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REVIEW 4 major objections 5 minor 28 references

Discerning and Characterising Types of Competency Questions for Ontologies

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper introduces a first model that classifies competency questions for ontologies into five types—scoping, validating, foundational, relationship, and metaproperty—each with its own purpose and required components.

desk verdict A genuinely new five-type taxonomy for competency questions and a public repository, but the boundaries between types rest more on intent than on question text, so the distinctness claim needs qualification. read the letter →

arxiv 2412.13688 v1 pith:UKIX4L3P submitted 2024-12-18 cs.AI cs.CL

C. Maria Keet , Zubeida Casmod Khan This is my paper · ORCID
classification cs.AIcs.CL
keywords competencyquestionsontologyengineeringdevelopmentscopingvalidationfoundationalalignmentmetapropertiesROCQS
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Competency questions (CQs) are widely used to scope and validate ontologies, but engineers get little guidance on what makes a good one. This paper tries to establish that CQs are not all alike: they serve at least five distinguishable purposes, and each purpose can be recognised by characteristic components in the question. It proposes the Questions for Ontologies (QuO) model, which defines Scoping, Validating, Foundational, Relationship, and Metaproperty CQs, each with a formal characterisation and a diagrammatic specification. The authors support the model with a coffee-ontology user story, a mapping onto stages of a standard ontology-engineering methodology, and a FAIR repository of 438 annotated CQs (ROCQS). If the model is right, ontology engineers gain a vocabulary for saying what a CQ is for and what it must contain, which should reduce ambiguity and improve ontology quality.

What carries the argument

The carrying object is the QuO model itself: a taxonomy of five CQ types anchored by formal definitions (Definitions 1 to 6) and entity-relationship diagram snippets that specify the mandatory constituents of each type. The key components are the primitives DomainEntity, SubjectDomain, Ontology, foundational-ontology elements, Relationship, Metaproperty, and Metametaproperty, plus the closed sets of relational properties and metaproperties; the definitions use these to give each type a distinct purpose and a distinguishing shape. The model is put to work by annotating a standard development methodology with the CQ types relevant at each stage and by populating ROCQS, a FAIR repository of 438 CQs with examples and templates for each type.

What would settle it

Look at the 438 questions in ROCQS and have two or more ontology engineers, working only from the paper's definitions, assign each question to exactly one type; if agreement is low or many questions are assigned to multiple types, the claimed distinctness is not present in the data.

Watch

Extended reading notes

Core claim

The paper's central claim is that the apparent jumble of competency questions in ontology development resolves into a first model with five principal types. Scoping CQs delimit the subject domain and must mention a domain entity and a subject domain; Validating CQs test an ontology's content and must be formalisable and answerable within the ontology's language; Foundational CQs interrogate a domain entity against the vocabulary of a foundational ontology to support alignment; Relationship CQs probe a relationship's arity, elementariness, participants, or relational properties; Metaproperty CQs classify an entity according to metametaproperties such as rigidity, identity, unity, and dependence. Each type is given a formal definition with distinct constituent elements, and the paper argues this accounts for the different uses of CQs documented in the literature. It further demonstrates where each type fits in ontology-development tasks and supplies an annotated repository, ROCQS, containing 438 CQs as evidence and as a resource.

Load-bearing premise

The taxonomy assumes that a competency question's purpose can be read off from its wording and context, and that those purposes sort cleanly into five categories that different ontology engineers would agree on.

Editorial extensions

If this is right

  • Developers can choose a CQ type by task: scoping during requirements, validation during implementation, foundational alignment when connecting to upper-level ontologies, and metaproperty or relationship questions during formalisation.
  • Validating CQs come with an expressiveness constraint: a question only qualifies if the ontology language can express and answer it, which gives a concrete criterion for dropping unusable CQs.
  • Foundational CQs make alignment errors diagnosable: a question is faulty when it asks for distinctions the target foundational ontology does not make.
  • Relationship and metaproperty CQs are answered by the modeller through ontological analysis rather than by querying the ontology, changing what tool support for them should look like.
  • The ROCQS repository provides a shared, annotated pool of 438 CQs whose type labels can be used to evaluate automated CQ generation and authoring tools.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the formal definitions give necessary conditions rather than a decision procedure, the five types are likely to overlap in practice: one and the same question can serve scoping and validation, so the model may be most useful as a multi-label characterisation rather than a partition.
  • The same component-based method could be carried over to other requirements-engineering settings: classify any requirement question by purpose and mandatory constituents, then use the type to guide repair; this would test whether the underlying idea generalises beyond ontologies.
  • A low-cost experiment the paper does not run is to give the definitions to independent ontology engineers and measure whether they assign repository questions to the same types; a positive result would turn the model into an operational coding scheme.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a model, QuO, for competency questions (CQs) in ontology engineering, claiming that CQs can be discerned and characterised into five principal types: Scoping (SCQ), Validating (VCQ), Foundational (FCQ), Relationship (RCQ), and Metaproperty (MpCQ). Each type is given an informal definition, an EER-style diagram, and a first-order logic formalisation. The paper illustrates the model with a coffee-ontology user story, maps the types onto stages of the NeOn methodology, discusses faulty CQs, and introduces ROCQS, a FAIR repository of 438 annotated CQs. The central claim is that the five types have distinct purposes and identifiable distinct constituent elements, enabling better authoring, validation, and deployment of CQs in ontology development.

Significance. The paper addresses a real gap: although CQs are widely used in ontology engineering, there is little theoretical guidance on their types and constituents. The proposed taxonomy is a useful conceptual contribution if the types can be reliably distinguished, and the accompanying ROCQS repository is a concrete, reusable artifact that can support further empirical work. The paper is also commendable for making its definitions explicit, for attempting formal characterisations, and for connecting the types to methodology stages in NeOn. However, the central claim is currently supported mainly by author-generated examples and a single illustrative user story; there is no inter-annotator study or operational coding procedure. The definitions, as written, do not yet establish that the five types are mutually exclusive or reliably assignable. The significance therefore rests on the plausibility of the taxonomy rather than on demonstrated empirical support, and the formal apparatus contains technical gaps that need to be addressed before it can serve as a reliable basis for classification.

major comments (4)
  1. [§3.2, Definitions 2 and 3] The definitions of SCQ and VCQ do not distinguish the two types. Definition 2 requires mention of a domain entity and use with an ontology and subject domain; Definition 3 requires mention of a domain entity and validation with an ontology. The only formal difference is the unformalised primitive usage versus validate. The paper itself notes that most SCQs are reused for validation, and the example 'Which animals are endangered?' satisfies both definitions. Thus the claimed 'identifiable distinct constituent elements' are not captured by the formalisations. If the types are distinguished by intent or purpose, that predicate must be made explicit, or the taxonomy should be presented as a purpose-based idealisation rather than a constituent-based classification. This is load-bearing because the central claim is that the types are discernible from their constituents.
  2. [§3.2, drRCQ formalisation and Definition 3] The first-order logic formalisation of drRCQ contains an unbound variable w: the formula states Ontology(w) and containsVocab(w,y) and containsVocab(w,z) without quantifying w. In addition, the expressiveness constraint in Definition 3, L_v ⊆ L_o, is asserted as sufficient for VCQ answerability without proof; answerability also depends on content coverage and on the query language used. These issues matter because the formalisations are presented as evidence that the types are distinct and can be operationalised.
  3. [§4.1 and §5] The evaluation does not support the claim that the taxonomy can be reliably discerned. Section 4.1 presents a single user story with examples authored for the purpose, and Section 5 acknowledges that the authors 'needed to design templates and generate CQs for other categories.' There is no inter-annotator study, no coding procedure, and no independent corpus annotation beyond the authors' own repository. The paper should either provide an operational annotation protocol with agreement measures or explicitly reposition the taxonomy as a hypothesis whose empirical validation is future work. Without this, the repository's type labels cannot be taken as evidence of distinct types.
  4. [§3.2, closing comparison of CQ scope] The paper asserts that 'the ontological CQs—aRCQ, efRCQ, rpRCQ, MpCQ—relate to an entity and the answer to the CQ ought to necessarily hold across all ontologies.' This strong claim is not established by the definitions. For example, a rpRCQ is defined with respect to a particular ontology o and requires that the relational property be expressible in the representation language of o, which makes the answer ontology-relative. Please either justify the universality claim or qualify it, since it is used to divide ontological from domain CQs.
minor comments (5)
  1. [§3.2, Definition 2] The formalisation contains a typo: 'SQC' should be 'SCQ'.
  2. [§4.1] The bullet list uses 'FQCs' where 'FCQs' is intended.
  3. [Throughout] The methodology name is written inconsistently as both 'NeON' and 'NeOn'; please standardise.
  4. [§4.3] The repository is referenced by a URL only; a DOI or other stable identifier would strengthen the FAIR claim.
  5. [§4.2] The distinction between VCQs and the listed 'constraint-checking queries' is unclear, since the example 'Are there animals that are both a carnivore and a herbivore?' could also be read as a VCQ; clarifying the criterion would help readers apply the taxonomy.

Circularity Check

2 steps flagged · score 6.0 of 10

SCQ/VCQ distinction rests on unformalised purpose predicates rather than distinct constituents, and the new CQ types are demonstrated with self-authored examples, making the taxonomy's central distinctness claim partially circular.

  1. self definitional [Section 3.2, Definitions 2 and 3 and surrounding text]
    "A Validation CQ (VCQ) may sound as if it were the same as, or else a kind of a SCQ, or the set of VCQs for an ontology to be a subset of the set of SCQs, since most SCQs are being reused for validation. There are subtle differences, however, both with respect to intent or purpose (validation versus scoping) and they must be answerable by the ontology."

    Definition 2 formalises SCQ as ∀x(SCQ(x) → ∃y,z,w(mention(x,y) ∧ DomainEntity(y) ∧ usage(x,z,w) ∧ Ontology(z) ∧ SubjectDomain(w))) and Definition 3 formalises VCQ as ∀x(VCQ(x) → ∃y,z(mention(x,y) ∧ DomainEntity(y) ∧ validate(x,z) ∧ Ontology(z))). Both require mention of a DomainEntity and an Ontology; the only difference is the unformalised purpose predicates usage versus validate. The paper states most SCQs are reused for validation, so any SCQ sentence also satisfies the VCQ conditions when the intent changes. The claimed 'identifiable distinct constituent elements' are therefore not distinct; type membership is assigned by purpose labels built into the definitions, not by constituents of the question.

  2. other [Section 5 and Section 4.3 (ROCQS); cf. Abstract]
    "While there were ample SCQs and VCQs readily available ... we needed to design templates and generate CQs for other categories. ... we created the Repository of Ontology Competency QuestionS (ROCQS), which consists of 438 CQs covering all types described in this paper."

    The repository is presented as demonstrating the distinctions among types, but the novel types (FCQ, RCQ, MpCQ) were instantiated by the authors using templates designed from Definitions 4-6. The CQs in the user story are likewise generated by applying the formal definitions. These self-authored instances cannot independently confirm that the categories are real or mutually distinguishable; they are constructed to satisfy the very definitions they are then used to illustrate, so the demonstration is an artefact of construction rather than external validation. Existing SCQ/VCQ data provide some independent grounding, which is why the circularity is partial.

full rationale

The paper proposes a conceptual taxonomy rather than deriving a mathematical or empirical prediction, so most of the content is definitional and not circular in the strict sense. However, the central claim that the five CQ types are distinguished by 'identifiable distinct constituent elements' is contradicted by the paper's own formalisations: the SCQ and VCQ definitions have the same structural constituents and differ only in primitive purpose predicates, and the text explicitly says most SCQs are reused for validation. This means the SCQ/VCQ boundary is imposed by definitional purpose labels rather than discovered in the question text. For the three novel types (FCQ, RCQ, MpCQ), the paper acknowledges it had to generate CQs and templates because such examples were scarce; those self-authored examples are then placed in ROCQS and the coffee user story and presented as demonstrating the distinctions. The repository thus partly validates the model with instances produced from the model, which is a self-definitional loop. The existing dataset of SCQs/VCQs and the alignment-question literature provide some external grounding, so the paper is not wholly circular, but the distinctive part of the contribution rests on self-generated illustration rather than independent evidence. Score 6 reflects partial circularity in the central distinctness claim.

Assumptions & free parameters 1 free parameters · 4 assumptions · 1 invented entities

The QuO model rests on a hand-selected number of categories, a set of primitives with assumed natural-language meanings, and the OntoClean taxonomy of metaproperties. The novel categories are instantiated with examples written by the authors, so independent evidence for the taxonomy is currently limited.

free parameters (1)
  • Number of CQ types = 5
    The number of categories is a hand-chosen structural decision based on the authors' analysis of CQ purposes; it is not estimated from data and the authors acknowledge that further types may be identified.
assumptions (4)
  • ad hoc to paper The set of all competency questions for ontologies can be partitioned into five principal types with distinct purposes.
    This is the core claim of the QuO model (Definitions 1 to 6); no formal derivation or empirical fit is provided.
  • domain assumption The primitives 'mention' and 'usage' have their usual natural language meaning and can be suitably formalised.
    Used in the FOL formalisations of SCQs and VCQs in Definitions 2 and 3; no formal semantics is given.
  • domain assumption The OntoClean metametaproperties and metaproperties form a closed set usable for MpCQ classification.
    MpCQs in Definition 6 rely on the OntoClean taxonomy from Guarino and Welty (refs [10,11]).
  • ad hoc to paper The expression-containment constraint L_v ⊆ L_o is sufficient to guarantee that a VCQ is answerable.
    Definition 3 introduces this constraint to ensure VCQ answerability, but no proof is provided that it is sufficient.
invented entities (1)
  • Five-type taxonomy of competency questions (SCQ, VCQ, FCQ, RCQ, MpCQ)
    purpose: To classify competency questions for ontology development and to guide authoring, validation, and repository organization.
    The categories are new conceptual constructs introduced by the paper; their validity is demonstrated using self-generated examples and a hypothetical user story rather than external benchmarks or independent annotations.

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Cite this review

Pith. "Pith review of Discerning and Characterising Types of Competency Questions for Ontologies." pith.science (2026). https://pith.science/paper/UKIX4L3P

@misc{pith2026241213688,
  author       = {Pith},
  title        = {Pith review of: Discerning and Characterising Types of Competency Questions for Ontologies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UKIX4L3P}},
  note         = {Machine review of arXiv:2412.13688}
}
read the original abstract

Competency Questions (CQs) are widely used in ontology development by guiding, among others, the scoping and validation stages. However, very limited guidance exists for formulating CQs and assessing whether they are good CQs, leading to issues such as ambiguity and unusable formulations. To solve this, one requires insight into the nature of CQs for ontologies and their constituent parts, as well as which ones are not. We aim to contribute to such theoretical foundations in this paper, which is informed by analysing questions, their uses, and the myriad of ontology development tasks. This resulted in a first Model for Competency Questions, which comprises five main types of CQs, each with a different purpose: Scoping (SCQ), Validating (VCQ), Foundational (FCQ), Relationship (RCQ), and Metaproperty (MpCQ) questions. This model enhances the clarity of CQs and therewith aims to improve on the effectiveness of CQs in ontology development, thanks to their respective identifiable distinct constituent elements. We illustrate and evaluate them with a user story and demonstrate where which type can be used in ontology development tasks. To foster use and research, we created an annotated repository of 438 CQs, the Repository of Ontology Competency QuestionS (ROCQS), incorporating an existing CQ dataset and new CQs and CQ templates, which further demonstrate distinctions among types of CQs.

Figures

Figures reproduced from arXiv: 2412.13688 by the authors.

Figure 1
Figure 1. Illustration of the key aspects of SCQs and VCQs for ontologies, in EER notation with annotation. or interpretations for the ontology or at least the so-called search space for candidate content to be added to the ontology or to be reused from an existing ontology. It is graphically sketched in [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Illustration of the key aspects of FCQs, which are used to interrogate an entity to be aligned to an entity in a foundational ontology. us consider representation language Lv for the axiom(s) needed to validate the VCQ and Lo either the language used to represent the ontology or the one permitted by the project specification, whichever is more expressive, then Lv ⊆ Lo. The reason why this constraint is included is b… view at source ↗
Figure 3
Figure 3. Illustration of the types of RCQs for ontologies and their salient aspects. but unique among other types of CQs, reference to any one element of the closed set of relational properties from R, hence, we obtain ∀x(rpRQC(x) → ∃=1 y(re f erent(x, y)∧ RelationalProperty(y))). Unlike the SCQs and VCQs on the one hand and FCQs on the other, as RCQ without the further subtyping, they can be either domain CQs or ontological… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: MpCQs for ontologies in EER notation, annotated with typical examples. The structure of an MpCQ is sketched in [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: The NeON methodology annotated where diverse types of CQ can be used for various tasks in the process. the available options. In its summary figure, we indicated where which type of CQ may be used, as shown in [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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