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REVIEW 4 major objections 7 minor 253 references

Conceptual Modeling: Topics, Themes, and Technology Trends

T0 review · 4 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A 5,303-paper survey shows conceptual modeling shifting from data to process models.

desk verdict A useful large-scale map of conceptual modeling research, but the headline trend rests on an unreconciled corpus subset and lacks shipped data; the core narrative is plausible, not confirmed. read the letter →

arxiv 2505.13648 v1 pith:PPCX7ZY7 submitted 2025-05-19 cs.HC cs.DB

V. C. Storey , R. Lukyanenko , A. Castellanos This is my paper · ORCID
classification cs.HCcs.DB
keywords conceptualmodelingstructuredliteraturereviewtopicLatentDirichletAllocationprocessdataemergingtechnologiesinformationsystems
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

This paper surveys more than 5,300 papers across 35 journals and conferences, spanning 1976 to 2022, to establish how conceptual modeling research has evolved over five decades. Its central claim is that conceptual modeling remains anchored in a core of entities, relationships, and processes, even as the field's center of gravity migrated from data-oriented modeling (entity-relationship diagrams, UML class models) to process-oriented modeling (BPMN, process mining) over the past fifteen years. The survey also establishes that major emergent technologies — artificial intelligence, social media, blockchain, and goal- and value-oriented modeling — attract far less conceptual modeling research than their digital prominence would suggest. A sympathetic reader would see this as evidence that the discipline is mature but in need of new frameworks and broader participation.

What carries the argument

The load-bearing mechanism is a structured corpus of 5,303 full-text papers collected from 35 journals and conferences, manually screened by six coders, then analyzed per year. Latent Dirichlet Allocation (a probabilistic method that represents each document as a mixture of topics, each topic as a distribution over words) produces the year-level topic clusters; coherence scores select the number of topics, and a Doc2Vec language model learns semantic similarity between terms such as BPMN and BPEL. The per-year topic clusters are what support the claims about the data-to-process shift and about which topics are absent.

What would settle it

Rebuild the corpus from a venue mix that excludes the most process-oriented conferences, then re-run the same per-year LDA; if process topics no longer dominate the post-2005 clusters, the central shift claim fails, and if AI-related papers appear in large numbers in machine-learning venues under the same keyword rules, the 'AI is underexplored' gap is a sampling artifact.

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Extended reading notes

Core claim

The paper's discovery, on its own terms, is a documented shift in what conceptual modeling research actually studies. Using full-text Latent Dirichlet Allocation topic models run year by year, together with a Doc2Vec language model trained on the corpus, the authors show that until about 2005 the dominant topics were data-oriented concerns — entity-relationship modeling, relational database design, and knowledge modeling — whereas from 2005 to 2020 roughly half of the topic clusters concern processes: business process design, process mining, and process analysis. The same analysis shows a persistent common core of modeling languages, ontologies, and methodological research, which the paper reads as the field's continuity. It then argues that goal, intention, and contingency modeling, along with applications to AI, social media, blockchain, and large language models, remain underexplored, and that the field must broaden its assumptions about who models and what modeling is for.

Load-bearing premise

The whole trend story rests on the assumption that the 35 selected venues together with the keyword protocol and manual screening fairly represent what conceptual modeling research actually is, so if the venue list skews toward process-oriented outlets, the data-to-process shift and the AI/blockchain gaps are artifacts of source selection rather than field-wide facts.

Editorial extensions

If this is right

  • Process modeling, especially BPMN and process mining, will likely remain the most active conceptual modeling research area for the near term.
  • Data-oriented conceptual modeling (ER, UML class diagrams) will continue to lose relative share as relational databases give ground to NoSQL and data lakes, unless new notations emerge.
  • Goal-, intention-, and value-oriented modeling (e.g., the i* tradition) remains a persistently under-filled niche despite decades of calls for more work.
  • Artificial intelligence, social media, blockchain, and large language models are open frontiers where conceptual modeling frameworks tailored to those settings are needed.
  • Conceptual modeling research should broaden from professional analysts to citizen modelers, with instance-based and narrative representations complementing abstraction-based diagrams.

Reading between the lines

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

  • If the shift is real, then conceptual modeling's future value may lie in process and organizational contexts, while data representation is increasingly delegated to machine-learned or schema-less infrastructure.
  • The corpus's heavy weight of process-oriented venues (the ER conference, CAiSE, EMMSAD) could overstate the decline of data modeling; a corpus balanced with data-management venues might show a slower, more regional shift.
  • The Doc2Vec model the authors built is a reusable asset: tracking when terms such as 'large language model' or 'blockchain' begin to cluster with core modeling constructs would give a quantitative early signal of conceptual modeling absorbing a new technology.
  • A direct test of the survey's forward-looking claim is whether the 2023–2030 literature shows AI and goal modeling rising from their current small share.
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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 / 7 minor

Summary. This manuscript presents a structured literature review of conceptual modeling research from 1976 to 2022. The authors assembled a corpus of 5,303 papers from 35 journals and conferences, applied manual screening with an explicit inclusion protocol, ran yearly LDA topic models and a Doc2Vec language model on full texts, and supplemented the quantitative results with qualitative interpretation. The central claims are that conceptual modeling has shifted from data-oriented to process-oriented modeling over the past fifteen years; that core themes such as ER modeling, UML, BPMN, and ontology remain stable; and that technologies such as AI, blockchain, social media, and goal modeling are underexplored relative to their societal importance. The paper closes with a research agenda organized around foundations, non-traditional settings, new frameworks, modeling processes, a broadened user base, and grammar–script relationships.

Significance. The paper offers a large and carefully motivated corpus, and if the empirical claims hold, it would be a useful reference point for the conceptual modeling community. The explicit inclusion protocol, manual screening by multiple coders, comparison with Härer and Fill, and corpora-similarity check using sentence embeddings are genuine strengths that go beyond many prior reviews. The claimed shift from data-oriented to process-oriented modeling and the identification of underexplored areas are consequential for future research agendas. However, the paper's transparency is currently insufficient to support those claims at the level of confidence implied by the abstract, because the corpus totals are unreconciled and the modeling pipeline is not reproducible from the information given.

major comments (4)
  1. [Section 2.2.3, Table 2 vs. Table 4] The paper does not reconcile the corpus totals. Section 2.2.3 and Table 2 state that the final corpus contains 5,303 papers, but Table 4 reports only 4,345 'articles considered' and 3,173 'articles with full text' for the yearly analysis. Since the LDA and term-prevalence analyses in Section 3 are run on the full-text subset, the abstract's 'over 5,300 papers' overstates the evidence base for the central trend claims. The authors should provide a per-source and per-year reconciliation of the 958 missing papers, state explicitly which analyses use which subsets, and justify the decision to focus the statistical analysis on 2005–2020 in Section 3 while Table 4 also reports earlier years with lower full-text availability.
  2. [Section 3.1, Table 2 footnote, Table 4] The early-period comparison is not supported by the available full texts. Table 2 lists 2,062 ER conference papers, but the footnote states that full texts before 2005 were not available; Table 4 shows the entire 1976–2004 bucket as 801 considered and 639 full-text papers across all venues. This means the claim that early conceptual modeling was predominantly data-oriented rests on a possibly non-representative subset, because the flagship venue's early output is missing. The authors should quantify the missing coverage, report topic models separately for ER and non-ER sources, or provide a sensitivity analysis that omits the pre-2005 period from the data-to-process narrative.
  3. [Section 2.3, Table 3] The LDA pipeline is not reproducible and its outputs are not validated. The number of topics is selected by highest coherence for each year with a cap at 15, but the paper reports coherence only for year 2020 in Figure 2; it does not report the selected k for each year or the coherence values for all years, nor does it report LDA stability across random seeds or hyperparameter settings. Doc2Vec is described as built on 2,555 full papers (Table 3, Step f), which contradicts the 3,173 total in Table 4. The authors should release the code, the corpus identifiers, and the topic-term distributions for all years, and provide stability or robustness checks; without these, the interpretive topic labels in Sections 3.1–3.2 cannot be distinguished from researcher judgment.
  4. [Sections 3.2.2, 3.4, 5] The gap conclusions for AI, blockchain, and social media are stated as findings about the field, but the evidence only supports absence from the selected venues and keyword protocol. The sources are dominated by IS/SE/database outlets, and the inclusion keywords listed in Table 1 are terms like 'conceptual model', 'entity-relationship', and 'process'; they do not include terms such as 'model card', 'schema', 'ontology', or 'data model' that AI and blockchain communities also use. The paper should explicitly qualify all gap statements as corpus-relative and, ideally, validate them by running the same topic model on a supplementary sample from AI/blockchain venues or by using broader search terms.
minor comments (7)
  1. [Table 3, Step b] Table 3 expands LDA as 'linear discriminant analysis,' but Section 2.3 and the rest of the paper use LDA for Latent Dirichlet Allocation; please correct the expansion or distinguish the two methods.
  2. [Table 6] The meaning of an X in Table 6 is not defined; the paper does not state a frequency threshold or normalization criterion that qualifies a term for inclusion, so the table is not reproducible without additional detail.
  3. [Table 10] The column 'Frequency (this work)' repeats the same value across multiple topics (e.g., model = 61 appears in every topic row), which makes it unclear whether these are corpus-wide frequencies or per-topic frequencies; clarify how the frequencies were computed.
  4. [Section 3.3, Table 8] The sentenceTransformer similarity analysis reports global overlap scores but does not state what text was embedded (topic labels, topic-term distributions, or full documents) or why 0.784 is interpreted as 'substantial similarity'; include the comparison details.
  5. [Section 2.2.3 and Section 3.3] Several passages contain encoding artifacts (e.g., the 'we used full-text search as opposed to' sentence in Section 2.2.3 and the 's' fragments in Section 3.3); these should be cleaned before publication.
  6. [Table 4 and Section 3] Table 4 includes 2021 and 2022 rows with only CAiSE and EMISAJ papers (footnotes 3 and 4), which is inconsistent with the abstract's claim of coverage to 'the present' and with the stated focus on 2005–2020; state how these years are used or remove them.
  7. [Appendix A] The text refers to Appendix A as containing the entire list of papers considered, but the list is not included in the submitted manuscript; if it is in an online supplement, say so explicitly and provide access.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the survey's trend and gap claims are descriptive summaries of a transparently assembled corpus, not predictions derived from fitted inputs.

full rationale

The paper makes no formal derivation that reduces to its own inputs. Its central empirical statements—the shift from data-oriented to process-oriented modeling and the sparse coverage of AI, blockchain, and social media topics—are summaries of LDA topic models and term prevalences computed over the 5,303-paper corpus described in Section 2.2; the corpus is assembled by an explicit keyword and inclusion protocol (Table 1), and the LDA pipeline is standard and externally comparable (Table 3). A literature review describing its own corpus is not circular unless the target claim is definitionally identical to the selection criterion; here the selection criterion is 'conceptual modeling' and the target claims concern relative topic prevalence, which is an independent measurement over that corpus. The paper also checks against an external benchmark (Härer and Fill [95], Table 10) and reports substantial similarity between the general corpus and two added sources (Table 8), providing points of contact outside the present paper's fitted values. The unreconciled counts between Table 2 (5,303) and Table 4 (4,345 considered, 3,173 full text) and the missing ER full texts before 2005 are transparency and coverage limitations, not evidence that a fitted parameter was renamed as a prediction. Self-citations such as [135], [139], and [140] appear in the future-research discussion, but they motivate research directions rather than serving as the deductive basis for the survey's findings; none is a uniqueness theorem or an ansatz smuggled in by citation. There is therefore no specific equation, fitted-target equivalence, or self-citation chain to exhibit, and the appropriate finding is no significant circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The survey's quantitative claims rest mainly on source selection and the unsupervised topic modeling choices. The LDA topic cap and yearly topic counts are the principal hand-set numbers. The corpus representativeness and the manual coding consistency are domain assumptions that are stated but not externally validated. No new entities are introduced.

free parameters (3)
  • Maximum number of LDA topics (cap) = 15
    Section 2.3 Step c: authors capped topics at 15; varying the cap changes topic granularity and the apparent term clusters.
  • Yearly LDA topic count k = 6 to 12 depending on year, selected by highest C_v coherence
    Section 2.3 Step d and Table 4: each year's topic model is fit with k chosen by coherence, with no stability or seed analysis reported.
  • Doc2Vec embedding hyperparameters = not reported
    Section 2.3 Step f: the language model used for term similarity is trained on 2,555 full papers, but vector size, window, and epochs are not given, so replication is incomplete.
assumptions (5)
  • domain assumption The 35 selected sources and keyword protocol produce a corpus representative of conceptual modeling research.
    Section 2.2; all prevalence and gap findings inherit this assumption.
  • domain assumption LDA with coherence-maximizing topic count yields interpretable topics for each year.
    Section 2.3; no external validation of topic labels beyond manual inspection.
  • domain assumption Manual screening by six coders consistently applied the stated inclusion protocol.
    Section 2.2.3; no inter-rater reliability or coder agreement metrics are reported.
  • standard math Similarity scores from pretrained sentence embeddings (stsb-roberta-large) support corpus comparisons.
    Section 3.3; sentence embeddings are standard tools, but the threshold reading of 0.784 as 'substantial similarity' is interpretive.
  • domain assumption Full-text availability after 2005 approximates the full corpus for topic inference.
    Section 3.1 and Table 4; before 2005 only 639 of 801 papers had full text, and ER full texts before 2005 were unavailable, so early-period topics rely on a smaller and potentially different sample.

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Cite this review

Pith. "Pith review of Conceptual Modeling: Topics, Themes, and Technology Trends." pith.science (2026). https://pith.science/paper/PPCX7ZY7

@misc{pith2026250513648,
  author       = {Pith},
  title        = {Pith review of: Conceptual Modeling: Topics, Themes, and Technology Trends},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PPCX7ZY7}},
  note         = {Machine review of arXiv:2505.13648}
}
read the original abstract

Conceptual modeling is an important part of information systems development and use that involves identifying and representing relevant aspects of reality. Although the past decades have experienced continuous digitalization of services and products that impact business and society, conceptual modeling efforts are still required to support new technologies as they emerge. This paper surveys research on conceptual modeling over the past five decades and shows how its topics and trends continue to evolve to accommodate emerging technologies, while remaining grounded in basic constructs. We survey over 5,300 papers that address conceptual modeling topics from the 1970s to the present, which are collected from 35 multidisciplinary journals and conferences, and use them as the basis from which to analyze the progression of conceptual modeling. The important role that conceptual modeling should play in our evolving digital world is discussed, and future research directions proposed.

Figures

Figures reproduced from arXiv: 2505.13648 by the authors.

Figure 1
Figure 1. Structured survey of research on conceptual modeling [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Coherence score by number of topics To select the optimal number of topics, we chose the topics that gave the highest coherence score. Document models with higher topic coherence are more interpretable (i.e., words in a coherent topic have higher mutual information, thus are assumed to be related). We capped the maximum number of topics at 15 and created a visual representation, as shown in [PITH_FULL_IMAGE:figures… view at source ↗
Figure 3
Figure 3. Visualization of Topic Models Legend: Marginal topic distribution: [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Language model adds context to terms in topics [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 5
Figure 5. Figure 5: Evolution and continued progression of conceptual modeling [PITH_FULL_IMAGE:figures/full_fig_p026_5.png]

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Pith tools

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