Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
This map shows the geographic impact of David Leake's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Leake with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Leake more than expected).
This network shows the impact of papers produced by David Leake. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Leake. The network helps show where David Leake may publish in the future.
Co-authorship network of co-authors of David Leake
This figure shows the co-authorship network connecting the top 25 collaborators of David Leake.
A scholar is included among the top collaborators of David Leake based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with David Leake. David Leake is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Leake, David, et al.. (2021). Supporting Case-Based Reasoning with Neural Networks: An Illustration for Case Adaptation..10 indexed citations
2.
Leake, David, et al.. (2017). Modelling Unsupervised Event Segmentation: Learning Event Boundaries from Prediction Errors.. Cognitive Science.2 indexed citations
3.
Leake, David, et al.. (2014). An Ensemble Approach to Adaptation-Guided Retrieval.. The Florida AI Research Society.1 indexed citations
4.
Roth–Berghofer, Thomas, Stefan Schulz, & David Leake. (2006). Modeling and Retrieval of Context: Second International Workshop, MRC 2005, Edinburgh, UK, July 31-August 1, 2005, Revised Selected Papers (Lecture Notes ... / Lecture Notes in Artificial Intelligence). Springer eBooks.1 indexed citations
5.
Leake, David & Thomas Reichherzer. (2006). Understanding the Role of Structure in Concept Maps. eScholarship (California Digital Library). 28(28).6 indexed citations
6.
Leake, David, et al.. (2005). Using Cases to Support Divergent Roles in Distributed Collaboration.. The Florida AI Research Society. 117–122.1 indexed citations
7.
Dey, Anind K., et al.. (2005). Modeling and using context : 5th International and Interdisciplinary Conference, CONTEXT 2005, Paris, France, July 5-8, 2005 : proceedings. Springer eBooks.2 indexed citations
Leake, David & Raja Sooriamurthi. (2003). Dispatching Cases versus Merging Case-Bases: When MCBR Matters. The Florida AI Research Society. 129–133.2 indexed citations
12.
Leake, David, Ana Gabriela Maguitman, & Thomas Reichherzer. (2003). Topic Extraction and Extension to Support Concept Mapping. The Florida AI Research Society. 325–329.12 indexed citations
13.
Leake, David, Ana Gabriela Maguitman, & Alberto J. Cañas. (2002). Assessing Conceptual Similarity to Support Concept Mapping. The Florida AI Research Society. 168–172.24 indexed citations
14.
Leake, David & Raja Sooriamurthi. (2002). Managing Multiple Case Bases: Dimensions and Issues. The Florida AI Research Society. 106–110.5 indexed citations
15.
Leake, David, et al.. (1999). Selecting Task-Relevant Sources for Just-in-Time Retrieval.3 indexed citations
16.
Wilson, David C., et al.. (1999). Constructing and Transforming CBR Implementations: Techniques for Corporate Memory Management. 9–18.2 indexed citations
17.
Plaza, Enric & David Leake. (1997). Case-based reasoning research and development : Second International Conference on Case-Based Reasoning, ICCBR-97, Providence, RI, USA, July 25-27, 1997 : proceedings. Springer eBooks.10 indexed citations
18.
Leake, David, et al.. (1997). Learning to integrate multiple knowledge sources for case-based reasoning. International Joint Conference on Artificial Intelligence. 246–251.13 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.