In mainstream knowledge organization, classificatory ontologies are widely employed for classifying, annotating and searching for data specific to a particular domain. The key bottleneck, however, remains the fact that these ontologies fail to encode heterogeneity in conceptual representations and are representationally static, i.e. they assume a one-size-fits-all argue that the above bottleneck fundamentally ignores the phenomenon of Conceptual Entanglement, i.e. the many-to-many entanglement between the source and the target conceptual representation existing independently within each of the following five levels: Perception, Labelling, Semantic Alignment, Hierarchical Modelling and Intensional Definition . To that end, we also introduce, at a high level, the notion of Conceptual Disentanglement which can be seen as a multi-level conceptual modelling strategy to enforce one-to-one correspondences disentangling the many-to-many entanglement within each of the above level, tuned to the purposive viewpoint of the chosen target reality.
Consider the motivating case of the Infinity Coast 1, one of the tallest buildings in Brazil. It can be
conceptually described and represented in diferent ways, for instance, as a conference venue in
a database for conference hosts, a gourmet space in a food destination database or as a party hub
in an event management database. Notice that the conceptual representations for each of the
above cases, while referring to the same entity, are nevertheless semantically heterogeneous (see
[
In this work, we concentrate on classificatory ontologies [
Such a formalism, in efect, ignores the phenomenon of Conceptual Entanglement, viz., a
layered, many-to-many entanglement from the perceptual generation of concepts to their
classificatory ontological formalization. We outline five ordered, functionally linked levels
into which Conceptual Entanglement distributes. The first level of entanglement ( Perception)
is generated due to the diferent ways of perceiving diferent real-world entities [
Our solution approach has two core assumptions. Firstly, we assume that there is “only one
‘real world’ but many diferent descriptions of this world depending on the aims, methodology
and terminology of the observer” [
The remainder of the paper is organized as follows: Section (2) details the layered phenomenon of conceptual entanglement. Section (3) elucidates the proposed conceptual disentanglement strategy for classificatory ontologies. Section (4) concludes the paper with a brief summative discussion of the work by comparing it to the relevant state-of-the-art.
A conceptual representation is defined as “an abstract, simplified view of the world that we wish to represent” [18] and is fundamentally mental in nature [19]. We adhere to an extended notion of conceptual representation which models concepts via a five-level stratification: Perception, Labelling, Semantic Alignment, Hierarchical Modelling and Intensional Definition . Founded in the aforementioned stratification, we define Conceptual Entanglement to be the many-to-many entanglement between the source and the target conceptual representation that is ubiquitous with respect to the above levels (individually as well as cumulatively across levels). We now elucidate the many-to-many conceptual entanglement as it instantiates in each level.
Perception: Concepts, universally regarded as the “building blocks of thoughts” [20], are aggregated and abstracted via the process of perceiving a target reality. Note that both the realworld referrent as well as their relevant properties are concepts in our view. To that end, the fact that the same referrent or property can be perceived variously by diferent agents depending on the diferent viewpoints leads to, we argue, the many-to-many entanglement at the perceptual level [21]. For instance, the Infinity Coast can be perceived as a conference venue or a party hub, each of which can also be a valid perception for the Epic Tower 2.
Labelling: Given perception, the focus of the second level is on linguistically labelling the perceived concepts for human as well as machine interaction. Nevertheless, the activity of labelling is non-trivial due to the deep interaction between language and thought. We mention two highlights. Firstly, languages are “itemized inventories” of a target reality [22]. Secondly, every language generates a similar but not same labelling of a perceived concept [23] inducing the many-to-many entanglement. For instance, the same perception of the Infinity Coast as a ’conference venue can be heterogeneously labelled as a locus or as an emplacement in English. Moreover, the same perception can have further divergent labellings in multilingual scenarios.
Semantic Alignment: The third level aligns the labelled concepts to top-level ontological
distinctions such as whether it is an independent or a dependent concept, or, for instance, a process
or an event [
Hierarchical Modelling: The fourth level concentrates on modelling the labelled, semantically constrained concepts in a taxonomical hierarchy. We ground our hierarchy modelling design in the four-step ontologically well-founded classification theory by Ranganathan [ 24, 25]. The first step concerns deciding the many diferentiating characteristics for classification at diferent depths in the hierarchical tree. The second step involves the succession of characteristics which determine how diferent concepts are organized and applied at diferent successive level in the hierarchy. The third step and the fourth step concentrates on the consistency of the organization of multiple concepts horizontally across multiple depths (each depth termed as an array) and vertically across multiple paths (each path termed chain) in the taxonomic tree respectively. For example, the Infinity Coast as a building can be equally classified based on the characteristic colour or purpose. Similarly, given the first classifying characteristic as colour, the next classificatory characteristic can be, for instance, the square footage. All of these characteristics are, in turn, applicable for hiearchically modelling many other concepts in the same/similar target reality.
Intensional Definition : The fith and the final level of entanglement occurs from modelling
the concepts at an intensional level, wherein, each individual concept in the taxonomic hierarchy
is defined via their appropriate attributes, thereby rendering the hierarchical model as a formal
classificatory ontology [
To sum up, we mention an important observation. The many-to-many conceptual entanglement across the diferent levels of conceptual representation results in a non-trivial incorrespondence amongst its mental model (which is language-agnostic), classificatory ontological model (which, almost always, is expressed in a formal ontology language) and its underlying logical axiomatization (expressed, mostly, as a decidable fragment of first order logic).
We propose Conceptual Disentanglement as a conceptual modelling strategy to tackle the fivefold characterization of conceptual entanglement that instantiates in modelling classificatory ontologies. It refers to a set of guiding normative principles which, if considered as best practice for each of the five levels, can enforce one-to-one correspondences with respect to the many-tomany entanglement at each level while, concurrently, factoring in the required heterogeneity of the target reality that needs to be modelled. In efect, conceptual disentanglement can provide the conceptual modelling foundations based on which, later on, a methodology for dynamically harmonizing diverse conceptual representations into a single classificatory ontology can be developed. We now elucidate conceptual disentanglement strategy specific to each level.
Perception: Firstly, we concentrate on the norms which tackle the conceptual entanglement instantiated at the Perception level. We recommend the following best practices: • At the outset, the target reality should be specified with precision in terms of their spatiotemporal coverage. In case the target reality is comprised of several smaller component target realities, it should be modelled as a disjoint union of the component realities (i.e. as a disjoint union of component spatio-temporal coverages). • Given the precise specification of the target reality, the second sub-activity should determine not only the concepts which should be modelled within the chosen target reality but also their purpose-driven viewpoints.
The aforementioned best practices allows precise selection the intended ontological commitment of the target reality and thus avoid instances of overcommitment and undercommitment which frequently plague domain classificatory ontologies. This, in efect, reduces the many-to-many entanglement at the conception layer to a one-to-one correspondence. For example, fixing the geospatial coordinates and temporal duration of the Infinity Coast modelled from two viewpoints, that of a cinema theatre and a party hub.
Labelling: Secondly, we outline the guiding norms for conceptual disentanglement with respect to labelling concepts: • Fixation of the base natural language(s), e.g., English, Portugese etc., and corresponding controlled vocabulary terms, i.e., a widely inter-labeller agreed terminology that can be exploited to unambiguously label the perceived concepts. International terminological standards for various domains can be utilized for this purpose. Such a choice forces a one-to-one correspondence out of the multiplicity of possible labellings in the selected language(s) on one hand, and neutralizes the efect of linguistically-grounded labelling conflicts such as endonym and exonym [26] on the other hand. • Optionally, the next step, especially key in multilingual data classification and search scenarios [27], should be to add global, unambiguous identifiers (such as from Wikidata 3) to disambiguate each such labelled concepts.
Semantic Alignment: For this level, an ontological analysis [28] should be performed with respect to each of the labelled concepts (including both referrents and their relevant attributes) from the previous level. The key aim of the ontological analysis should be to determine the exact ontological nature of each labelled concepts via their semantic conformance to a specific top-level ontological distinction. For example, in a specific case, the analysis might result in aligning the concept of Infinity Coast to the top-level distinction of a dependent concept.
There are two crucial advantages behind disentangling the conceptual entanglement at the semantic alignment level. Firstly, the ontological analysis on labelled concepts disobfuscates the ‘arbitrarity’ in the intended meaning of such concepts [29, 30] by ascertaining their exact ontological nature, and, in turn, streamlines a one-to-one correspondence between the labelled concept and the top-level distinction out of the many-to-many entanglement exisiting previously. Secondly, and more relevant from the application perspective, the semantic conformance to toplevel ontological distinctions also facilitate linking the concepts in a classificatory ontology to the Linked Open Data Cloud4, thus, making it (and the data it classifies and annotates) interoperable with a highly interconnected network of semantically classified data and knowledge.
Hierarchical Modelling: Given the conformance of the labelled concepts to the top-level ontological distinctions, we now focus on the conceptual disentanglement best practices while modelling the taxonomical hierarchy. As from Section 2, we also ground our solution in Ranganathan’s classification theory [ 24, 25]. In particular, we exploit the mutually coordinating normative principles proposed by Ranganathan [24] (termed as canons), for each of the four steps of building a taxonomy (see Section 2), to disentangle the many-to-many entanglement immanent in each step: • In the first step, we reduce the many-to-many entanglement in the selection of the diferentiating characteristic to one-to-one correspondence 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 a local government, we fix legal nature of building as the first classification characteristic. • The many-to-many entanglement for the second step of choosing the succession of characteristics is tackled by employing the canon of relevant succession which enforces that the selection of successive diferentiating 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 many-to-many entanglement for the third step of organizing an array is tackled by the canon of exhaustiveness which ensures that all the concepts at a specific depth in the taxonomic tree is exhaustively classified at the next depth and thereby ensures the exclusivity of chosen purpose-driven diferentiating characteristic(s). 3https://www.wikidata.org/wiki/Wikidata:Main_Page 4https://lod-cloud.net/ • Lastly, the many-to-many entanglement for the fourth step of organizing a chain is tackled by the canon of modulation which ensures that there are no missing conceptual links in any path of a taxonomy. 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 facilitates ruling out missing links which indicates a many-to-many crossover in the succession of characteristics.
Intensional Definition : Given the disentanglement of the taxonomic hierarchy, the final many-to-many entanglement is fixated by precisely determining the attributes that ought to be encoded by the classificatory ontology for each concept in its hierarchy. For example, as from before, we fix the attributes of the concept of Conference Venue as the {number of rooms, number of seminar halls} given our purpose of accommodating co-located talks and tutorial speeches.
To sum up, note that the conceptual disentanglement across the diferent levels of conceptual representation enforces a novel correspondence amongst its mental model (which is languageagnostic), classificatory ontology (expressed in a formal ontology language) and its underlying logical axiomatization (expressed as a decidable fragment of first order logic). Also notice that the decision to reuse concepts as is from existing ontologies or knowledge classification schemes, at any level of conceptual representation as characterized above, is a decision completely dependent on the project team efectuating conceptual disentanglement.
Given the explication of the phenomenon of Conceptual Entanglement in classificatory ontologies
and a possible solution strategy in the form of Conceptual Disentanglement, the next key
question becomes the reuse and/or development of a methodology for engineering conceptually
disentangled classificatory ontologies. The existing landscape of general-purpose ontology
development methodologies (see [31] for a survey on early generation methodologies; also see
[
In summary, the short paper introduced the novel phenomenon of Conceptual Entanglement in classificatory ontologies within the broad context of knowledge organization. It also proposed, at a high level, the conceptual modelling strategy of Conceptual Disentanglement as a solution to the above phenomenon.
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