The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
A literature review on the state-of-the-art in patent analysis
Overview
The claims and statistics of this paper haven’t been checked yet.
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Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
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Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Citations
Cites
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,199
Self-Organization and Associative Memory
Kohonen, T. (1989) · Springer Series in Information Sciences · doi:10.1007/978-3-642-88163-3 · Cited by 8,740
Data Mining - Concepts and Techniques.
Perner, P. (2002) · Künstliche Intell. · Cited by 4,886
Neural-network-based fuzzy logic control and decision system
Lin, C.-T., & Lee, C. S. G. (1991) · IEEE Transactions on Computers · doi:10.1109/12.106218 · Cited by 1,480
A Survey of Opinion Mining and Sentiment Analysis
Liu, B., & Zhang, L. (2012) · Mining Text Data · doi:10.1007/978-1-4614-3223-4_13 · Cited by 1,323
Self-citationText mining techniques for patent analysis
Tseng, Y.-H., Lin, C.-J., & Lin, Y.-I. (2007) · Information Processing & Management · doi:10.1016/j.ipm.2006.11.011 · Cited by 782
A text-mining-based patent network: Analytical tool for high-technology trend
Yoon, B., & Park, Y. (2003) · The Journal of High Technology Management Research · doi:10.1016/j.hitech.2003.09.003 · Cited by 569
Innovation assessment through patent analysis
Abraham, B. P., & Moitra, S. D. (2001) · Technovation · doi:10.1016/s0166-4972(00)00040-7 · Cited by 298
Visualization of patent analysis for emerging technology
Kim, Y. G., Suh, J. H., & Park, S. C. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.01.033 · Cited by 274
Cited by
Academic Plagiarism Detection: A Systematic Literature Review
Foltýnek, T., Meuschke, N., & Gipp, B. (2020) · ACM Computing Surveys · doi:10.1145/3345317 · Cited by 238
Metadata
- Authors
- Abbas, A., Zhang, L., & Khan, S. U.
- Venue
- World Patent Information
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 37
- Pages
- 3-13
- ISSN
- 0172-2190 (ISSN-L)
- OpenAlex
- W2106116881
- Cited by
- 477
- References
- 33
- FWCI
- 69.84
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A literature review on the state-of-the-art in patent analysis. https://app.cochat.ai/papers/10.1016/j.wpi.2013.12.006
- The paper
- Abbas, A., Zhang, L., & Khan, S. U. (2014). A literature review on the state-of-the-art in patent analysis. World Patent Information. https://doi.org/10.1016/j.wpi.2013.12.006
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.