement (IIM) Bangalore and Indian Statistical Institute (ISI) Kolkata, Bangalore;
https://library.iimb.ac.in/conference2019/contributorspresentations, 2019.
[47] B. Smith, W. Ceusters, Ontological realism: A methodology for coordinated evolution of
scientific ontologies, Applied ontology 5 (2010) 139–188.
[48] M. Bagchi, Conceptualising a library chatbot using open source conversational artificial
intelligence., DESIDOC Journal of Library & Information Technology 40 (2020).
[49] M. Poveda-Villalón, A. Fernández-Izquierdo, M. Fernández-López, R. García-Castro, Lot:
An industrial oriented ontology engineering framework, Engineering Applications of
Artificial Intelligence 111 (2022) 104755.
[50] I. Budak Arpinar, A. Sheth, C. Ramakrishnan, E. Lynn Usery, M. Azami, M.-P. Kwan,
Geospatial ontology development and semantic analytics, Transactions in GIS 10 (2006)
551–575.