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REVIEW 5 major objections 6 minor 85 references

Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A five-guideline method grounded in entity-relationship modeling improves machine learning performance and traceability in two foster-care applications.

desk verdict CMML's guidelines are a sensible packaging of known data-prep practices, but the empirical case is compromised by unit-of-analysis changes, so the performance claim needs a redo on a common task. read the letter →

arxiv 2507.02922 v1 pith:FVRFKINW submitted 2025-06-25 cs.LG cs.HC

V.C. Storey , J. Parsons , A. Castellanos , M. Tremblay , R. Lukyanenko , W. Maass , A. Castillo This is my paper · ORCID
classification cs.LGcs.HC
keywords conceptualmodelingmachinelearningdatapreparationentity-relationshipmodelCMMLprocesstransparencyfeatureengineeringfostercare
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

The paper claims that the domain knowledge encoded in a conceptual model can be used to prepare training data, and that doing so yields machine learning models that are both more accurate and more transparent. It introduces CMML, a method with five guidelines built on the constructs of extended entity-relationship models (entities, attributes, relationships, cardinalities, and specialization). Applied to two real foster-care problems—predicting length of stay from structured placement records and detecting psychotropic-medication use from casework notes—the method reportedly improves RMSE, explained variance, and recall, while focus groups of data scientists judged the guidelines useful and traceable. If correct, the paper shows that a systematic, semantically grounded data-preparation step can improve ML outcomes without requiring more data.

What carries the argument

The carrying mechanism is CMML itself: an iterative data-preparation method whose five guidelines map EER constructs onto dataset transformations. Guideline 1 labels each feature with its entity of origin; Guideline 2 derives features from derived attributes; Guideline 3 restricts imputation to values that are applicable but unknown; Guideline 4 summarizes the many side of a one-to-many relationship into counts and numeric summaries to remove duplicate target rows; Guideline 5 builds separate training datasets for specialized subtypes. The argument is that preserving entity semantics during preparation prevents unit-of-analysis mismatches, spurious imputation, and duplication, while keeping lineage of every feature.

What would settle it

Rerun Case 1 holding the unit of analysis fixed: keep the number of placements per episode as a feature while predicting episode length at the episode level, and compare against TDS1; if the advantage disappears, the reported gain is an artifact of changing granularity. Also check whether models trained on DS0 after applying only a statistical aggregation baseline (e.g., group-by means without entity labeling) match TDS1 performance.

Watch

Extended reading notes

Core claim

The central claim is that applying CMML to a raw tabular dataset (DS0) produces one or more training datasets (TDSn) that support better-performing and more transparent ML models. The paper reports that in the structured case, models trained on entity-summarized data (TDS1) outperformed those on the original placement-level data across five algorithms, with an average 23.8% increase in explained variance and a 7.4% RMSE reduction, and that splitting by child-age subtypes improved AutoML results. In the unstructured case, aggregating home-visit notes to the child level and dropping multi-child homes improved recall significantly at no significant precision cost. The paper also claims the method improves process transparency, primarily supported by focus-group agreement.

Load-bearing premise

The comparison assumes that the original DS0 and each transformed TDSn are solving the same prediction task, so their error metrics can be directly compared; in Case 1 the target mean and standard deviation change between datasets, and in Case 2 a subset is dropped, which could confound any observed improvement.

Editorial extensions

If this is right

  • Structured tabular tasks where a target-bearing entity sits on the one side of a one-to-many relationship can be improved by entity summarization, without adding more data.
  • Missing-value handling should be driven by whether an attribute is optional versus unknown, which requires entity and subtype knowledge.
  • AutoML pipelines could take a conceptual model as an additional input to constrain preprocessing, reducing the usual tradeoff between performance and transparency.
  • In domains with meaningful subclasses, training a separate model per subtype can outperform a single combined model, at least in some settings.
  • Entity-labeled feature names provide a lineage trail that supports reproducibility, auditing, and regulatory review.

Reading between the lines

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

  • If the method generalizes, its main quantitative gains may come from aligning the unit of analysis with the target attribute rather than from the full conceptual-model semantics; a head-to-head test against a purely statistical group-by aggregation would separate these effects.
  • The transparency claim currently rests on focus-group testimony; an objective metric such as documentation completeness, feature-importance stability, or error traceability could put it on firmer ground.
  • Organizations without formal conceptual models could still benefit by automatically extracting entity-relationship structure from databases or from text using large language models, a direction the paper notes only as future work.
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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

5 major / 6 minor

Summary. The paper proposes Conceptual Modeling for Machine Learning (CMML), a five-guideline method that uses Extended Entity-Relationship (EER) model constructs to guide data preparation for supervised machine learning. The method is evaluated in two real-world foster care cases: predicting episode length (regression) and detecting psychotropic medication prescription from case notes (classification). Models trained on the original dataset (DS0) are compared with models trained on transformed datasets (TDSn) using RMSE, r2, recall, precision, and F-measure, with statistical tests reported. The paper also reports a focus-group study with 15 data scientists to assess applicability and transparency. The central claim is that applying CMML improves both ML model performance and process transparency.

Significance. If the empirical claims held, the paper would make a useful contribution to data-centric ML by providing an operational, guideline-based way to inject domain knowledge from conceptual models into data preparation, and it would strengthen the link between conceptual modeling and ML practice. The five guidelines are clearly described, the application to foster care is socially relevant, the focus-group protocol is detailed, and the authors explicitly acknowledge limitations of the method. However, the reported comparisons are confounded by changes in unit of analysis, target distribution, and deliberate data subsetting, so the current evidence does not support the central performance claim as stated.

major comments (5)
  1. [Sec. 4.1.1, Tables 3 and 4] The DS0 versus TDS1 comparison changes the unit of analysis and the target distribution: DS0 contains 25,462 placement rows with the episode-length target repeated per placement, while TDS1 contains 4,437 episode rows, and the target mean/SD changes from 704.78/414.89 to 440.47/340.70. RMSE and r2 are therefore not directly comparable across these datasets, because the prediction targets are defined on different units and different target variances. The claim that TDS1 'consistently outperformed' DS0 is not supported unless both datasets are re-evaluated on a common prediction unit, for example by aggregating placement-level features to the episode level and comparing episode-level predictions.
  2. [Sec. 4.1.1, Table 5] The Wilcoxon signed-rank comparison 'DS0 vs TDS1' lists a sample size of 12,438, which matches neither DS0 (25,462 rows) nor TDS1 (4,437 rows). More importantly, the pairing of prediction errors across rows that represent different units (placements versus episodes) is not described, and it is unclear how a paired test can be defined when the two datasets have different row sets. Without a valid paired design or an explanation of how the 12,438 pairs were formed, the statistical conclusion in the sentence following Table 5 ('we conclude that applying the CMML method can improve the performance of ML models') does not follow from the reported evidence.
  3. [Sec. 4.1.1, Tables 4 and 5] TDS2 and TDS3 are age-based subsets of DS0, not datasets produced by applying Guidelines 2 and 4, so comparing their combined performance with DS0 tests only Guideline 5 (specialization) on subpopulations. The text states that combining TDS2 and TDS3 yielded equivalent performance to DS0 for four of the five models, yet Table 5 reports a significant Wilcoxon result for the same combined comparison; this contradiction needs to be resolved. In addition, the heading 'Guideline 7' in Table 4 for the TDS2/TDS3 rows appears to be a typo for Guideline 5. The conclusion should not attribute any combined gain to the full CMML method when only one guideline is being tested and the aggregate gain appears limited to the AutoML model.
  4. [Sec. 4.1.2, Tables 6 and 7] In Case 2, the dataset TDS2-Multiple (multi-child homes) is dropped before the performance comparison, and DS0 is at the level of home-visit notes (1,545 rows) while TDS1-Single is at the level of individual children (852 rows). The reported recall improvement from 59.28 to 84 may therefore reflect the removal of harder multi-child notes or the change in unit of analysis rather than the effect of the CMML guidelines. A child-level evaluation that includes all notes, or an explicit analysis of the dropped TDS2-Multiple subset, is required before concluding that the guidelines improved classification performance.
  5. [Sec. 4.2] The claim that CMML improves process transparency is supported only by a focus-group study in which 15 data scientists recruited for the study were asked whether the guidelines are useful. This is an applicability and acceptance check, not a measurement of transparency, and it is partly self-referential because the same team that developed the guidelines also conducted the assessment. The conclusion in Section 5 that the results show 'consistent and robust evidence for the benefits of using CMML to improve process transparency' overstates what a qualitative applicability check with a small, non-independent sample can establish.
minor comments (6)
  1. [Sec. 4.1.1] The sentence 'Table 3 shows these results' appears to refer to Table 4, since Table 3 is a dataset description and Table 4 contains the RMSE and r2 results.
  2. [Sec. 4.1.1, Table 4] The heading 'Guideline 7' in the TDS2-Younger and TDS3-Older rows should be 'Guideline 5' to match the dataset description in Table 3.
  3. [Sec. 4.1.2] The phrase 'the F-measure difference was 5%, indicating that the application of our guideline improved performance, and the improvement was statistically significant [2]' cites reference [2] (Adomavicius et al.) for statistical significance, which is not a standard statistics reference; a statistics or methodology citation would be more appropriate.
  4. [Sec. 3.4.2] The text cites 'Ohno-Machado et al., 1998' but this reference is not included in the reference list; either add the full citation or remove the citation.
  5. [Throughout] There are several typographical errors, including 'no doubtly' in Section 2.1, 'iterativeley' in Section 2, and 'in iterativeley' in the same paragraph; a careful proofreading pass is needed.
  6. [Sec. 5] The discussion would benefit from a clearer separation between the evidence for the performance claim (which is currently confounded) and the evidence for the applicability/transparency claim (which is qualitative), so that readers can weigh the two contributions independently.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: CMML is a procedural method evaluated empirically, with no fitted parameter renamed as prediction and no load-bearing self-citation chain.

full rationale

The paper's central claim is empirical: applying the five CMML guidelines to prepare training data yields models with better measured performance and rated transparency. There is no mathematical derivation in which an output is equated to an input by construction. The guidelines are defined a priori from Extended Entity-Relationship constructs (entity types, attributes, relationships, generalization/specialization), and the evaluation compares models trained on the original DS0 against models trained on the transformed TDSn datasets using RMSE, r2, F1, and Wilcoxon tests. No fitted parameter is later renamed as a prediction, and no uniqueness theorem is imported from the authors' prior work. The paper cites several works by the same authors (e.g., Maass and Storey 2021; Maass et al. 2022; Storey et al. 2025), but these appear in motivational or future-work contexts and do not carry the empirical performance claim. The focus-group assessment is qualitative and partly self-referential because the authors propose the method and also solicit practitioner opinions about it, but this is a validity and independence limitation, not a circular derivation. Concerns about comparing different units of analysis in Case 1 (placement rows vs. episode rows) and dropping multi-child homes in Case 2 are legitimate threats to the evaluation's correctness, but they do not amount to the paper reducing its conclusions to its own assumptions by definition. Therefore, no specific circular step can be quoted, and the appropriate circularity score is 0.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The method introduces no new physical or conceptual entities. 'Entity summarization' is a label for standard aggregation, not a postulated entity. The main ledger entries are domain assumptions about the availability and fidelity of conceptual models, plus the hand-chosen age split and the questionable assumption that different data granularities are directly comparable.

free parameters (1)
  • Age split threshold for TDS2/TDS3 = 7 years
    Chosen by the authors as a 'well-accepted' child subclass boundary (Courtney et al., Kadushin and Martin) and used to build separate models. The performance comparison is not isolated from this hand-chosen threshold.
assumptions (4)
  • domain assumption An EER conceptual model of the domain is available
    Section 3.1 states this as a required input. Section 5 acknowledges that many real datasets, especially from data lakes or integrated sources, have no agreed conceptual model.
  • domain assumption The dataset contains a target attribute and sufficient relevant features
    Section 3.1 lists this as the second main assumption of CMML.
  • domain assumption The conceptual model accurately reflects current domain rules
    Section 5 notes that outdated or incomplete conceptual models can degrade ML performance, so the method's value depends on model fidelity.
  • ad hoc to paper Comparing model metrics across different data granularities is meaningful
    Central to the Case 1 evaluation. DS0 rows are placements and TDS1 rows are episodes, with different target means and standard deviations, yet RMSE and r2 are compared directly.

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Pith. "Pith review of Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability." pith.science (2026). https://pith.science/paper/FVRFKINW

@misc{pith2026250702922,
  author       = {Pith},
  title        = {Pith review of: Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FVRFKINW}},
  note         = {Machine review of arXiv:2507.02922}
}
read the original abstract

Machine learning enables the extraction of useful information from large, diverse datasets. However, despite many successful applications, machine learning continues to suffer from performance and transparency issues. These challenges can be partially attributed to the limited use of domain knowledge by machine learning models. This research proposes using the domain knowledge represented in conceptual models to improve the preparation of the data used to train machine learning models. We develop and demonstrate a method, called the Conceptual Modeling for Machine Learning (CMML), which is comprised of guidelines for data preparation in machine learning and based on conceptual modeling constructs and principles. To assess the impact of CMML on machine learning outcomes, we first applied it to two real-world problems to evaluate its impact on model performance. We then solicited an assessment by data scientists on the applicability of the method. These results demonstrate the value of CMML for improving machine learning outcomes.

Figures

Figures reproduced from arXiv: 2507.02922 by the authors.

Figure 1
Figure 1. Extended Entity-Relationship (EER) Constructs Conceptual [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Conceptual Modeling for Machine Learning 3.1 Assumptions CMML is based on two main assumptions. First, a conceptual model of the domain is available in the form of an extended entity-relationship (EER) diagram. Second, a dataset is available containing a target attribute and sufficient relevant features to construct a machine learning model. The following scenario, adapted from Khatri et al. [42], illustrates the de… view at source ↗
Figure 10
Figure 10. Feature importance after applying Guideline 1 – feature labeling 4.1.2 Case 2 – Prediction of Psychotropic Prescription (Classification) This second machine learning project aimed to help caseworkers identify children taking psychotropic medication. The organization previously used a random selection method to identify potential positive cases of psychotropic drug use. The organization faced new mandates requiring m… view at source ↗

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Reference graph

Works this paper leans on

85 extracted references · 77 canonical work pages

  1. [1]

    Adadi, A. and M. Berrada, Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE access, 2018. 6: p. 52138-52160

    2018

  2. [2]

    ACM Transactions on Information systems (TOIS), 2005

    Adomavicius, G., et al., Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information systems (TOIS), 2005. 23(1): p. 103-145

    2005

  3. [3]

    ACM Transactions on Information Systems (TOIS), 2020

    Ahmad, F., et al., A deep learning architecture for psychometric natural language processing. ACM Transactions on Information Systems (TOIS), 2020. 38(1): p. 1-29

    2020

  4. [4]

    Teredesai, and C

    Ahmad, M.A., A. Teredesai, and C. Eckert. Fairness, accountability, transparency in AI at scale: Lessons from national programs. in Proceedings of the 2020 conference on fairness, accountability, and transparency. 2020

    2020

  5. [5]

    Data & Knowledge Engineering, 2024

    Akoka, J., et al., Unraveling the foundations and the evolution of conceptual modeling—Intellectual structure, current themes, and trajectories. Data & Knowledge Engineering, 2024. 154: p. 102351

    2024

  6. [6]

    Brainwash: A data system for feature engineering

    Anderson, M.R., et al. Brainwash: A data system for feature engineering. in Cidr. 2013

    work page 2013

  7. [7]

    Jordan, and R

    Becker, M.A., N. Jordan, and R. Larsen, Predictors of successful permanency planning and length of stay in foster care: The role of race, diagnosis and place of residence. Children and Youth Services Review, 2007. 29(8): p. 1102-1113. Storey et al. 2025 Domain Knowledge in AI Data & Knowledge Engineering, 2025 p. 36

    work page 2007

  8. [8]

    Deep learning of representations for unsupervised and transfer learning

    Bengio, Y. Deep learning of representations for unsupervised and transfer learning. in Proceedings of ICML workshop on unsupervised and transfer learning. 2012. JMLR Workshop and Conference Proceedings

    work page 2012

Show all 85 references
  1. [9]

    Courville, and P

    Bengio, Y., A. Courville, and P. Vincent, Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 2013. 35(8): p. 1798-1828

    2013

  2. [10]

    INFORMS Journal on Computing, 1999

    Bhargava, H.K., Data mining by decomposition: Adaptive search for hypothesis generation. INFORMS Journal on Computing, 1999. 11(3): p. 239-247

    1999

  3. [11]

    Bishop, C.M. and N.M. Nasrabadi, Pattern Recognition and Machine Learning. Vol

  4. [12]

    IEEE transactions on neural networks and learning systems, 2022

    Borisov, V., et al., Deep neural networks and tabular data: A survey. IEEE transactions on neural networks and learning systems, 2022

    2022

  5. [13]

    Bray, D. and R. Wang, Three People-Centered Design Principles for Deep Learning. MIT Sloan Management Review, 2019

    2019

  6. [14]

    IEEE Signal Processing Magazine, 2017

    Bronstein, M.M., et al., Geometric deep learning: Going beyond euclidean data. IEEE Signal Processing Magazine, 2017. 34(4): p. 18-42

    2017

  7. [15]

    Software and Systems Modeling, 2020

    Bucchiarone, A., et al., Grand challenges in model-driven engineering: an analysis of the state of the research. Software and Systems Modeling, 2020. 19: p. 5-13

    2020

  8. [16]

    5th workshop on artificial intelligence and model-driven engineering (mde 2023)

    Burgueño, L., et al. 5th workshop on artificial intelligence and model-driven engineering (mde 2023). in 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). 2023. IEEE

    2023

  9. [17]

    Candel, A., et al., Deep learning with H2O. H2O. AI Inc, 2016: p. 1-21

    2016

  10. [18]

    Journal of the Association for Information Systems, 2020

    Castellanos, A., et al., Basic classes in conceptual modeling: theory and practical guidelines. Journal of the Association for Information Systems, 2020. 21(4): p. 3

    2020

  11. [19]

    Chahal, H. and H. Toner, Small data are also crucial for machine learning. Scientific American, 2021

    2021

  12. [20]

    Chazette, L. and K. Schneider, Explainability as a non-functional requirement: Challenges and recommendations. Requirements Engineering, 2020. 25(4): p. 493- 514

    2020

  13. [21]

    ACM transactions on database systems (TODS), 1976

    Chen, P.P.-S., The entity-relationship model—toward a unified view of data. ACM transactions on database systems (TODS), 1976. 1(1): p. 9-36

    1976

  14. [22]

    Data cleaning: Overview and emerging challenges

    Chu, X., et al. Data cleaning: Overview and emerging challenges. in Proceedings of the 2016 International Conference on Management of Data. 2016

    2016

  15. [23]

    Educational and Psychological Measurement, 1960

    Cohen, J., A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 1960. 20(1): p. 37-46

    1960

  16. [24]

    Kampik, and M

    Corea, C., T. Kampik, and M. Montali. Explainable DMN. in International Conference on Business Process Management. 2024. Springer

    2024

  17. [25]

    2020: Cambridge University Press

    Duboue, P., The art of feature engineering: essentials for machine learning. 2020: Cambridge University Press

    2020

  18. [26]

    Feldman, R. and J. Sanger, The text mining handbook: advanced approaches in analyzing unstructured data. 2007: Cambridge university press

    2007

  19. [27]

    Advances in neural information processing systems, 2015

    Feurer, M., et al., Efficient and robust automated machine learning. Advances in neural information processing systems, 2015. 28

    2015

  20. [28]

    Annals of Statistics, 2001: p

    Friedman, J.H., Greedy function approximation: a gradient boosting machine. Annals of Statistics, 2001: p. 1189-1232

    2001

  21. [29]

    2019: Walter de Gruyter GmbH & Co KG

    Ghavami, P., Big data analytics methods: analytics techniques in data mining, deep learning and natural language processing. 2019: Walter de Gruyter GmbH & Co KG

    2019

  22. [30]

    2016, MIT press

    Goodfellow, I., Deep learning. 2016, MIT press. Storey et al. 2025 Domain Knowledge in AI Data & Knowledge Engineering, 2025 p. 37

    2016

  23. [31]

    Rubachev, and A

    Gorishniy, Y., I. Rubachev, and A. Babenko, On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems, 2022. 35: p. 24991-25004

    2022

  24. [32]

    Guizzardi, G. and N. Guarino, Explanation, semantics, and ontology. Data & Knowledge Engineering, 2024. 153: p. 102325

    2024

  25. [33]

    Defense Advanced Research Projects Agency (DARPA), Tech

    Gunning, D., Broad agency announcement explainable artificial intelligence (XAI). Defense Advanced Research Projects Agency (DARPA), Tech. Rep., 2016

    2016

  26. [34]

    2009: Springer

    Hastie, T., The elements of Statistical learning: Data mining, inference, and prediction. 2009: Springer

    2009

  27. [35]

    Zhao, and X

    He, X., K. Zhao, and X. Chu, AutoML: A survey of the state-of-the-art. Knowledge- based systems, 2021. 212: p. 106622

    2021

  28. [36]

    Nature, 2019

    Heaven, D., Why deep-learning AIs are so easy to fool. Nature, 2019. 574(7777): p. 163-166

    2019

  29. [37]

    2018, American Association for the Advancement of Science

    Hutson, M., Artificial intelligence faces reproducibility crisis. 2018, American Association for the Advancement of Science

    2018

  30. [38]

    Forbes, 2018

    Jones, M., How do we address the reproducibility crisis in Artificial Intelligence. Forbes, 2018

    2018

  31. [39]

    Vrbsky, and S

    Jukic, N., S. Vrbsky, and S. Nestorov, Database systems: Introduction to databases and data warehouses. 2016: Prospect press

    2016

  32. [40]

    Advances in Neural Information Processing Systems, 2017

    Ke, G., et al., Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 2017. 30

    2017

  33. [41]

    Kenett, R.S. and G. Shmueli, Information quality: The potential of data and analytics to generate knowledge. 2016: John Wiley & Sons

    2016

  34. [42]

    Information Systems Research, 2006

    Khatri, V., et al., Understanding conceptual schemas: Exploring the role of application and IS domain knowledge. Information Systems Research, 2006. 17(1): p. 81-99

    2006

  35. [43]

    Auto-encoding variational bayes

    Kingma, D.P. Auto-encoding variational bayes. in arXiv preprint arXiv:1312.6114. Presented at ICLR 2014. 2014

    2014 arXiv

  36. [44]

    Kipf, T.N. and M. Welling. Semi-supervised classification with graph convolutional networks. in ICLR. 2017

    2017

  37. [45]

    MIT Technology Review

    Knight, W., DARPA is funding projects that will try to open up AI’s black boxes. MIT Technology Review. Available at: https://www. technologyreview. com/2017/04/13/152590/thefinancial-world-wants-to-open-ais-black-boxes/. Accessed on Sept, 2017. 25: p. 2020

    2017

  38. [46]

    2010: Prentice Hall Upper Saddle River, NJ

    Kroenke, D.M., et al., Database Concepts. 2010: Prentice Hall Upper Saddle River, NJ

    2010

  39. [47]

    2000: Sage Publications

    Krueger, R., A practical guide for applied research. 2000: Sage Publications

    2000

  40. [48]

    Kung, C. and A. Soelvberg. Activity modeling and behavior modeling. in Proc. of the IFIP WG 8.1 working conference on Information systems design methodologies: improving the practice. 1986

    1986

  41. [49]

    Larsen, K.R. and D.S. Becker, Automated machine learning for business. 2021: Oxford University Press

    2021

  42. [50]

    Bengio, and G

    LeCun, Y., Y. Bengio, and G. Hinton, Deep learning. nature, 2015. 521(7553): p. 436-444

    2015

  43. [51]

    Using conceptual modeling to support machine learning

    Lukyanenko, R., et al. Using conceptual modeling to support machine learning. in Information Systems Engineering in Responsible Information Systems: CAiSE Forum 2019, Rome, Italy, June 3–7, 2019, Proceedings 31. 2019. Springer. Storey et al. 2025 Domain Knowledge in AI Data & ...

    2019

  44. [52]

    AI Explainability: A conceptual model embedding

    Maass, W., et al. AI Explainability: A conceptual model embedding. in International Conference on Information Systems. 2022

    2022

  45. [53]

    ConceptSuperimposition: Using conceptual modeling method for Explainable AI

    Maass, W., et al. ConceptSuperimposition: Using conceptual modeling method for Explainable AI. in AAAI Spring Symposium: MAKE. 2022

    2022

  46. [54]

    Maass, W. and V.C. Storey, Pairing conceptual modeling with machine learning. Data & Knowledge Engineering, 2021. 134: p. 101909

    2021

  47. [55]

    3(GROUP): p

    Mao, Y., et al., How data scientistswork together with domain experts in scientific collaborations: To find the right answer or to ask the right question? Proceedings of the ACM on Human-Computer Interaction, 2019. 3(GROUP): p. 1-23

    2019

  48. [56]

    arXiv preprint arXiv:1301.3781, 2013

    Mikolov, T., et al., Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013. 3781

    2013 arXiv

  49. [57]

    Thousand Oaks

    Miles, M.B., Qualitative data analysis: An expanded sourcebook. Thousand Oaks. 1994

    1994

  50. [58]

    Conceptual modelling, databases, and CASE: An integrated view of information system development, 1992: p

    Mylopoulos, J., Conceptual modelling and Telos. Conceptual modelling, databases, and CASE: An integrated view of information system development, 1992: p. 49-68

    1992

  51. [59]

    Nalchigar, S. and E. Yu, Business-driven data analytics: A conceptual modeling framework. Data & Knowledge Engineering, 2018. 117: p. 359-372

    2018

  52. [60]

    Learning Feature Engineering for Classification

    Nargesian, F., et al. Learning Feature Engineering for Classification. in IJCAI. 2017

    2017

  53. [61]

    Runtime Monitoring of Human-Centric Requirements in Machine Learning Components: A Model-Driven Engineering Approach

    Naveed, H. Runtime Monitoring of Human-Centric Requirements in Machine Learning Components: A Model-Driven Engineering Approach. in 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). 2023. IEEE

    2023

  54. [62]

    Journal of machine learning research, 2011

    Pedregosa, F., Scikit‐learn: Machine learning in python Fabian. Journal of machine learning research, 2011. 12: p. 2825

    2011

  55. [63]

    Journal of Machine Learning Research, 2011

    Pedregosa, F., et al., Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 2011. 12: p. 2825-2830

    2011

  56. [64]

    Forbes, 2021

    Press, G., Andrew Ng launches a campaign for data-centric AI. Forbes, 2021. 16(June): p. 2021

    2021

  57. [65]

    Ieee Access, 2020

    Reddy, G.T., et al., Analysis of dimensionality reduction techniques on big data. Ieee Access, 2020. 8: p. 54776-54788

    2020

  58. [66]

    Why should i trust you?

    Ribeiro, M.T., S. Singh, and C. Guestrin. " Why should i trust you?" Explaining the predictions of any classifier. in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016

    2016

  59. [67]

    Everyone wants to do the model work, not the data work

    Sambasivan, N., et al. “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI. in proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 2021

    2021

  60. [68]

    Khatri, and V

    Samuel, B.M., V. Khatri, and V. Ramesh, Exploring the effects of extensional versus intensional representations on domain understanding. MIS Quarterly, 2018. 42(4): p. 1187-A16

    2018

  61. [69]

    arXiv preprint arXiv:2112.07844, 2021

    Sanyal, A., et al., Fix your models by fixing your datasets. arXiv preprint arXiv:2112.07844, 2021

    2021 arXiv

  62. [70]

    Proceedings of the IEEE, 2021

    Schölkopf, B., et al., Toward causal representation learning. Proceedings of the IEEE, 2021. 109(5): p. 612-634

    2021

  63. [71]

    Shafique, U. and H. Qaiser, A comparative study of data mining process models (KDD, CRISP-DM and SEMMA). International Journal of Innovation and Scientific Research, 2014. 12(1): p. 217-222

    2014

  64. [72]

    Shmueli, G. and O.R. Koppius, Predictive analytics in information systems research. MIS quarterly, 2011: p. 553-572. Storey et al. 2025 Domain Knowledge in AI Data & Knowledge Engineering, 2025 p. 39

    2011

  65. [73]

    Shwartz-Ziv, R. and A. Armon, Tabular data: Deep learning is not all you need. Information Fusion, 2022. 81: p. 84-90

    2022

  66. [74]

    Silge, J. and D. Roginson, Text mining with R: A tidy approach. 2017: O'Reilly Media, Inc

    2017

  67. [75]

    nature, 2016

    Silver, D., et al., Mastering the game of Go with deep neural networks and tree search. nature, 2016. 529(7587): p. 484-489

    2016

  68. [76]

    2020, NYC Data Science Academy

    Statnikov, A., Growing as a Data Scientist and the Role of Communication. 2020, NYC Data Science Academy

    2020

  69. [77]

    Preprint, 2025

    Storey, V.C., et al., Large language models for conceptual modeling: Assessment and application potential. Preprint, 2025

    2025

  70. [78]

    Yang, and J.P

    Teorey, T.J., D. Yang, and J.P. Fry, A logical design methodology for relational databases using the extended entity-relationship model. ACM Computing Surveys (CSUR), 1986. 18(2): p. 197-222

    1986

  71. [79]

    Dutta, and D

    Tremblay, M.C., K. Dutta, and D. Vandermeer, Using data mining techniques to discover bias patterns in missing data. Journal of Data and Information Quality (JDIQ), 2010. 2(1): p. 1-19

    2010

  72. [80]

    Hevner, and D.J

    Tremblay, M.C., A.R. Hevner, and D.J. Berndt, Focus groups for artifact refinement and evaluation in design research. Communications of the association for information systems, 2010. 26(1): p. 27

    2010

  73. [81]

    Automated machine learning in practice: state of the art and recent results

    Tuggener, L., et al. Automated machine learning in practice: state of the art and recent results. in 2019 6th Swiss Conference on Data Science (SDS). 2019. IEEE

    2019

  74. [82]

    Data & Knowledge Engineering, 2008

    van de Riet, R.P., Twenty-five years of Mokum: For 25 years of data and knowledge engineering: Correctness by design in relation to MDE and correct protocols in cyberspace. Data & Knowledge Engineering, 2008. 67(2): p. 293-329

    2008

  75. [83]

    Wand, Y. and R. Weber, On the deep structure of information systems. Information Systems Journal, 1995. 5(3): p. 203-223

    1995

  76. [84]

    IEEE transactions on neural networks and learning systems, 2020

    Wu, Z., et al., A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 2020. 32(1): p. 4-24

    2020

  77. [85]

    O'Reilly Media, Inc

    Zheng, A. and A. Casari, Feature engineering for machine learning: principles and techniques for data scientists. 2018: " O'Reilly Media, Inc."

    2018

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.