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a{color:var(--color-progressive)!important}}@media print{body.ns-0 .mw-parser-output .sidebar{display:none!important}}</style><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1129693374"><table class="sidebar nomobile nowraplinks plainlist"><tbody><tr><td class="sidebar-pretitle">Part of <a href="/wiki/Category:Citation_metrics" title="Category:Citation metrics">a series</a> on</td></tr><tr><th class="sidebar-title-with-pretitle" style="font-size:200%;font-weight:normal;"><a href="/wiki/Citation_impact" title="Citation impact">Citation metrics</a></th></tr><tr><td class="sidebar-image"><span typeof="mw:File"><a href="/wiki/File:Locations_of_papers_in_a_map_of_science_and_locations_of_the_key_papers_for_Nobel_prizes.tif" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/d/d2/Locations_of_papers_in_a_map_of_science_and_locations_of_the_key_papers_for_Nobel_prizes.tif/lossy-page1-110px-Locations_of_papers_in_a_map_of_science_and_locations_of_the_key_papers_for_Nobel_prizes.tif.jpg" decoding="async" width="110" height="67" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/d/d2/Locations_of_papers_in_a_map_of_science_and_locations_of_the_key_papers_for_Nobel_prizes.tif/lossy-page1-165px-Locations_of_papers_in_a_map_of_science_and_locations_of_the_key_papers_for_Nobel_prizes.tif.jpg 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/d/d2/Locations_of_papers_in_a_map_of_science_and_locations_of_the_key_papers_for_Nobel_prizes.tif/lossy-page1-220px-Locations_of_papers_in_a_map_of_science_and_locations_of_the_key_papers_for_Nobel_prizes.tif.jpg 2x" data-file-width="2220" data-file-height="1356" /></a></span></td></tr><tr><td class="sidebar-content"> <div class="hlist"> <ul><li><a href="/wiki/Altmetrics" title="Altmetrics">Altmetrics</a></li> <li><a href="/wiki/Article-level_metrics" title="Article-level metrics">Article-level</a></li> <li><a href="/wiki/Author-level_metrics" title="Author-level metrics">Author-level</a> <ul><li><a href="/wiki/Eigenfactor" title="Eigenfactor">Eigenfactor</a></li> <li><a href="/wiki/G-index" title="G-index">G-index</a></li> <li><a href="/wiki/H-index" title="H-index">H-index</a></li></ul></li> <li><a href="/wiki/Bibliographic_coupling" title="Bibliographic coupling">Bibliographic coupling</a></li> <li><a href="/wiki/Citation" title="Citation">Citation</a> <ul><li><a href="/wiki/Citation_analysis" title="Citation analysis">Analysis</a></li> <li><a href="/wiki/Citation_dynamics" title="Citation dynamics">Dynamics</a></li> <li><a href="/wiki/Citation_index" title="Citation index">Index</a></li> <li><a href="/wiki/Citation_graph" title="Citation graph">Graph</a></li></ul></li> <li><a href="/wiki/Co-citation" title="Co-citation">Co-citation</a> <ul><li><a class="mw-selflink selflink">Proximity Analysis</a></li></ul></li> <li><a href="/wiki/Coercive_citation" title="Coercive citation">Coercive citation</a></li> <li><a href="/wiki/Citation_cartel" title="Citation cartel">Citation cartel</a></li> <li><a href="/wiki/Initiative_for_Open_Citations" title="Initiative for Open Citations">I4OC</a></li> <li><a href="/wiki/Journal_ranking" title="Journal ranking">Journal-level</a> <ul><li><a href="/wiki/CiteScore" title="CiteScore">CiteScore</a></li> <li><a href="/wiki/Impact_factor" title="Impact factor">Impact factor</a></li> <li><a href="/wiki/SCImago_Journal_Rank" title="SCImago Journal Rank">SCImago</a></li></ul></li> <li><a href="/wiki/Kardashian_Index" title="Kardashian Index">Kardashian Index</a></li></ul> </div></td> </tr><tr><td class="sidebar-navbar"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1129693374"><style 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abbr{color:var(--color-base)!important}@media(prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .navbar li a abbr{color:var(--color-base)!important}}@media print{.mw-parser-output .navbar{display:none!important}}</style><div class="navbar plainlinks hlist navbar-mini"><ul><li class="nv-view"><a href="/wiki/Template:Citation_metrics" title="Template:Citation metrics"><abbr title="View this template">v</abbr></a></li><li class="nv-talk"><a href="/wiki/Template_talk:Citation_metrics" title="Template talk:Citation metrics"><abbr title="Discuss this template">t</abbr></a></li><li class="nv-edit"><a href="/wiki/Special:EditPage/Template:Citation_metrics" title="Special:EditPage/Template:Citation metrics"><abbr title="Edit this template">e</abbr></a></li></ul></div></td></tr></tbody></table> <figure class="mw-halign-right" typeof="mw:File/Thumb"><a href="/wiki/File:Co-citation_Proximity_Analysis_Concept.svg" class="mw-file-description"><img alt="Documents B and C are cited in closer proximity to each other in the full-text of the citing document, when compared to document A. Hence, according to co-citation proximity analysis, documents B and C are more strongly related than documents A and B or A and C." src="//upload.wikimedia.org/wikipedia/en/thumb/5/50/Co-citation_Proximity_Analysis_Concept.svg/300px-Co-citation_Proximity_Analysis_Concept.svg.png" decoding="async" width="300" height="226" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/en/thumb/5/50/Co-citation_Proximity_Analysis_Concept.svg/450px-Co-citation_Proximity_Analysis_Concept.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/5/50/Co-citation_Proximity_Analysis_Concept.svg/600px-Co-citation_Proximity_Analysis_Concept.svg.png 2x" data-file-width="230" data-file-height="173" /></a><figcaption>Figure visualizing the Co-citation Proximity Analysis (CPA) approach to document similarity computation.</figcaption></figure> <p><b>Co-citation Proximity Analysis</b> (<b>CPA</b>) is a document <a href="/wiki/Similarity_measure" title="Similarity measure">similarity measure</a> that uses <a href="/wiki/Citation_analysis" title="Citation analysis">citation analysis</a> to assess <a href="/wiki/Semantic_similarity" title="Semantic similarity">semantic similarity</a> between documents at both the global document level as well as at individual section-level.<sup id="cite_ref-Gipp_1-0" class="reference"><a href="#cite_note-Gipp-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-2" class="reference"><a href="#cite_note-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> The similarity measure builds on the <a href="/wiki/Co-citation" title="Co-citation">co-citation analysis</a> approach, but differs in that it exploits the information implied in the placement of citations within the full-texts of documents. </p><p>Co-citation Proximity Analysis was conceived by B. Gipp in 2006<sup id="cite_ref-3" class="reference"><a href="#cite_note-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> and the description of the document similarity measure was later published by Gipp and Beel in 2009.<sup id="cite_ref-Gipp_1-1" class="reference"><a href="#cite_note-Gipp-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> The similarity measure rests on the assumption that within a document’s full-text, the documents cited in close proximity to each other tend to be more strongly related than those documents cited farther apart. The figure to the right illustrates the concept. The CPA approach to document similarity assumes the documents B and C to be more strongly related than the documents B and A, because the citations to B and C occur within the same sentence, whereas the citations to B and A are separated by several paragraphs. </p><p>The advantage of the CPA approach compared to other citation and co-citation analysis approaches is an improvement in precision. Other widely used citation analysis approaches, such as <a href="/wiki/Bibliographic_coupling" title="Bibliographic coupling">Bibliographic Coupling</a>, <a href="/wiki/Co-citation" title="Co-citation">Co-Citation</a> or the <a href="/wiki/Bibliographic_coupling#History" title="Bibliographic coupling">Amsler measure</a>, do not take into account the location or proximity of citations within documents. The CPA approach allows a more granular automatic classification of documents and can also be used to identify not only related documents, but the specific sections within texts that are most related. </p> <meta property="mw:PageProp/toc" /> <div class="mw-heading mw-heading2"><h2 id="Method_of_calculation">Method of calculation</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Co-citation_Proximity_Analysis&action=edit&section=1" title="Edit section: Method of calculation"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>The CPA similarity measure calculates a <b>Citation Proximity Index</b> (<b>CPI</b>) for each set of documents cited by an examined document.<sup id="cite_ref-Gipp_1-2" class="reference"><a href="#cite_note-Gipp-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> Cited documents are assigned a weight of <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle {\frac {1}{2^{n}}}}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mrow class="MJX-TeXAtom-ORD"> <mfrac> <mn>1</mn> <msup> <mn>2</mn> <mrow class="MJX-TeXAtom-ORD"> <mi>n</mi> </mrow> </msup> </mfrac> </mrow> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle {\frac {1}{2^{n}}}}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/f98a4804516c6a111a316c88d88290a31951034d" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -2.005ex; width:3.217ex; height:5.343ex;" alt="{\displaystyle {\frac {1}{2^{n}}}}"></span>, where <i>n</i> stands for the number of levels between citations. Beginning at the lowest level, levels may be defined as citation groups, sentences, paragraphs, chapters, and finally the entire document or even journal. </p><p>There are several variations of the CPA algorithm. </p> <ul><li><i>Basic-CPA</i> – fundamental concept of CPA as described above</li> <li><i>Extended-CPA</i> – considers the tree structure and order of citations within citation groups</li> <li><i>Multidimensional-CPA</i> – uses additional information such as the impact factor</li> <li><i>Hybrid-CPA</i> – combines the CPI with other similarity measures, for example text-based measures. This boosts performance especially for documents with insufficient citation information.</li></ul> <div class="mw-heading mw-heading2"><h2 id="Performance">Performance</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Co-citation_Proximity_Analysis&action=edit&section=2" title="Edit section: Performance"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>The CPA similarity measure builds upon the co-citation document similarity approach with the distinctive addition of proximity analysis. Therefore, the CPA approach allows for the calculation of a more granular resolution of overall document similarity. CPA has been found to outperform co-citation analysis, especially when documents contain extensive bibliographies and in cases where documents have not been frequently cited together (i.e. have a low co-citation score).<sup id="cite_ref-Gipp_1-3" class="reference"><a href="#cite_note-Gipp-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-Schwarzer16_4-0" class="reference"><a href="#cite_note-Schwarzer16-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> Liu and Chen found that sentence-level co-citations are potentially more efficient markers for use in co-citation analysis in comparison to the loosely coupled article-level only co-citations, since sentence-level co-citations tend to preserve the essential structure of the traditional co-citation network and also form a much smaller subset of all co-citation instances.<sup id="cite_ref-5" class="reference"><a href="#cite_note-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> </p><p>An analysis by Schwarzer et al.<sup id="cite_ref-Schwarzer16_4-1" class="reference"><a href="#cite_note-Schwarzer16-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> showed that the citation-based measures CPA and <a href="/wiki/Co-citation" title="Co-citation">co-citation analysis</a>, have complementary strengths compared to text-based similarity measures. Text-based similarity approaches reliably identified more narrowly similar articles out of a test collection of Wikipedia articles, e.g. articles sharing identical terms, while the CPA approach outperformed CoCit at identifying more broadly related articles, as well as more popular articles, which the authors claim to likely also be of higher quality.<sup id="cite_ref-Schwarzer16_4-2" class="reference"><a href="#cite_note-Schwarzer16-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading2"><h2 id="See_also">See also</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Co-citation_Proximity_Analysis&action=edit&section=3" title="Edit section: See also"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li><a rel="nofollow" class="external text" href="http://www.isg.uni-konstanz.de/projects/citrec/">CITREC</a>, an evaluation framework for citation-based similarity measures such as <a href="/wiki/Bibliographic_coupling" title="Bibliographic coupling">Bibliographic coupling</a>, <a href="/wiki/Co-citation" title="Co-citation">Co-citation</a>, Co-citation Proximity Analysis and others.<sup id="cite_ref-Gipp15_6-0" class="reference"><a href="#cite_note-Gipp15-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup></li></ul> <div class="mw-heading mw-heading2"><h2 id="References">References</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Co-citation_Proximity_Analysis&action=edit&section=4" title="Edit section: References"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <style data-mw-deduplicate="TemplateStyles:r1239543626">.mw-parser-output .reflist{margin-bottom:0.5em;list-style-type:decimal}@media screen{.mw-parser-output .reflist{font-size:90%}}.mw-parser-output .reflist .references{font-size:100%;margin-bottom:0;list-style-type:inherit}.mw-parser-output .reflist-columns-2{column-width:30em}.mw-parser-output .reflist-columns-3{column-width:25em}.mw-parser-output .reflist-columns{margin-top:0.3em}.mw-parser-output .reflist-columns ol{margin-top:0}.mw-parser-output .reflist-columns li{page-break-inside:avoid;break-inside:avoid-column}.mw-parser-output .reflist-upper-alpha{list-style-type:upper-alpha}.mw-parser-output .reflist-upper-roman{list-style-type:upper-roman}.mw-parser-output .reflist-lower-alpha{list-style-type:lower-alpha}.mw-parser-output .reflist-lower-greek{list-style-type:lower-greek}.mw-parser-output .reflist-lower-roman{list-style-type:lower-roman}</style><div class="reflist"> <div class="mw-references-wrap"><ol class="references"> <li id="cite_note-Gipp-1"><span class="mw-cite-backlink">^ <a href="#cite_ref-Gipp_1-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-Gipp_1-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-Gipp_1-2"><sup><i><b>c</b></i></sup></a> <a href="#cite_ref-Gipp_1-3"><sup><i><b>d</b></i></sup></a></span> <span class="reference-text">Bela Gipp and Joeran Beel, 2009 <a rel="nofollow" class="external text" href="http://gipp.com/wp-content/papercite-data/pdf/gipp09a.pdf">"Citation Proximity Analysis (CPA) – A new approach for identifying related work based on Co-Citation Analysis"</a> in Birger Larsen and Jacqueline Leta, editors, Proceedings of the 12th International Conference on Scientometrics and Informetrics (ISSI’09), volume 2, pages 571–575, Rio de Janeiro (Brazil), July 2009.</span> </li> <li id="cite_note-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-2">^</a></b></span> <span class="reference-text">Bela Gipp and Joeran Beel. "Method and system for detecting a similarity of documents". Patent Application, Oct 27, 2011. <a rel="nofollow" class="external text" href="http://www.patentlens.net/patentlens/patents.html?patnums=US_2011_0264672_A1&language=en&%7CUS">2011/0264672 A1</a>.</span> </li> <li id="cite_note-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-3">^</a></b></span> <span class="reference-text">Bela Gipp, 2006. "Doctoral Proposal: (Co-)Citation Proximity Analysis – A Measure to Identify Related Work"</span> </li> <li id="cite_note-Schwarzer16-4"><span class="mw-cite-backlink">^ <a href="#cite_ref-Schwarzer16_4-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-Schwarzer16_4-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-Schwarzer16_4-2"><sup><i><b>c</b></i></sup></a></span> <span class="reference-text">M. Schwarzer, M. Schubotz, N. Meuschke, C. Breitinger, <a href="/wiki/Volker_Markl" title="Volker Markl">V. Markl</a>, and B. Gipp, <a rel="nofollow" class="external text" href="https://www.gipp.com/wp-content/papercite-data/pdf/schwarzer2016.pdf">"Evaluating Link-based Recommendations for Wikipedia"</a> in Proceedings of the 16th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), New York, NY, USA, 2016, pp. 191-200. </span> </li> <li id="cite_note-5"><span class="mw-cite-backlink"><b><a href="#cite_ref-5">^</a></b></span> <span class="reference-text">Shengbo Liu and Chaomei Chen, 2001 <a rel="nofollow" class="external text" href="http://cluster.ischool.drexel.edu/~cchen/papers/2011/ISSI2011.pdf">"The Effects of Co-citation Proximity on Co-citation Analysis"</a>, The 13th Conference of the International Society for Scientometrics and Informetrics (ISSI), July 4–7, 2011 Durban, South Africa.</span> </li> <li id="cite_note-Gipp15-6"><span class="mw-cite-backlink"><b><a href="#cite_ref-Gipp15_6-0">^</a></b></span> <span class="reference-text">Bela Gipp, Norman Meuschke & Mario Lipinski, 2015. <a rel="nofollow" class="external text" href="http://gipp.com/wp-content/papercite-data/pdf/gipp15b.pdf">"CITREC: An Evaluation Framework for Citation-Based Similarity Measures based on TREC Genomics and PubMed Central"</a> in Proceedings of the iConference 2015, Newport Beach, California, 2015.</span> </li> </ol></div></div> <div class="mw-heading mw-heading2"><h2 id="Further_reading">Further reading</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Co-citation_Proximity_Analysis&action=edit&section=5" title="Edit section: Further reading"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Bela Gipp and Joeran Beel. Identifying Related Documents For Research Paper Recommender By CPA And COA. In S. I. Ao, C. Douglas, W. S. Grundfest, and J. Burgstone, editors, Proceedings of the world congress on engineering and computer science 2009, volume 1 of Lecture Notes in Engineering and Computer Science, pages 636-639, Berkeley (USA), oct 2009. International Association of Engineers (IAENG), Newswood Limited. Available <a rel="nofollow" class="external text" href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.208.7997&rep=rep1&type=pdf">here</a> </p><p>Bela Gipp. Measuring Document Relatedness by Citation Proximity Analysis and Citation Order Analysis. In M. Lalmas, J. Jose, A. Rauber, F. Sebastiani, and I. Frommholz, editors, Proceedings of the 14th European conference on digital libraries (ecdl’10): research and advanced technology for digital libraries, volume 6273 of Lecture Notes of Computer Science (LNCS). Springer, sep 2010. 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