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Data & Knowledge Engineering

journal

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General referenceInterpretive dossier study · Crown Archives visual atlas
Record originEnglish Wikipedia
Text licenseCC BY-SA 4.0
Source revisionFeb 21, 2026
Entity authorityQ5227237 ↗
Source-derived summary

Data & Knowledge Engineering is a monthly peer-reviewed academic journal in the area of database systems and knowledge base systems. It is published by Elsevier and was established in 1985. The editor-in-chief is P.P. Chen (Louisiana State University).

Abstracting and indexing

The journal is abstracted and indexed in Current Contents/Engineering, Computing & Technology, Ei Compendex, Inspec, Science Citation Index Expanded, Scopus, and Zentralblatt MATH. According to the Journal Citation Reports, the journal has a 2020 impact factor of 1.992.

Editorial summary

“Data & Knowledge Engineering” enters the record as journal. Crown Archives preserves that source wording while asking what Data, Knowledge and Engineering can confirm, complicate or overturn.

Editorial reviewA concise reference frame for defining the subject, testing terminology and identifying the institution closest to the evidence. The current lead gives the account dated anchors—1985, 2020—that can be checked directly. The linked authority record independently contributes the date 1985-01-01. Its strongest next move is a source search built around Data, Knowledge and Engineering.
Editorial analysis

Why this record matters

“Data & Knowledge Engineering” is worth following because a concise public description often conceals a longer documentary argument. Here, Data, Knowledge and Engineering provides the most credible route into that argument.

Evidence profile

Vocabulary and entity names are the principal evidence signals here, because they determine the precision of every later search. The source revision retrieved here is dated Feb 21, 2026. The linked authority identifier is Q5227237. 3 of 3 selected statements include explicit references; 1 carry qualifiers and 0 use preferred rank. The first chronological checks are 1985 and 2020.

Critical limits

The absence of detail may reflect summary conventions rather than a lack of surviving documentation. The lead is largely declarative, so disagreement and counter-evidence require a deliberate search beyond the opening account. Authority statements aid reconciliation but still require their own references, qualifiers and ranks to be checked.

How to read it

Use the entry as an orientation point, then follow its citations and revision history. Names, dates and institutional relationships should be checked against the original record.

Best used for
  • Subject orientation
  • Search vocabulary
  • Locating named sources
Verify next

The closest primary source, responsible institution and strongest cited specialist reference.

Three-step research path

  1. Establish the record: confirm the title “Data & Knowledge Engineering”, its source revision and the description used here.
  2. Expand the search: follow Data & Knowledge Engineering primary sources, Data & Knowledge Engineering archive and Data research across catalogues and specialist indexes.
  3. Test the account: compare the strongest cited source with the responsible institution’s current record and note any disagreement.

Questions for further research

  1. Which source most directly establishes the central claim about “Data & Knowledge Engineering”?
  2. Which cited source is closest to the event, object or claim?
  3. Which institution is responsible for the underlying evidence?
Subject index

Search terms from this dossier

Source & attribution

This entry incorporates text from “Data & Knowledge Engineering” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.