Mind Maps and Information Retrieval
University of Wolfenbüttel
Karl Scharfenberg Faculty
Abstract—Mind maps are used by millions of people. In this
paper we present how information retrieval on mind maps could
be used to enhance expert search, document summarization,
keyword based search engines, document recommender systems
and determining word relatedness. For instance, words in a mind
map could be used for creating a skill profile of the mind maps’
author and hence enhance expert search. This paper is a
research-in-progress paper which means no research results are
presented but only ideas.
Keywords-data mining, information retrieval, mind maps,
expert seach, document clustering, document classification
Mind maps were originally invented by Tony Buzan in the
1970s  and are nowadays used by millions of people for
brainstorming, note taking, project planning, decision making,
and document drafting. Many software tools exist to support
the creation of mind maps [2, 3]. The probably most popular
ones are MindManager with about 1.5 million users  and
FreeMind with about 150,000 downloads a month .
Hundreds of books and research articles were published about
how to create mind maps and about evaluating mind maps’
effectiveness, for instance, in the field of education [6-9].
MindMaps are also called Mind Maps Concept Maps or
However, to our knowledge, no research exists whether
information extracted from mind maps could be used for
enhancing other applications. We believe, it can and present
our ideas in this paper. Each of the next sections deals with
enhancing a particular application, namely
Keyword based search engines
(Document) Recommender Systems
Determining word relatedness
After presenting the ideas, the concept of information
retrieval on mind maps in general is discussed as well as future
Finding the right experts in a big company is a difficult
endeavor. In first attempts, databases were used and employees
could enter their skills manually [10, 11]. In the last decade
much research has been performed on automatically creating
skill profiles. The probably most promising approach is
analyzing documents. For instance, if a researcher has
published many documents containing the word ‘mind map’,
she probably has some expertise in the field of mind mapping.
Typical documents being analyzed are emails, visited websites,
scholarly articles and documents published in a company’s
intranet [12-16]. Mind maps have not been used so far.
Figure 1. A mind map (early draft of this paper)
A mind map (see Figure 1) seems well suited for creating a
skill profile of its author. The words in a mind map should
specify quite well the author’s expertise. In addition, nodes can
contain notes and links which could also be analyzed. In
contrast to text documents, a mind map seems likely to contain
less stop and other irrelevant words. This should facilitate the
creation of skill profiles.
III. DOCUMENT SUMMARIZATION
Search engines usually display summarized data for each
search result. This could be the document’s title, URL, or a
short extract of the document’s text. Academic search engines
additionally display data such as the author, publishing date or
the abstract (see Figure 2). This does not always deliver
satisfying results. In Figure 2, for instance, the extract is not
very informative, it equals basically the title. Alternatively to
text extracts, some researchers attempted to automatically
create abstracts [17-19] or summarizing documents based on
user generated data such as hyperlinks , social annotations
 and annotated bibliographies .
Figure 2. Example of summary data on Google Scholar
Mind maps could be used to complement summarization of
documents. Most mind mapping tools allow to link nodes in the
mind map with documents on the user’s hard drive or to link a
node to a webpage. The node’s text, and the text of parent
nodes, could be seen as a summary for the linked document.
Figure 3 illustrates this approach: The node with the red arrow
and gray background links to the PDF file of the scholarly
article ‘Are your citations clean?’ . This article deals with
problems of citation analysis. In this example the node which is
linking to the PDF and its parent nodes summarize the article’s
Citation Analysis -> Problems -> Technical Problems –>
problem: different authors with the same name
Certainly, one occurrence in a mind map would not be
sufficient for a thoroughly summary. But if several users would
link a document in several mind maps, this could add up to a
descent summary, highlighting what readers found most
relevant in the document.
Figure 3. Mind maps as document summary and for determining word
IV. KEYWORD BASED SEARCH ENGINES
When searching for documents, usually a keyword is
entered and the search engine returns those documents
containing the keyword. Various algorithms exist to calculate
how relevant a document is for a certain keyword search (e.g.
tf-idf and BM25(f)), but usually only words contained in the
documents are considered. Only few approaches consider
words of ‘neighbored’ documents
Considering neighbored documents means, document A could
be found for a keyword search even if document A does not
contain the keyword, but document B, which is linking to
document A. Usually this kind of link analysis is applied to
scholarly literature and websites. However, it seems likely that
the same concept could be applied to mind maps.
Figure 4. Enhance keyword based search engines
If a mind map links to a document, the words of the linking
(and parental) node could be assigned to the linked document.
Figure 4 illustrates this: The mind map contains a node called
‘expert search’ and child nodes link to documents related to
expert search (those with the red arrows). However, many of
these documents do not contain the term ‘expert search’, but
other expressions such as ‘expert finder’, ‘expertise
management’ or ‘skill management’. If search engines would
analyze mind maps and treat them as ‘neighbored’ documents,
recall in document retrieval could be increased.
(DOCUMENT) RECOMMENDER SYSTEMS
One common recommendation approach is to recommend
those items which are related to items a user likes (item based
recommendations). For scholarly literature and websites,
relatedness often is determined via citation analysis and
hyperlink analysis respectively. The same concept could be
applied to mind maps.
Node (No Link)
Node (No Link)
Node (No Link)
Node (No Link)
Node (No Link)
Figure 5. Expected Link Relatedness (Illustration)
The basic idea of what we call ‘Mind Map Citation
Analysis’: when two documents A and B are linked by a mind
map, document B could be recommended to those users liking
document A. This concept could be enhanced with common
citation analysis approaches. For instance, if two documents are
linked in high proximity, their relatedness can be expected to
be higher than two documents linked in lower proximity [25,
26]. Figure 5 illustrates this concept: Link 1 and 2 are in direct
proximity. Therefore, the linked documents can be expected to
be highly related. Between link 3 and 4 is a higher distance, so
their relatedness is likely to be lower.
VI. DETERMINING WORD RELATEDNESS
Knowing how words are related is important for many
applications. For instance, search engines want to determine
synonyms [27-29] and offer search query recommendations [-];
social tagging systems often recommend related tags to their
users [-]; and, among others, for web 2.0 applications, (semi)
automatic generation of ontologies is desirable [-]. Again, it
seems likely that information retrieval on mind maps could
help enhancing these applications.
A mind map is a graph and nodes are in hierarchical order. Download full-text
As such the nodes’ terms are in direct relation to each other.
For instance, in Figure 3: Based on the mind map a search
query recommender could recommend the terms ‘problems’ or
‘definition’ to someone searching for ‘citation analysis’ in
order to specify his search. Or, if a person is searching for
‘citation analysis’, then ‘peer review’ might be an interesting
search term to broaden the search.
VII. DISCUSSION AND FURTHER RESEARCH
In this paper we presented how data of mind maps could be
used to enhance expert search, document summarization,
keyword based search engines, document recommender
systems and determining word relatedness. The presented ideas
are not yet supported by research and it could turn out that data
of mind maps is not able to enhance the mentioned
applications. In addition, two more challenges exist. First, it is
unknown if a sufficient number of people create mind maps
and if they are willing to share their data. Second, the
robustness of data seems critical. All platforms analyzing data
of users do have to cope with spam and fraud as soon as they
become successful. There is no reason to assume that this
would be different if information retrieval on mind maps
As part of the SciPlore.org project we will further research
information retrieval on mind maps. Recently we developed a
special mind mapping software focusing on researchers needs
. This software will help to gather and analyze mind maps in
order to see if the here presented ideas may be realized.
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