Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:49:38.986739Z
Paper Citation Record · LEDGER
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability
As of 20 August 2026, this Paper Citation Record lists 85 of 85 outbound references and 0 inbound Pith citation observations for arXiv:2507.02922.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
85 of 85 outbound references displayed
- verified exact0
- verified fuzzy58
- unresolved26
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation cc132e5a-9288-409e-ab10-cdac2e0519c2 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:49:31.892001Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8ecb8b57-34b2-4495-a01a-7e6445ecab12 · outbound
This paper cites ACM Transactions on Information systems (TOIS), 2005.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability ACM Transactions on Information systems (TOIS), 2005
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:49:31.961507Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9be2695a-5869-465a-859a-36701c23772e · outbound
This paper cites ACM Transactions on Information Systems (TOIS), 2020.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability ACM Transactions on Information Systems (TOIS), 2020
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:49:32.036793Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 37ff5e5f-7f7e-42f7-97ea-4bc3d9b57e7b · outbound
This paper cites Teredesai, and C.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Teredesai, and C
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:49:32.128395Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3635db5f-e3b4-4eab-bc9f-a3ab0f7021b4 · outbound
This paper cites Data & Knowledge Engineering, 2024.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Data & Knowledge Engineering, 2024
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:49:32.219193Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 081fa88e-6a5c-4cd2-9cd7-042c70a4a427 · outbound
This paper cites Brainwash: A data system for feature engineering.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Brainwash: A data system for feature engineering
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.894919Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 40d5a859-92ef-4bf4-8b60-ff38d8a7ca17 · outbound
This paper cites Jordan, and R.
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.884354Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c7b461ac-d1b6-4e9a-b0af-1994848a557a · outbound
This paper cites Deep learning of representations for unsupervised and transfer learning.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Deep learning of representations for unsupervised and transfer learning
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.872856Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 8c9ccf80-a54e-4a52-91fc-b74b9bfac5c6 · outbound
This paper cites Courville, and P.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Courville, and P
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.862490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 0dbd1bb7-dd02-4901-95e1-bf4f7d008aa3 · outbound
This paper cites INFORMS Journal on Computing, 1999.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability INFORMS Journal on Computing, 1999
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.851819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 34aabbd6-8ca7-4d35-b689-08ab75466672 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 11
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.840559Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4a58bfc9-8b0b-4580-8502-9b35999adffa · outbound
This paper cites IEEE transactions on neural networks and learning systems, 2022.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability IEEE transactions on neural networks and learning systems, 2022
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.829439Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f56a507e-654c-4608-83d5-ae4c8905e5f7 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 13
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.817675Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a15542b4-afce-4c3b-8045-f73c68a3962c · outbound
This paper cites IEEE Signal Processing Magazine, 2017.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability IEEE Signal Processing Magazine, 2017
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.806505Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c0a35b76-ac8b-49df-8c26-7016955a1498 · outbound
This paper cites Software and Systems Modeling, 2020.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Software and Systems Modeling, 2020
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.795148Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c51f877d-e75a-4f8b-9042-55432e160b60 · outbound
This paper cites 5th workshop on artificial intelligence and model-driven engineering (mde 2023).
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability 5th workshop on artificial intelligence and model-driven engineering (mde 2023)
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.784471Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9c8c335a-127c-45f7-810d-90209882760c · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 17
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.772959Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9149ef25-fa3f-44dc-9891-a51886c35c9e · outbound
This paper cites Journal of the Association for Information Systems, 2020.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Journal of the Association for Information Systems, 2020
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.761177Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 55b873e3-2cfb-4e2b-9325-1a5aa828aa17 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 19
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.750597Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 885d1358-4544-477f-8beb-86248872bec5 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 20
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.739325Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 15241689-eec8-4f42-bde2-3b7bcc00fec2 · outbound
This paper cites ACM transactions on database systems (TODS), 1976.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability ACM transactions on database systems (TODS), 1976
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.728724Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f26761e1-2269-456a-982f-02dc140e09b6 · outbound
This paper cites Data cleaning: Overview and emerging challenges.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Data cleaning: Overview and emerging challenges
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.718497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3d37e387-e400-4824-87a5-6a59642d4fd8 · outbound
This paper cites Educational and Psychological Measurement, 1960.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Educational and Psychological Measurement, 1960
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.707650Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation eac14b00-2314-4f38-af4b-52d134fb3b4a · outbound
This paper cites Kampik, and M.
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.697641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation fdd62a66-19fc-4338-be98-33f4550ef24f · outbound
This paper cites 2020: Cambridge University Press.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability 2020: Cambridge University Press
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.687901Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation fd9ffc62-da74-4f79-aae1-6ec1988b1fda · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 26
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.678618Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation ff3d623c-01bc-461d-a2d8-0a709014e3c6 · outbound
This paper cites Advances in neural information processing systems, 2015.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Advances in neural information processing systems, 2015
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.669852Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 8c553786-20cd-48ee-b431-86fa4bd18cc2 · outbound
This paper cites Annals of Statistics, 2001: p.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Annals of Statistics, 2001: p
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.660306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6369f241-b695-4c6e-8fca-6f51c6bdc5ae · outbound
This paper cites 2019: Walter de Gruyter GmbH & Co KG.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability 2019: Walter de Gruyter GmbH & Co KG
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.651044Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 64fe07d5-7898-440b-97ad-93c630f789b3 · outbound
This paper cites 2016, MIT press.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability 2016, MIT press
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.642270Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9cab8acd-9c25-4bd7-8a15-4058f6ac183d · outbound
This paper cites Rubachev, and A.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Rubachev, and A
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.632252Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 8692b22d-9ac0-491b-9329-2edd8fce5973 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 32
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.622951Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2bd37ca9-ed4d-4d66-ac9d-c9144f6dc9e0 · outbound
This paper cites Defense Advanced Research Projects Agency (DARPA), Tech.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Defense Advanced Research Projects Agency (DARPA), Tech
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.612848Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4a7c88a3-75e5-40eb-8742-3e2714e740a4 · outbound
This paper cites 2009: Springer.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability 2009: Springer
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.603511Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2da75f9e-8200-4abe-965f-584d3a3cf3eb · outbound
This paper cites Zhao, and X.
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.594716Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4dcef4cd-2b34-4624-8772-e2035bf778cc · outbound
This paper cites Nature, 2019.
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.585513Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation ca3d1a1c-c058-438f-8dcf-16c233cad1b6 · outbound
This paper cites 2018, American Association for the Advancement of Science.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability 2018, American Association for the Advancement of Science
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.576639Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e531315f-7f14-4cdc-a0e3-b6d8db226fe7 · outbound
This paper cites Forbes, 2018.
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.568117Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d6481526-498a-4280-bc17-375836bacf52 · outbound
This paper cites Vrbsky, and S.
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.558958Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3495ca46-d511-4c07-a8c9-e192be1182a3 · outbound
This paper cites Advances in Neural Information Processing Systems, 2017.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Advances in Neural Information Processing Systems, 2017
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.550196Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 81f0b900-8473-43bd-bc6c-9497ede78888 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 41
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.540888Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 7a4e1f02-80e3-4490-8967-e2addcc26b18 · outbound
This paper cites Information Systems Research, 2006.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Information Systems Research, 2006
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.531488Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a976d86f-b619-4b25-aada-9e286c3fc5ff · outbound
This paper cites Auto-Encoding Variational Bayes.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Auto-Encoding Variational Bayes
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:49:35.946781Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7ca2e4c4-949a-47db-b1c7-37b7166dd372 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 44
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.522513Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f1a2f4c5-5315-4386-96a7-94cb0b7198df · outbound
This paper cites MIT Technology Review.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability MIT Technology Review
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.513127Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 494f2499-87b1-432b-8fd4-ae223a899844 · outbound
This paper cites 2010: Prentice Hall Upper Saddle River, NJ.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability 2010: Prentice Hall Upper Saddle River, NJ
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.504246Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 16fa6fd1-5cb3-4fdb-938c-ebd1da112fc1 · outbound
This paper cites 2000: Sage Publications.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability 2000: Sage Publications
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.495541Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2db5aa11-12bf-409a-8589-fd5d6a0aac68 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 48
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.486919Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 661f41cc-15ba-4900-af0e-2d08732f2f7a · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 49
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.478479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2372b577-8328-4f52-91df-022d4257752b · outbound
This paper cites Bengio, and G.
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.469729Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e401f233-77b1-4395-8cb3-efe29c319fd7 · outbound
This paper cites Using conceptual modeling to support machine learning.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Using conceptual modeling to support machine learning
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.460794Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 0bfd823a-b7bf-4d4e-9ebf-481ee604a03b · outbound
This paper cites AI Explainability: A conceptual model embedding.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability AI Explainability: A conceptual model embedding
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.451345Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4db1f74b-04c4-48e4-800d-75e895f4a72f · outbound
This paper cites ConceptSuperimposition: Using conceptual modeling method for Explainable AI.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability ConceptSuperimposition: Using conceptual modeling method for Explainable AI
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.441625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e70bc94d-b7f3-43f9-80e3-a1b0c2c518f1 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 54
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.431885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6757bd60-06a9-4200-8e10-2d43666c2af3 · outbound
This paper cites 3(GROUP): p.
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.422039Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2aef79e3-1020-4fe7-b3ac-6146686c8b34 · outbound
This paper cites Efficient Estimation of Word Representations in Vector Space.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Efficient Estimation of Word Representations in Vector Space
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:49:36.902488Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1dbaaf80-36c4-478f-a6c8-520ea0eab80a · outbound
This paper cites Thousand Oaks.
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.412175Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5142e7d1-0a16-4455-9b91-6e10065691ed · outbound
This paper cites Conceptual modelling, databases, and CASE: An integrated view of information system development, 1992: p.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Conceptual modelling, databases, and CASE: An integrated view of information system development, 1992: p
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.402385Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation af66b2f5-956d-403d-a2bf-ea4c44b32105 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 59
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.392189Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c6b2f875-201a-49f8-9c21-41a73ba243fe · outbound
This paper cites Learning Feature Engineering for Classification.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Learning Feature Engineering for Classification
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.380111Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 53fa163c-f00a-4641-a97b-2f38a2d6e6fe · outbound
This paper cites Runtime Monitoring of Human-Centric Requirements in Machine Learning Components: A Model-Driven Engineering Approach.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Runtime Monitoring of Human-Centric Requirements in Machine Learning Components: A Model-Driven Engineering Approach
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.370364Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2ded448a-4a40-4468-b637-7b2055ddbec7 · outbound
This paper cites Journal of machine learning research, 2011.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Journal of machine learning research, 2011
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.361133Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation cb1e733c-dd84-4503-b558-6920a6ac2e4e · outbound
This paper cites Journal of Machine Learning Research, 2011.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Journal of Machine Learning Research, 2011
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.352271Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9929a52d-eb19-48af-ac48-0cde70a06c0b · outbound
This paper cites Forbes, 2021.
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.341945Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d056dd2e-588f-48d5-a63c-4ad414f468ad · outbound
This paper cites Ieee Access, 2020.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Ieee Access, 2020
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.332058Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2d7c050e-a4d1-479d-8446-d52b7ecf076c · outbound
This paper cites Why should i trust you?.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Why should i trust you?
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.320866Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 8c6b1ac6-9efa-4538-a049-dbbe7bfefd8a · outbound
This paper cites Everyone wants to do the model work, not the data work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Everyone wants to do the model work, not the data work
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.309813Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation faadcb26-5052-448a-a15b-a097733bb3e6 · outbound
This paper cites Khatri, and V.
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.299025Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4081a1ba-af87-4c8c-b881-e0e77de144c7 · outbound
This paper cites Fix your Models by Fixing your Datasets.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Fix your Models by Fixing your Datasets
Reference 69
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T22:49:39.155775Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5e928fbc-7c6f-4960-bafd-901844cd6f41 · outbound
This paper cites Proceedings of the IEEE, 2021.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Proceedings of the IEEE, 2021
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:42.287923Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dbd9a957-7962-461e-8c56-4faf6eb94885 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 71
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.218718Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation edb18ac0-eb1a-442e-8194-4a98e3357c74 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 72
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:42.045586Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d59d5317-11bc-444c-a43f-5c551d0d1206 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 73
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:41.948914Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 0fa7ae5d-0584-4eaa-933f-941479d46469 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 74
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:41.674419Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 649057ff-1f8b-46bf-a81e-8e42d8d1758a · outbound
This paper cites nature, 2016.
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:41.344727Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 40dbb439-1489-4219-80e4-6b50bcb6b80c · outbound
This paper cites 2020, NYC Data Science Academy.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability 2020, NYC Data Science Academy
Reference 76
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:41.038576Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c9a9e90e-fc8c-4eb8-b657-52bfeb7f019a · outbound
This paper cites Preprint, 2025.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Preprint, 2025
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:40.761326Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 38923272-35c3-4a29-bca3-755c4c1f353d · outbound
This paper cites Yang, and J.P.
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:40.451746Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e96f0107-d897-4f28-91ee-8d099ae8b5c7 · outbound
This paper cites Dutta, and D.
Reference 79
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:40.237852Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 644acce5-2d5a-40a8-bfde-10662e532692 · outbound
This paper cites Hevner, and D.J.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Hevner, and D.J
Reference 80
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:40.033844Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d45fb3a0-9ce0-488a-97b8-fedf21ec2fda · outbound
This paper cites Automated machine learning in practice: state of the art and recent results.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Automated machine learning in practice: state of the art and recent results
Reference 81
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:39.847658Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 21c7b88b-449d-4cb8-86cd-f63e627a842d · outbound
This paper cites Data & Knowledge Engineering, 2008.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Data & Knowledge Engineering, 2008
Reference 82
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:39.474144Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 02293a7a-7341-4084-9068-c01171263457 · outbound
This paper cites an unresolved cited work.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability Unresolved cited work
Reference 83
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T22:49:39.386749Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation cdcfbbab-a49b-4d84-b12c-22b5618d31e6 · outbound
This paper cites IEEE transactions on neural networks and learning systems, 2020.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability IEEE transactions on neural networks and learning systems, 2020
Reference 84
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:49:38.925511Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 74e29ad8-5e0e-4ffb-816c-f0c39ea6e9bb · outbound
This paper cites O'Reilly Media, Inc.
Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability O'Reilly Media, Inc
Reference 85
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:49:39.274757Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Pith citing papers
No inbound Pith citation observations are available.