David Buttler

1.6k total citations · 1 hit paper
40 papers, 980 citations indexed

About

David Buttler is a scholar working on Information Systems, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, David Buttler has authored 40 papers receiving a total of 980 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Information Systems, 21 papers in Artificial Intelligence and 16 papers in Computer Networks and Communications. Recurrent topics in David Buttler's work include Web Data Mining and Analysis (21 papers), Advanced Database Systems and Queries (7 papers) and Advanced Text Analysis Techniques (7 papers). David Buttler is often cited by papers focused on Web Data Mining and Analysis (21 papers), Advanced Database Systems and Queries (7 papers) and Advanced Text Analysis Techniques (7 papers). David Buttler collaborates with scholars based in United States, South Korea and France. David Buttler's co-authors include David Andrzejewski, Calton Pu, Keith Stevens, Philip Kegelmeyer, Ling Liu, Wei Han, Terence Critchlow, Wei Tang, David Hysom and James Caverlee and has published in prestigious journals such as Journal of Materials Chemistry A, Advanced Science and Journal of Chemical Information and Modeling.

In The Last Decade

David Buttler

38 papers receiving 890 citations

Hit Papers

Exploring Topic Coherence over Many Models and Many Topics 2012 2026 2016 2021 2012 50 100 150 200 250
What are hit papers?

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.

  1. Exploring Topic Coherence over Many Models and Many Topics
    2012 289 citations Keith Stevens, Philip Kegelmeyer et al. Empirical Methods in Natural Language Processing profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David Buttler United States 15 534 448 244 107 78 40 980
Alfio Ferrara Italy 15 460 0.9× 312 0.7× 170 0.7× 46 0.4× 76 1.0× 74 707
Bo-June Hsu United States 13 715 1.3× 338 0.8× 82 0.3× 88 0.8× 83 1.1× 23 1.1k
Tehmina Amjad Pakistan 18 308 0.6× 358 0.8× 186 0.8× 75 0.7× 29 0.4× 43 962
David Hall United States 11 1.1k 2.0× 355 0.8× 52 0.2× 79 0.7× 116 1.5× 19 1.5k
Conor Hayes Ireland 16 379 0.7× 422 0.9× 151 0.6× 76 0.7× 21 0.3× 77 808
Corinna Breitinger Germany 12 509 1.0× 526 1.2× 67 0.3× 36 0.3× 37 0.5× 26 884
K. L. Kwok United States 15 882 1.7× 691 1.5× 116 0.5× 168 1.6× 50 0.6× 47 1.3k
Olga Vechtomova Canada 14 706 1.3× 661 1.5× 90 0.4× 141 1.3× 27 0.3× 48 1.1k
Marieke van Erp Netherlands 18 1.0k 1.9× 242 0.5× 42 0.2× 45 0.4× 102 1.3× 63 1.2k

Countries citing papers authored by David Buttler

Since Specialization
Citations

This map shows the geographic impact of David Buttler'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 Buttler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Buttler more than expected).

Fields of papers citing papers by David Buttler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Buttler. 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 Buttler. The network helps show where David Buttler may publish in the future.

Co-authorship network of co-authors of David Buttler

This figure shows the co-authorship network connecting the top 25 collaborators of David Buttler. A scholar is included among the top collaborators of David Buttler 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 Buttler. David Buttler 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.
Choi, Jiwoo, Jaewoong Choi, Kihoon Bang, et al.. (2025). MaTableGPT: GPT‐Based Table Data Extractor from Materials Science Literature. Advanced Science. 12(16). e2408221–e2408221. 4 indexed citations
2.
Choi, Jiwoo, Kihoon Bang, Jaewoong Choi, et al.. (2023). Deep learning of electrochemical CO2 conversion literature reveals research trends and directions. Journal of Materials Chemistry A. 11(33). 17628–17643. 9 indexed citations
3.
Hiszpanski, Anna M., Brian Gallagher, Peggy Li, et al.. (2020). Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge. Journal of Chemical Information and Modeling. 60(6). 2876–2887. 52 indexed citations
4.
Singh, Lisa, et al.. (2017). Using semantic graphs to detect overlapping target events and story lines from newspaper articles. International Journal of Data Science and Analytics. 5(1). 41–60. 10 indexed citations
5.
Stevens, Keith, Philip Kegelmeyer, David Andrzejewski, & David Buttler. (2012). Exploring Topic Coherence over Many Models and Many Topics. Empirical Methods in Natural Language Processing. 952–961. 289 indexed citations breakdown →
6.
Andrzejewski, David & David Buttler. (2011). Latent topic feedback for information retrieval. 600–608. 29 indexed citations
7.
Anastasiu, David C., Byron J. Gao, & David Buttler. (2011). A framework for personalized and collaborative clustering of search results. 573–582. 6 indexed citations
8.
Stoyanov, Veselin, Claire Cardie, Nathan Gilbert, et al.. (2010). Coreference Resolution with Reconcile. Meeting of the Association for Computational Linguistics. 156–161. 62 indexed citations
9.
Cárdenas, A.F., et al.. (2010). Measuring the interestingness of articles in a limited user environment. Information Processing & Management. 47(1). 97–116. 8 indexed citations
10.
Cárdenas, Alfonso F., et al.. (2007). Tracking multiple topics for finding interesting articles. 560–569. 23 indexed citations
11.
Symons, Christopher T., Nagiza F. Samatova, Byung Park, et al.. (2006). Multi-Criterion Active Learning in Conditional Random Fields. 12 indexed citations
12.
Samatova, Nagiza F., Christopher T. Symons, David Buttler, et al.. (2005). Information Extraction from Unstructured Text for the Biodefense Knowledge Center. University of North Texas Digital Library (University of North Texas).
13.
Buttler, David. (2004). A Short Survey of Document Structure Similarity Algorithms. University of North Texas Digital Library (University of North Texas). 3–9. 78 indexed citations
14.
15.
Buttler, David, Matthew A. Coleman, Terence Critchlow, et al.. (2002). Querying multiple bioinformatics information sources. ACM SIGMOD Record. 31(4). 59–64. 28 indexed citations
16.
Buttler, David, et al.. (2002). Establecimiento de parámetros físicos, químicos y organolépticos para diferenciar cacao fino y ordinario. 1 indexed citations
17.
Buttler, David & Terence Critchlow. (2002). Using meta-data to automatically wrap bioinformatics sources. Information and Software Technology. 44(4). 237–239. 3 indexed citations
18.
Han, Wei, David Buttler, & Calton Pu. (2001). Wrapping web data into XML. ACM SIGMOD Record. 30(3). 33–38. 40 indexed citations
19.
Liu, Ling, et al.. (2000). AQR-toolkit. ACM SIGMOD Record. 29(2). 597–597. 3 indexed citations
20.
Liu, Ling, Calton Pu, David Buttler, et al.. (1998). CQ. ACM SIGMOD Record. 27(2). 547–549. 2 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.

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