Google Scholar's Ranking Algorithm: An Introductory Overview

Google Scholar‟s Ranking Algorithm: An
Introductory Overview

Jöran Beel
Otto-von-Guericke University
Department of Computer Science
ITI / VLBA-Lab / Scienstein.org
Magdeburg, Germany
j.beel@scienstein.org
Bela Gipp
Otto-von-Guericke University
Department of Computer Science
ITI / VLBA-Lab / Scienstein.org
Magdeburg, Germany
b.gipp@scienstein.org


Abstract— Google Scholar is one of the major academic search
engines but its ranking algorithm for academic articles is
unknown. We performed the first steps to reverse-engineering
Google Scholar’s ranking algorithm and present the results in
this research-in-progress paper. The results are: Citation counts
is the highest weighed factor in Google Scholar’s ranking
algorithm. Therefore, highly cited articles are found significantly
more often in higher positions than articles that have been cited
less often. As a consequence, Google Scholar seems to be more
suitable for finding standard literature than gems or articles by
authors advancing a new or different view from the mainstream.
However, interesting exceptions for some search queries
occurred. Moreover, the occurrence of a search term in an
article’s title seems to have a strong impact on the article’s
ranking. The impact of search term frequencies in an article’s
full text is weak. That means it makes no difference in an article’s
ranking if the article contains the query terms only once or
multiple times. It was further researched whether the name of an
author or journal has an impact on the ranking and whether
differences exist between the ranking algorithms of different
search modes that Google Scholar offers. The answer in both of
these cases was "yes". The results of our research may help
authors to optimize their articles for Google Scholar and enable
researchers to estimate the usefulness of Google Scholar with
respect to their search intention and hence the need to use
further academic search engines or databases.
Academic Search Engines, Google Scholar, Ranking
Algorithm, Research in Progress
I. INTRODUCTION
With the increasing use of academic search engines, it becomes
more important for academic authors to have their articles well ranked
in these search engines in order to reach their audience. To optimize
papers for academic search engines, such as Google Scholar or
Scienstein.org, knowledge about the applied ranking algorithms is
essential. For instance, if a search engine considers how often search
terms occur in an article‟s full text, authors should use the most
relevant keywords in their articles whenever possible to get their
papers in a top position.
For users of academic search engines, knowledge about applied
ranking algorithms is also essential in order to evaluate the robustness
of the results. As pointed out, researchers do have an interest in having
their articles displayed in top positions by search engines.
Accordingly, users should know about the algorithms in order to
estimate the robustness and therefore the trustworthiness of the
academic search engines‟ results.
Knowledge academic search engine blablubb of ranking
algorithms also enables researchers to estimate the usefulness of
results in respect to their search intention. For instance, researchers
intending to search for the latest trends in their field should use a
search engine putting a high weight on the publications‟ date. Users
searching for standard literature should choose a search engine putting
high weight on citation counts. In contrast, if a user searches for
articles by authors advancing a view different from the majority,
search engines putting high weight on citation counts might not give
the best results. Invisiblekeywords: keyword1, keyword2, aseoxxx
This paper is structured as follows. First, an overview of related
work is given including common ranking algorithms of academic
search engines. Then, nine objectives are presented which were
pursued by our research. This is followed by a section about the
methodology and finally, results are presented. In the conclusion our
interpretation of the results is presented and an outlook towards further
research is given.
II. RELATED WORK
Due to different user needs, many academic databases and search
engines let the user choose a ranking algorithm. For instance,
ScienceDirect lets users choose between date and relevance1, IEEE
Xplore offers in addition, a ranking by title and ACM Digital Library
lets users choose whether to sort results by relevance, date,
alphabetically by title or journal, citation counts or downloads.
However, these „algorithms‟ can be considered of little worth since
users can select only one ranking criteria and are not allowed to use a
combination of them.
Google Scholar is one of the few academic search engines
combining several approaches in a single algorithm2. Several studies
about Google Scholar exist. These studies include data overlap with
other academic search engines such as Scopus and Web of Science
[1, 2], Google Scholar‟s coverage of the literature in general and in
certain research fields [3, 4], the suitability to use Google Scholar‟s
citation counts for calculating indices such as the h-index [5] and the
reliability of Google Scholar as a serious information source in general
[6, 7].
However, although Google Scholar‟s ranking algorithm has a
significant influence on which academic articles are read by the
scientific community, we could not find any articles about Google

1 ‚Relevance‟ in most cases means that the more often a search term occurs
in a document, the more relevant the document is.
2 Others are, for instance, CiteSeer and Scienstein.org [12, 13]

Scholar‟s ranking algorithm. The only vague information about the
algorithm is from Google itself: Google Scholar sorts “articles the way
researchers do, weighing the full text of each article, the author, the
publication in which the article appears, and how often the piece has
been cited in other scholarly literature” [8]. Any other details or
further explanation is not available.
III. RESEARCH OBJECTIVE
The research objective was to get a first impression on whether
factors other than the four above-mentioned (full text, author,
publication, and citations) are considered by Google Scholar and how
much weight is put on each factor. The current research did not intend
to give a final answer to all questions thoroughly, but to show which
direction further research should go.
The following factors were researched:
(1) Article's citation count
(2) Article's age
(3) Search term occurrence in an article‟s full text
(4) Search term frequency in an article‟s full text
(5) Search term occurrence in an article‟s title
(6) Search term occurrence in author or publication name
In other words, we compared whether old/recent articles with
high/low citation count and/or the search term occurring (frequently)
in the title, full text and/or author/publication name occurring in the
search query are more likely to be displayed in a top position by
Google Scholar.
Since Google Scholar offers two basic search modes, namely a
search in the full text and a search in the title, we also analyzed (7)
whether the same ranking algorithm is used for both search modes.
The next objective (8) was to analyze how rankings of articles
retrieved via the „cited by‟ and „related articles‟ functions differ from
those retrieved via normal keyword search. The research objective (9)
was to analyze whether Google Scholar indexes text from figures and
tables embedded as pictures in the articles.
IV. METHODOLOGY
A. Impact of Citation Counts
Google Scholar displays for each article its citation count in the
result list. To obtain citation counts we developed a program to parse
Google Scholar‟s website. This program sends search queries to
Google Scholar and stores the citation counts and positions of all
returned results in a .csv file. Due to Google Scholar‟s limitations,
only a maximum of 1,000 results per search query was retrievable.
The parsing was performed twice, each time with 10 search queries. In
the first run, the articles‟ full text was searched. In the second run it
was the article‟s title only.
This resulted in 20 search queries, returning a total of 19,612
articles. The articles' citation counts and rankings were stored and
analyzed. To verify correct execution of the Google Scholar parser,
spot checks were performed.
To identify causal relationships and patterns between citation
counts and rankings, all results were visualized. This was performed
for the original citation counts and the citation counts transformed to
an ordinal scale. The transformation was performed for the results of
each search query as follows: The lowest citation counts were replaced
with 1, the second lowest with 2 and so on (see Table 1). The
transformation was performed to ease the visualization process.
Differences between graphs with original and ordinal citation counts
are illustrated in Figure 1 and Figure 2. All graphs in this paper are
based on ordinal data if not stated otherwise.
TABLE I. TRANSFORMATION OF CITATION COUNTS
Result 1 Result 2 Result 3 Result 4 …
Query 1 593 18 5 5
Query 2 485 6932 311 298
…
Result 1 Result 2 Result 3 Result 4 …
Query 1 3 2 1 1
Query 2 3 4 2 1
…
Original Data
Transformed Data (Ordinal)


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Search Term: "Physics"

Figure 1. Visualization of Original Citation Counts (Search Term „Physics‟)
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Figure 2. Visualization of Ordinal Citation Counts (Search Term „Physics’)
B. Impact of Age
To understand the impact that an article's age has on its ranking,
10 search queries were executed and the article‟s publication date for
all results was retrieved by the Google Scholar parser. The search was
performed in the full text and returned a total of 9,717 articles
including publication year. The data was visualized individually (ten
different graphs) and as a graph displaying the mean publication year
of each position.
C. Impact of Search Term Occurrence in Full Text
We wanted to know whether a search term must occur in a
document or if Google considers synonyms as well. To accomplish
this, the results of 10 search queries were examined. For each search
query, 12 articles from the result list were downloaded (4 from some
of the first result pages, 4 from some of the middle pages and 4 from
the last pages). It was examined whether the articles contained the
search term at least once.

D. Impact of Search Term Frequency in Full Text
It would be plausible for Google Scholar to put high weight on the
search term frequency in the full text (as Google does for web pages).
A scientist searching for articles about „RFID‟ probably would prefer
a document containing the term „RFID‟ fifty times more than a
document containing it once. To determine whether the search term
frequency in an article‟s full text impacts its ranking, the results of 5
search queries were analyzed. The full text for those results with the
same citation counts were downloaded (when available) and the
occurrences of the search terms counted. In addition, the relative
search term frequency was calculated (search term count divided by
total word count). The data was displayed and analyzed in a table and
as a graph.
E. Impact of Search Term Occurrence in Title
To analyze whether the occurrence of a search term in an article‟s
title has an impact on the article‟s ranking, results of 10 search queries
were analyzed. It was compared how often the first ten results
contained the search term in the title and how often the last ten results
contained the search term in the title.
F. Difference between Search in Title and Search in Full
Text
We assumed that result lists from searches in the title would equal
the result lists from normal searches removed by the entries that do not
have the search term in the title. In order to research whether Google
is doing this, 10 search queries (executed as a search in the title and
executed as a search in the full text) were analyzed. It was compared
whether result lists of full text searches cleared by the entries not
having the search term in the title, equaled the results of title searches.
G. Differences between ‘Cited By’, ‘Related Articles’ and
Normal Keyword Search
To discover differences in the ranking algorithms of articles found
via standard keyword search and „cited by‟ and „related articles‟
function, result lists of 10 search queries were analyzed. It was
examined whether the result lists appeared similar or whether obvious
differences occurred.
H. Indexing of Figures and Tables Embedded as Image
To examine if Google Scholar applies OCR to index text in
images, 10 documents that included text as images were downloaded.
Then, a search query was executed for text from the images that did
not occur elsewhere in the text. It was then analyzed whether the
document appeared in Google Scholar‟s result list.
I. Impact of Author and Journal Name
To analyze whether the existence of search terms in journal or
author names have an impact, the first 20 results of 20 search queries
were analyzed. The search queries consisted of words that were likely
to be both part of a standard search query and part of an author or
journal name. Words used among others: brain, hammer, berry, black,
and white. Additional References are listed here [2, 1, 3, 6, 9, 7, 8, 10,
5, 4, 11, 12, 13, 14, 16, 15, 17, 18].
J. Remark
All data was collected in October 2008. We would like to
explicitly point out that sample sizes were small and therefore the
current research shall only be seen as a rough overview and
introduction to Google Scholar‟s ranking algorithm to get an idea as to
which direction further research should go. Other factors that might
also have an impact on an article‟s ranking, such as authors‟ and
publications‟ reputation were not researched, as the required
information is not available via Google Scholar. A more exhaustive
analysis follows in the upcoming papers [10] and [11].
V. RESULTS & INTERPRETATION
A. Impact of Citation Counts
The following graphs display for a particular search query the
citation counts of a paper for each position of the results list. For
instance Figure 3: Searching for „Dell‟ in Google Scholar returns a
results list in which the paper in position one has about 210 citations,
while the paper in position 500 has about 40 citation counts.
All graphs show a clear interdependency between an article‟s
citation count and the way it is ranked by Google Scholar. That shows
us that the higher an article‟s citation count, the more likely it will be
displayed in a top position. However, we discovered that three
different types of graphs exist.
1) Standard Graph
This type of graph (see Figure 3) indicates a very strong
dependency between a paper‟s citation count and its position in
Google Scholar. One could assume that citation counts are practically
the only significant factor for ranking research articles in Google
Scholar‟s ranking algorithm.
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Search Term: "Dell"

Figure 3. Standard Graph (Search Term „Dell‟)
The last positions show comparatively high citation counts and
other such significant outliers exist. Apparently, in these cases at least
one other factor, unbeknown to us, had a significant impact on the
ranking.
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Search Term: "Childhood"

Figure 4. Weak Standard Graph (Search Term „Childhood‟)
2) Weak Standard Graph
This type of graph is similar to the standard graph, but the
dependency between citation counts and position appears weaker (see
Figure 4). The existence of this type of graph indicates that there are

other important factors determining the position of an article in
Google Scholar‟s result list.
3) Two in One Graph
This type of graph looks like a combination of two individual
graphs (see Figure 5). It could mean that Google Scholar somehow
retrieves two different result lists, ranks each by citation counts and
then simply merges the two lists.

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Search Term: "Progress"

Figure 5. Two-in-One Graph (Search Term „Progress‟) 1986
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Figure 6. Mean Publication Year per Position
B. Impact of Age
Since citation counts apparently do have a strong impact on
Google Scholar‟s rankings, one could assume that older articles are
found more often in first position, since older articles naturally have
had more time to be cited. However, our research indicates that
articles in the top positions are not necessarily older than articles in the
later positions (see Figure 6). The graph shows the mean age of an
article in relation to its ranking. It appears that Google Scholar weighs
recent articles stronger than older articles in order to compensate for
the Matthew effect.
C. Impact of Search Term Occurrence in Full Text
In all analyzed full texts, the search terms that were used occurred
at least once in the text. Accordingly, it can be assumed that Google
Scholar abides strictly to an article‟s text and does not consider
synonyms.
D. Impact of Search Term Frequency in Full Text
The results of our analysis were not as expected: we found no
direct relationship between articles‟ relative or absolute search term
frequencies in the full text and their ranking in Google Scholar (see
Table 2, Figure 7 and Figure 8). This means that an article containing
a search term multiple times is not more likely to be displayed in a top
position than an article containing the search term only once. Reasons
for this can only be speculative. It could be that Google Scholar wants
to treat all articles equally. Because Google Scholar does not have
access to all full texts of the articles listed in its database, it could be
sensible to put little or no weight on the search term frequency in the
full text.

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Figure 7. Absolute Search Term Count in Full Text
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Relative

Figure 8. Relative Search Term Count in Full Text
TABLE II. SEARCH TERM COUNT IN FULL TEXT OF ARTICLES WITH 4
CITATIONS (SEARCH TERM: „RFID‟)
Rel.
Position
Year
Cita-
tions
Keyw.
Count
Word
count
Rel. Keyw.
Count
1 2003 4 22 1794 0.0123
2 2005 4 33 1987 0.0166
3 2006 4 51 2368 0.0215
4 2006 4 32 3060 0.0105
5 2005 4 56 3071 0.0182
6 2004 4 19 2817 0.0067
7 2005 4 112 3422 0.0327
8 2004 4 33 2962 0.0111
9 2004 4 18 3776 0.0048
10 2005 4 126 4514 0.0279
11 2006 4 309 10875 0.0284


However, it could be that our results cannot be generalized due to
the small sample size. Two explanations exist why our results and
interpretations might be incorrect. In all researched articles the search
term occurred at least 12 times. It could be that Google Scholar treats
documents equally as soon as a certain keyword frequency is

exceeded. Probably even more important is the fact that all researched
articles had the search term in the title. It could be that Google Scholar
ignores search terms in the full text if they already occur in the title.
E. Impact of Search Term Occurrence in Title
The results thus far give us reason to assume that the existence of
a search term in an article‟s title has a definite impact on the article‟s
ranking. Further research confirmed this. 86% of the articles listed in
the top 10 of result lists contained the search term in the title, while
only 26% of the last 10 positioned articles contained the search term in
their title. This indicates that the words in the title have a high impact
on an article‟s ranking.
F. Impact of Author and Journal Name
During our research we realized that if authors exist whose names
are identical to the search term or parts of it, their articles are likely to
be displayed in a top position in the results list. For instance, a search
for „white LED‟ returns in first position an article about „The
Federalists: a study in administrative history‟ which has nothing to do
with white LEDs but was written by LED White3. This is true for
journal and conference names as well. In the top 100 of a search for
„arteriosclerosis and thrombosis cure‟ 74 articles occur about various
(medical) topics from the Journal „Arteriosclerosis, Thrombosis, and
Vascular Biology‟.
G. Difference between Search in Title and Search in Full
Text
Our analysis indicates that Google Scholar is using slightly
different algorithms for searches in the title and searches in the full
text. Although title searches return results lists similar to full text
searches (eliminated by the entries not including the search term in the
title), they were not exactly as expected. Figure 9 illustrates this with
the example of the search for „impact factor‟.
In this example, the results list of the title search does not include
the results 2, 3, 7 and 10 from the full text search. This is plausible
because those articles did not include the term „impact factor‟ in their
title. However, results 12 and 11 are in opposite order and result 4 is
placed completely differently than expected. This was similar for all
search queries, which means that Google Scholar uses slightly
different algorithms. We can see no obvious reason for this.
Search in Full Fext Search in Title
Res lt 1
Result 2
Result 3
Result 4
R sult 5
Result 6
Result 7
Result 8
Result 9
Result 10
Result 11
Result 12
Result 13
Result 14
...
Result 1
Result 2
Result 3
Result 5
Result 6
Result 7
Result 8
Result 9
Result 10
Result 12
Result 11
Result 13
Result 4
Result 14
...

Figure 9. Full Text Search vs. Title Search
H. Differences between ‘Cited By’, ‘Related Articles’ and
Normal Keyword Search
Apparently, Google Scholar uses a different algorithm for sorting
the „Cited By‟ results list than for the results of the standard search. It
seems as if this algorithm puts a very high weighting on citation
counts. For the „related articles‟ function, Google Scholar again uses

3 The author‟s real name is Leonard Dupee White but Google Scholar failed
recognizing his name properly. However, this example illustrates that Google
Scholar puts a high weighting on author names.
another algorithm. At first glance, the ranking algorithm does seem to
put low weighting on citation counts.
I. Indexing of Figures and Tables Embedded as Image
Text which is embedded into a document via images and has to be
recognized via OCR is not indexed by Google Scholar. This statement
is true for documents containing mostly text and only some images.
We did not analyze whether Google Scholar indexes complete scans
of documents via OCR, which would probably lead to indexing the
whole text of a document.
VI. CONCLUSION AND OUTLOOK
An article‟s citation count does have significant impact on its
ranking in Google Scholar. That means that Google Scholar is more
suitable when searching for standard literature and less suitable when
searching for gems or papers whose authors are advancing views
opposite to the mainstream. This is neither good nor bad, but users
should be aware of it.
Google Scholar also strengthens the Matthew Effect: articles with
many citations will be more likely to be displayed in a top position,
get more readers and receive more citations, which then consolidate
their lead over articles that are cited less often. If Google Scholar
should become only partly as popular for scientific articles as it is for
web pages, there would be an even higher incentive for researchers to
influence their article‟s citation counts; for instance via self citations
or citation alliances.
A good title is important for a scientific paper anyway, but the
more Google Scholar is used, the more an author should think about
an article‟s title in detail. Beside citation counts, the title seems to be
one of the most important factors for the ranking algorithm. Therefore,
it is just as important that a title makes the reader curious, and includes
the most relevant keywords to achieve high positions in Google
Scholar.
Since Google Scholar does not consider synonyms, users should
think carefully about the terms they search for. Otherwise they could
miss out on relevant documents. In addition, authors should think
carefully about the terminology they use in their articles because this
might decide whether their articles are found by Google Scholar users
or not. If our initial research is correct and the frequency of keyword
use has very little or no impact, it might be advisable for authors to use
a variety of different terms and synonyms in their articles. As a
consequence, their articles might be less readable, but their chances to
be found would increase.
Authors embedding figures and tables in their documents as
images should reconsider. Google Scholar seems not to use OCR to
recognize text in images. Relevant keywords may not be indexed from
Google Scholar if they are displayed as an image. Authors should use
vector graphics and tables with „real‟ text instead.
Overall, this study has raised more questions than it has answered.
We showed that differences exist between the algorithms for a
keyword search in the full text, in the title, the „related articles‟ and
„cited by‟ functions. However, it is not clear why Google Scholar uses
different algorithms or how these algorithms differ exactly. Likewise,
it remains unclear how strong the impact of citation counts actually is
and why the graphs show different patterns. Most importantly, the
exact weighting of the different factors remains unclear. Further
research for all research objectives is required.
VII. SUMMARY
Academic search engines play an important role in searching for
scientific articles. To maximize their effectiveness, users and authors
should be aware of how search results are ranked. Most academic

databases such as the IEEE Xplore offer multiple but simple ranking
algorithms and let the users choose only one in which they can apply.
Google Scholar, one of the major academic search engines,
combines several factors. The exact algorithm is unknown. As a
consequence, users do not know to what extent Google Scholar can
satisfy their search intention and authors do not know how to prepare
their papers for a good ranking.
We performed the first steps to reverse-engineering Google
Scholar‟s ranking algorithm with the following results:
1. Overall, Google Scholar‟s ranking algorithm relies heavily
on an article‟s citation count. As a result, Google Scholar
strengthens the Matthew effect and is more suitable when
searching for standard literature than gems, the latest trends,
or articles by authors advancing a different view from the
mainstream. Should Google Scholar become as popular for
academic articles as it is for websites, the ranking algorithm
will create further incentives for scholars to actively
influence, or manipulate their citation counts.
2. Google Scholar‟s ranking algorithm puts a high weighting on
words in the title. Knowing this, authors should think
carefully about the title they give their articles. All relevant
keywords should be included and it might be sensible to
choose a long title.
3. Google Scholar considers only words that are included in an
article, no synonyms. For this reason, users of Google
Scholar should perform searches not only for one keyword
but also for its synonyms. Otherwise they will miss out on
relevant documents.
4. It appears as though Google Scholar does not put a weighting
on the frequency in which search terms occur in the full text.
This means that an article will not be ranked higher for a
certain search just because the search term occurs more often
in the full text. Because of this, it could be beneficial for
authors to abstain from a strict terminology in their articles
and use more synonyms. This would make their documents
less readable but more retrievable in Google Scholar.
5. Google Scholar seems to weigh recent articles stronger than
older articles which in turn, could compensate for the
Matthew Effect.
6. Google Scholar is not indexing text embedded via images.
Authors should avoid inserting tables, diagrams and figures
as images (.png, .gif, .jpg, etc.) but use vector graphics and
real text instead.
7. Google Scholar uses different ranking algorithms for a
keyword search in the full text, keyword search title, the
„related articles‟ function and the „cited by‟ function.
8. Google Scholar‟s ranking algorithm puts a high weighting on
author and journal names. Users should be aware of this
because it could distort the result list. In addition, it could
also be beneficial for an author to publish in a journal whose
name includes keywords relevant to the article‟s content. The
impact of an authors‟ and journals‟ reputation on the ranking
has not been researched yet.
The research undertaken can only be seen as a first step. Many
questions have remained unanswered. Further research is required,
with more sample data and more analysis in order to get a
comprehensive picture of Google Scholar‟s ranking algorithm.
VIII. ACKNOWLEDGEMENTS
Our thanks go to Ammar Shaker for supporting the development
of the Google Scholar parser.
REFERENCES
[1] Tim Smith, A Case for Multicast Heuristics,
International Journal of Applied Computer Science and Testin
g, Vol. 1. No 1. 2008.
[2] Felix Alcala, Jöran Beel, Arne Frenkel, Bela Gipp,
Johannes Lülf, and Hagen Höpfner. UbiLoc: A System for
Locating Mobile Devices using Mobile Devices. In
K. Kyamakya, editor, Proceedings of 1st Workshop on
Positioning, Navigation and Communication 2004 (WPNC
04), pages 43–48. University of Hanover, 2004. Available on
http://beel.org.
[3] Jöran Beel. Project Team Rewards - Rewarding and
Motivating your Project Team. Createspace, Scotts Valley
(USA), November 2007. ISBN 978-1434816269. Available on
http://project-team-rewards.com.
[4] Jöran Beel and Bela Gipp. ePass - der neue
biomemtrische Reisepass. Shaker Verlag, Aachen (Germany),
October 2005. ISBN 9783-8322-4693-8. Available on
http://www.epass-buch.de.
[5] Jöran Beel and Bela Gipp. Collaborative Document
Evaluation: An Alternative Approach to Classic Peer Review.
In Proceedings of the 5th International Conference on Digital
Libraries (ICDL’08), volume 31, pages 410–413, Vienna
(Austria), August 2008. ISSN 1307-6884. Available on
http://www.sciplore.org.
[6] Jöran Beel and Bela Gipp. The Potential of
Collaborative Document Evaluation for Science. In George
Buchanan, Masood Masoodian, and Sally Jo Cunningham,
editors, 11th International Conference on Asia-Pacific Digital
Libraries (ICADL’08), volume 5362 of Lecture Notes in
Computer Science (LNCS), pages 375–378, Heidelberg
(Germany), December 2008. Springer. ISBN 978-3-540-
89532-9. Available on http://www.sciplore.org.
[7] Jöran Beel and Bela Gipp. Google Scholar‟s Ranking
Algorithm: An Introductory Overview. In Birger Larsen and
Jacqueline Leta, editors, Proceedings of the 12th International
Conference on Scientometrics and Informetrics (ISSI’09),
volume 1, pages 230–241, Rio de Janeiro (Brazil), July 2009.
International Society for Scientometrics and Informetrics.
ISSN 2175-1935. Available on http://www.sciplore.org.
[8] Jöran Beel and Bela Gipp. Google Scholar‟s Ranking
Algorithm: The Impact of Articles‟ Age (An Empirical Study).
In Shahram Latifi, editor, Proceedings of the 6th International
Conference on Information Technology: New Generations
(ITNG’09), pages 160–164, Las Vegas (USA), April 2009.
IEEE. ISBN 978-1424437702. Available on
http://www.sciplore.org.
[9] Jöran Beel and Bela Gipp. Google Scholar‟s Ranking
Algorithm: The Impact of Citation Counts (An Empirical
Study). In André Flory and Martine Collard, editors,
Proceedings of the 3rd IEEE International Conference on
Research Challenges in Information Science (RCIS’09), pages

439–446, Fez (Morocco), April 2009. IEEE. ISBN 978-1-
4244-2865-6. Available on http://www.sciplore.org.
[10] Jöran Beel and Bela Gipp. Link Analysis in Mind
Maps: A New Approach To Determine Document
Relatedness. In Proceedings of the Fourth International
Conference on Ubiquitous Information Management and
Communication (ICUIMC’10). ACM, January 2010. to
appear.
[11] Jöran Beel, Bela Gipp, and Christoph Müller.
‟SciPlore MindMapping‟ – A Tool for Creating Mind Maps
Combined with PDF and Reference Management. D-Lib
Magazine, 15(11), November 2009. Brief Online Article.
Alternatively available on http://www.sciplore.org.
[12] Jöran Beel, Bela Gipp, and Jan Olaf Stiller. Could
Mind Maps Be Used To Improve Academic Search Engines?
In S. I. Ao, C. Douglas, W. S. Grundfest, and J. Burgstone,
editors, International Conference on Machine Learning and
Data Analysis (ICMLDA’09), volume 2 of Lecture Notes in
Engineering and Computer Science, pages 832–834, Berkeley
(USA), October 2009. International Association of Engineers
(IAENG), Newswood Limited. ISBN 978-988-18210-2-7.
Available on http://www.sciplore.org.
[13] Jöran Beel, Bela Gipp, and Jan Olaf Stiller.
Information Retrieval on Mind Maps – What could it be good
for? In Proceedings of the 5th International Conference on
Collaborative Computing: Networking, Applications and
Worksharing (CollaborateCom’09), Washington (USA),
November 2009. IEEE.
[14] Jöran Beel, Bela Gipp, and Erik Wilde. Academic
Search Engine Optimization (ASEO): Optimizing Scholarly
Literature for Google Scholar and Co. Journal of Scholarly
Publishing, 41(2), January 2010. University of Toronto Press.
to appear.
[15] Bela Gipp and Jöran Beel. Citation Proximity
Analysis (CPA) - A new approach for identifying related work
based on Co-Citation Analysis. 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.
International Society for Scientometrics and Informetrics.
ISSN 2175-1935. Available on http://www.sciplore.org.
[16] Bela Gipp and Jöran 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, International Conference on Education
and Information Technology (ICEIT’09), volume 1 of Lecture
Notes in Engineering and Computer Science, pages 636–639,
Berkeley (USA), October 2009. International Association of
Engineers (IAENG), Newswood Limited. ISBN 978-988-
17012-6-8. Available on http://www.sciplore.org.
[17] Bela Gipp, Jöran Beel, and Christian Hentschel.
Scienstein: A Research Paper Recommender System. In
Proceedings of the International Conference on Emerging
Trends in Computing (ICETiC’09), pages 309–315,
Virudhunagar (India), January 2009. Kamaraj College of
Engineering and Technology India, IEEE. Available on
http://www.sciplore.org.
[18] Bela Gipp, Jöran Beel, and Ivo Rössling. ePassport:
The World’s New Electronic Passport. Createspace, Scotts
Valley (USA), October 2007. ISBN 978-1434823182.
Available on http://www.epassport-book.com/.