Anchoring Data Quality Dimensions in Ontological Foundations
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article Free AccessAnchoring data quality dimensions in ontological foundations Authors: Yair Wand University of British Columbia, Vancouver University of British Columbia, VancouverView Profile , Richard Y. Wang MIT Sloan School of Management MIT Sloan School of ManagementView Profile Authors Info & Claims Communications of the ACMVolume 39Issue 11Nov. 1996 pp 86–95https://doi.org/10.1145/240455.240479Published:01 November 1996Publication History 738citation9,629DownloadsMetricsTotal Citations738Total Downloads9,629Last 12 Months1,300Last 6 weeks160 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
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A systematic review of 151 tools found that most prepare 'data quality for AI,' while few exploit 'AI for data quality,' exposing a gap between current market offerings and the need for intelligent, automated DQM.
“However, a systematic review of 151 tools found that most prepare 'data quality for AI,' while few exploit 'AI for data quality' [108], exposing a gap between current market offerings and the need for intelligent, automated DQM.”
From Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles (2025) · cites this paper as [108]
The Ontological Foundations and Concluding Remarks sections define data quality dimensions rather than review tools. Searches for the 151-tool review, “data quality for AI,” “AI for data quality,” and intelligent automated DQM found no passage in our copy addressing those claims. (LLM, conf=0.78)
Experimental — accuracy not yet measuredFor black-box models, XAI uses post hoc explanation approaches that explain how opaque models such as neural networks reach their decisions.
“Faced with black-box models, XAI adopts post hoc approaches to explanation, 112 which build techniques to explain how opaque models such as neural networks arrive at their decisions.”
From Towards eXplainable Artificial Intelligence (XAI) in Tax Law: The Need for a Minimum Legal Standard (2022) · cites this paper as [112]
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References 30 · Cited by 1,479
Research works printed but not linked: 5
- [1]1. Agmon, N., and Ahituv, N. Assessing Data Reliability in an Infor- mation Systems. J. of Manage. Info. Syst. 4, 2 (1987), pp. 34-44.
- [6]6. Bunge, M. Ontology I: The furniture of the world. Treaties on Basic Philosophy, Vol. 3-4. Reidel Publishing, Boston, Mass. 1977, 1979.
- [9]9. Feltham, G. The value of information. Account. Rev. 43, 4 (1968), pp. 684-696.
- [10]10. Firth, C.P., and Wang, R.Y. Data Quality Systems: Evaluation and Implementation. Cambridge Market Intelligence, London. 1996.
- [17]17. Shannon, C.E., and Weaver, W. The Mathematical Theory of Com- munication. University of Illinois Press, Urbana, Ill. 1949.
- Authors
- Wand, Y., & Wang, R. Y.
- Affiliations
- University of British Columbia (CA)
- Massachusetts Institute of Technology (US)
- ORCID
- Wang, Richard Y.
- Venue
- Communications of the ACM
- Published
- 1996-11
- Volume
- 39
- Issue
- 11
- Pages
- 86-95
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2034334856
- Cited by
- 1,479
- References
- 30
- Citations by year
- 2026: 30 · 2025: 38 · 2024: 42 · 2023: 45 · 2022: 48 · 2021: 55 · 2020: 48 · 2019: 66 · 2018: 72 · 2017: 59 · 2016: 59 · 2015: 87 · 2014: 71 · 2013: 71 · 2012: 88
- FWCI
- 31.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (acceptedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Knowledge Management and Technology
- Keywords
- data quality, ontological foundations, ontology (information science), data quality dimensions, knowledge representation
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