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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).
The analysis highlights Technology and Science as prominent areas in the source structure around Data & Knowledge Engineering.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around Data & Knowledge Engineering shows recurring relationship patterns in the source. For example, Data & Knowledge Engineering → DKENEW Another extracted example is Data & Knowledge Engineering → Computer science. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
journal data chen engineering elsevier knowledge monthly 1985 science impact factor 992 2020 peer-reviewed editor-in-chief citation academic area database systems
TTTA extracted 13 structured relationships around Data & Knowledge Engineering. Examples in this analysis include Data & Knowledge Engineering → CODEN → DKENEW and Data & Knowledge Engineering → Discipline → Computer science. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Data & Knowledge Engineering | CODEN | DKENEW | 1.00 | infobox |
| Data & Knowledge Engineering | Discipline | Computer science | 1.00 | infobox |
| Data & Knowledge Engineering | Edited by | P.P. Chen | 1.00 | infobox |
| Data & Knowledge Engineering | Frequency | Monthly | 1.00 | infobox |
| Data & Knowledge Engineering | History | 1985–present | 1.00 | infobox |
| Data & Knowledge Engineering | Impact factor | 1.992 (2020) | 1.00 | infobox |
| Data & Knowledge Engineering | ISO 4 | Data Knowl. Eng. | 1.00 | infobox |
| Data & Knowledge Engineering | ISSN | 0169-023X (print) 1872-6933 (web) | 1.00 | infobox |
| Data & Knowledge Engineering | Language | English | 1.00 | infobox |
| Data & Knowledge Engineering | LCCN | 90649274 | 1.00 | infobox |
| Data & Knowledge Engineering | OCLC no. | 630595125 | 1.00 | infobox |
| Data & Knowledge Engineering | Publisher | Elsevier | 1.00 | infobox |
| Data & Knowledge Engineering | is a | monthly peer-reviewed academic journal in the area of database systems and knowledge base systems | 0.90 | text |
The concept neighborhoods around Data & Knowledge Engineering bring nearby vocabulary together. In this analysis, examples include Knowledge, Monthly and Engineering. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Data & Knowledge Engineering, one of the stronger structural bridges in this analysis connects Data & Knowledge Engineering with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Data & Knowledge Engineering to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Technology & Science, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Data & Knowledge Engineering · EN edition · Analysis: TopicsToTalkAbout