| 1 | Knowledge engineering: Principles and methods | Data and Knowledge Engineering | 1998 | 1,145 |
| 2 | ST-DBSCAN: An algorithm for clustering spatial–temporal data | Data and Knowledge Engineering | 2007 | 783 |
| 3 | A k-mean clustering algorithm for mixed numeric and categorical data | Data and Knowledge Engineering | 2007 | 386 |
| 4 | Workflow mining: A survey of issues and approaches | Data and Knowledge Engineering | 2003 | 267 |
| 5 | A conceptual view on trajectories | Data and Knowledge Engineering | 2008 | 266 |
| 6 | Methodologies, tools and languages for building ontologies. Where is their meeting point? | Data and Knowledge Engineering | 2003 | 245 |
| 7 | Mining itemset utilities from transaction databases | Data and Knowledge Engineering | 2006 | 195 |
| 8 | Case handling: a new paradigm for business process support | Data and Knowledge Engineering | 2005 | 189 |
| 9 | IT support for healthcare processes – premises, challenges, perspectives | Data and Knowledge Engineering | 2007 | 189 |
| 10 | Investigating diversity of clustering methods: An empirical comparison | Data and Knowledge Engineering | 2007 | 171 |
| 11 | Change patterns and change support features – Enhancing flexibility in process-aware information systems | Data and Knowledge Engineering | 2008 | 169 |
| 12 | Isolated items discarding strategy for discovering high utility itemsets | Data and Knowledge Engineering | 2008 | 158 |
| 13 | How do practitioners use conceptual modeling in practice? | Data and Knowledge Engineering | 2006 | 156 |
| 14 | From humor recognition to irony detection: The figurative language of social media | Data and Knowledge Engineering | 2012 | 148 |
| 15 | Theoretical and practical issues in evaluating the quality of conceptual models: current state and future directions | Data and Knowledge Engineering | 2005 | 145 |
| 16 | Big data technologies and Management: What conceptual modeling can do | Data and Knowledge Engineering | 2017 | 140 |
| 17 | Frameworks for entity matching: A comparison | Data and Knowledge Engineering | 2010 | 138 |
| 18 | Computing iceberg concept lattices with Titanic | Data and Knowledge Engineering | 2002 | 135 |
| 19 | A semiotic metrics suite for assessing the quality of ontologies | Data and Knowledge Engineering | 2005 | 128 |
| 20 | The thematic and citation landscape of Data and Knowledge Engineering (1985–2007) | Data and Knowledge Engineering | 2008 | 126 |
| 21 | A data mining approach for location prediction in mobile environments | Data and Knowledge Engineering | 2005 | 124 |
| 22 | A semantic similarity metric combining features and intrinsic information content | Data and Knowledge Engineering | 2009 | 118 |
| 23 | Measuring semantic similarity between Gene Ontology terms | Data and Knowledge Engineering | 2007 | 116 |
| 24 | Mereotopology: A theory of parts and boundaries | Data and Knowledge Engineering | 1996 | 112 |
| 25 | Complexity and clarity in conceptual modeling: Comparison of mandatory and optional properties | Data and Knowledge Engineering | 2005 | 110 |
| 26 | Text document clustering based on frequent word meaning sequences | Data and Knowledge Engineering | 2008 | 110 |
| 27 | Knowledge discovery from imbalanced and noisy data | Data and Knowledge Engineering | 2009 | 105 |
| 28 | Online clustering of parallel data streams | Data and Knowledge Engineering | 2006 | 103 |
| 29 | Workflow evolution | Data and Knowledge Engineering | 1998 | 101 |
| 30 | Mining interesting knowledge from weblogs: a survey | Data and Knowledge Engineering | 2005 | 98 |
| 31 | Supporting ontological analysis of taxonomic relationships | Data and Knowledge Engineering | 2001 | 96 |
| 32 | Correctness criteria for dynamic changes in workflow systems––a survey | Data and Knowledge Engineering | 2004 | 96 |
| 33 | MMR: An algorithm for clustering categorical data using Rough Set Theory | Data and Knowledge Engineering | 2007 | 95 |
| 34 | The category concept: An extension to the entity-relationship model | Data and Knowledge Engineering | 1985 | 92 |
| 35 | Parts, wholes, and part-whole relations: The prospects of mereotopology | Data and Knowledge Engineering | 1996 | 92 |
| 36 | The refined process structure tree | Data and Knowledge Engineering | 2009 | 92 |
| 37 | Semantic integration of heterogeneous information sources | Data and Knowledge Engineering | 2001 | 90 |
| 38 | Dominance-based rough set approach to incomplete interval-valued information system | Data and Knowledge Engineering | 2009 | 90 |
| 39 | A UML profile for multidimensional modeling in data warehouses | Data and Knowledge Engineering | 2006 | 88 |
| 40 | Matching large ontologies: A divide-and-conquer approach | Data and Knowledge Engineering | 2008 | 86 |
| 41 | A link clustering based overlapping community detection algorithm | Data and Knowledge Engineering | 2013 | 86 |
| 42 | Reusing ontologies on the Semantic Web: A feasibility study | Data and Knowledge Engineering | 2009 | 85 |
| 43 | Advances in business process management | Data and Knowledge Engineering | 2004 | 84 |
| 44 | Detection and prediction of errors in EPCs of the SAP reference model | Data and Knowledge Engineering | 2008 | 84 |
| 45 | SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks | Data and Knowledge Engineering | 2000 | 83 |
| 46 | egoisst: a negotiation support system for electronic business-to-business negotiations in e-commerce | Data and Knowledge Engineering | 2003 | 83 |
| 47 | Analysis of Naive Bayes’ assumptions on software fault data: An empirical study | Data and Knowledge Engineering | 2009 | 83 |
| 48 | WB-index: A sum-of-squares based index for cluster validity | Data and Knowledge Engineering | 2014 | 83 |
| 49 | Artificial intelligence in digital twins—A systematic literature review | Data and Knowledge Engineering | 2024 | 80 |
| 50 | Learning multiple layers of knowledge representation for aspect based sentiment analysis | Data and Knowledge Engineering | 2018 | 79 |
