=Paper=
{{Paper
|id=Vol-1311/paper1
|storemode=property
|title=Dewey Decimal Classification Based Concept Visualization for Information Retrieval
|pdfUrl=https://ceur-ws.org/Vol-1311/paper1.pdf
|volume=Vol-1311
}}
==Dewey Decimal Classification Based Concept Visualization for Information Retrieval==
https://ceur-ws.org/Vol-1311/paper1.pdf
Dewey Decimal Classification Based Concept
Visualization for Information Retrieval
Jae-wook Ahn, Xia Lin, and Michael Khoo
College of Computing and Informatics
Drexel University, Philadelphia, PA USA
{jaewook.ahn,linx,mjk326}@drexel.edu
Abstract. Visual knowledge maps utilizing concepts have great poten-
tial to support interactive information retrieval. Unlike keyword-based
visual information retrieval, concept-based knowledge maps can make
the visualization easier to comprehend and manipulate. In this paper,
we introduce our novel visual search interface based on Dewey Decimal
Classification concept annotations. The web browser based interface vi-
sualizes search results initialized from user queries. Main functions of the
interface include interactive manipulation, exploration, and filtering of
concepts and links in different levels from overview to details. The vi-
sualization connects related concepts not apparent in conventional tree-
like hierarchical representations and it can promote discovery of novel
concepts during the visual exploration of search space. A real use-case
scenario is presented to highlight the advantages of the approach.
Keywords: Concept Visualization, Knowledge Map, Dewey Decimal
Classification
1 Introduction
Dewey Decimal Classification (DDC) is a popular document classification sys-
tem. It has been extensively employed by many traditional libraries to provide
users with effective way to browse and search for library resources. It supports
a multi-level hierarchy of concept classes that can be used to express the asso-
ciations and hierarchical relationships of the entire set of resources within any
given library. The resource classified with DDC is given a number composed of
three or more digits (class, division, and section) that describe the nature of the
resource from broader to narrower categories. It is perceived as an efficient way
to organize not only in the traditional library settings but also in the modern
networked settings [8].
On the other hand, knowledge structure visualization has been actively stud-
ied for creating knowledge maps to support interactive search of internet re-
sources. Various approaches have been studied in the literature to present the
actual knowledge structure as precise as possible [7]: (1) visualize existing knowl-
edge structures such as tree-like hierarchies (e.g., TreeMap [4]) or ontologies (e.g.,
2 J. Ahn, X. Lin, and M. Khoo
User
query
Calculate
Extract DDC similarity
Retrieve top Create a graph
Solr index from top N between DDC Visualize
N docs (DDC as nodes)
docs (doc co-
occurrence)
Fig. 1. DDC-based search visualization process
OntoViz [10]), (2) visualize knowledge structures that need to be extracted and
learned using various text mining techniques such as automatic thesaurus con-
struction [2] or clustering multi-word terms [9], and (3) visualize knowledge
structures through visual metaphors (e.g., [6]).
This paper introduces our novel method to exploit DDC for visualizing knowl-
edge structure of search results. It makes use of existing knowledge structure
dynamically derived from live search against three digital library resources. We
define four design goals for the knowledge structure visualization: (1) visual-
ize the overview of topics in the search results, (2) visualize concept groups or
clusters within the visualization, (3) use DDC to represent concepts, and (4)
support discovery of new knowledge using the DDC and visualization. In the
following, we will discuss how these goals are achieved from a real use-case of
the implemented visualization system.
2 Visualizing Concepts using DDC
The visualization task of this study is based on the Dewey Decimal Classifi-
cation1 classes assigned to a large number of digital library records. 263,550
records were collected from three digital libraries (Internet Public Library, In-
tute, and NSDL)2 and weighted keywords form their title, description, and sub-
ject metadata were used. The resources were matched to multiple DDC classes
by calculating the similarity between the resource keyword vectors and the DDC
description keyword vectors [5]. Therefore the 263,550 digital library resources
were assigned with multiple DDC classes that represent the relevant concepts of
the content in three levels – class, division, and section. For example, a web site
stored in one of the participating digital libraries is titled as “Olympic History”
and presents information about the history of Olympic games. From the auto-
matic DDC class assignment system [5], it was given 10 DDC classes: 796, 943,
945, 942, 949, 941, 940, 948, 944, and 947. The first digits (classes) 7 and 9 repre-
sent “Arts & recreation” and “History & geography” respectively. It is clear that
1
http://dewey.info
2
http://www.ipl.org, http://intute.ac.uk, http://nsdl.org
Concept Visualization Based Information Retrieval 3
the resource is understood by the system that it is about sports (i.e., recreation)
and history. The remaining two digits (divisions and sections) specifies the de-
tailed topics. For example, the DDC class 796 is labeled as “Athletic & outdoor
sports & games,” which combines three concepts hierarchically: 7 (class: Arts &
recreation), 9 (division: History & geograph), and 6 (section: Technology).
2.1 Visualization Process
By using the DDC classes assigned to the 263,550 records, we implemented
a search system that visualizes knowledge structure and concept relationships
within search results. Figure 1 depicts the visualization process. We indexed all
the documents using Apache Solr information retrieval system3 so that users
can instantly retrieve documents that match their queries. From the retrieved
documents, top N documents are selected to calculate the DDC relationships
related to them. Each document was assigned with 10 DDC classes following
the procedure describe above. Therefore maximum N * 10 (less than N * 10 due
to duplicates) DDC classes are retrieved from the database. Then the similarity
values between all the DDC class pairs are calculated. Jaccard coefficient is used
to calculate the similarity between the DDC class pairs by counting the number
of documents that the DDC classes are assigned to.
|ClassA Doc ∩ ClassB Doc|
SimJaccard (ClassA , ClassB ) = (1)
|ClassA Doc ∪ ClassB Doc|
Finally a graph is constructed by connecting the DDC classes (nodes) that
have higher similarity than a threshold. Similar DDC classes are connected
within the graph (links). The resulting graph is visualized in the live search in-
terface using a JavaScript-based network visualization library called Sigma js4 .
ForceAtlas2 [3] force-directed placement graph layout algorithm [1] is used to
calculate node locations and the shape of the entire graph.
Figure 2 shows an overview visualization of 694 DDC classes extracted from
all documents in the collection. In Figure 3 a user enters a query “olympic AND
history” to search for documents that contain both keywords. Among the search
results 168 ‘unique’ DDC classes associated with them are visualized. The nodes
(discs) represents the DDC 168 classes and the similar nodes (inter-similarity is
above a threshold) are linked by curves to each other. The node color reflects
the DDC class (first digit) and the legend is located on the left of the screen.
Because of the links, groups of similar nodes are clustered together and it can
be easily seen that same class nodes (same colors) are forming clusters. For
example, there is a large cluster at the top of the screen and is about “social
science (DDC=300).” All the search, DDC class retrieval, graph calculation, and
visualization functions are performed on the fly when a query is entered.
3
http://lucene.apache.org/solr/
4
http://sigmajs.org
4 J. Ahn, X. Lin, and M. Khoo
Fig. 2. Overview visualization showing DDC ‘class’ level distribution of concepts for
all 263,550 documents in the collection. 694 classes (nodes) are extracted from the
documents and linked to each other based on similarity.
2.2 Additional Visualization Features for Concept Exploration
There are visual features in addition to the basic concept visualization feature
to enable more efficient DDC-based search and exploration.
Fig. 3. Overview visualization showing DDC ‘class’ level distribution of concepts for
the query ‘olympic AND history’. Overall, history (900-purple), social science (300-red),
technology (600-green), and arts (700-light purple) are prevailing. From the division
level adaptive legend (top-left) history of two continents (North America and Europe),
and ‘Recreational Arts’ topics are recommended.
Concept Visualization Based Information Retrieval 5
Interactive Dynamic Visualization. By using a mouse, users can pan the
graph across the screen and zoom in or out to see the details or the overview of
the graph. If one clicks on a node a list of documents associated to the concept
is displayed (Figure 5).
Adaptive Concept Legend: DDC class-division level. Above the static
DDC class legend that show 10 DDC classes and associated colors there is
an adaptive DDC class-division legend that is automatically updated follow-
ing user’s panning and zooming action. It counts the most frequent DDC class-
division pairs (e.g., DDC=94* with color code purple-pink) of the nodes currently
displayed on the screen. As the distribution of the DDC class-division pairs can
change following the panning and zooming action it shows dynamic information
about the area that the user is currently examining (Figure 5).
Filtering classes from legend. If a user clicks on a DDC class and division
from the legend, the system highlights the nodes that has the class and the
division in red color. It helps users to search for specific group of concepts in a
larger concept map and enables targeted examination of concepts (Figure 4).
2.3 DDC-based Visual Search Scenario – Search for “Olympic
History”
The design principles of this system is to promote search and browsing by in-
corporating DDC concepts within a visual search environment. More specifically
the following design goals are illustrated in an example search in this section.
1. Visually show the overview of concepts included in a search result.
2. Help users search for specific concepts or concept groups easily within the
visualization.
3. Help users find relevant documents by examining the DDC concepts or con-
cept groups mapped in the visualization.
4. Support users discover new information by following links between concept
groups. The concept groups are formed by linking homogeneous concepts but
heterogeneous groups are inter-linked as well.
We will show how these goals are achieved in a real use-case information
retrieval scenario. Suppose a user was assigned a task to find out as many DDC
classes and documents as possible to write a report about “Olympic history.”
Using the search interface, she enters a query “olympic AND history.” The query
is immediately transmitted to the backend Solr index and retrieves 103 docu-
ments from the entire collection (236,550 documents). From the 103 documents
168 DDC classes are identified by looking up the database that stores the pre-
calculated DDC classes for all documents in the dataset. The system then cal-
culates the Jaccard similarity values of 5,253 DDC class pairs (168 * 167 / 2) by
calculating the co-occurrence of the documents they are assigned to. The DDC
6 J. Ahn, X. Lin, and M. Khoo
classes with higher similarity values above a specific threshold are linked and
creates a graph (Figure 3).
As described before the nodes are color-coded by their DDC classes and the
clusters are easily identified from the map (Goal 1 and 2). By clicking on the
adaptive legend (top-left of the screen) the selected DDC class-division (first and
second digits) classes are highlighted in the visualization. In Figure 4, the adap-
tive legend shows that the most frequent DDC class-division pairs are 970:His-
tory of North America, 940:History of Europe, and so forth. A user clicks on
DDC 940 then the nodes starting with 94 are all highlighted in red and the
other nodes are de-highlighted. A node directly connected to the highlighted
cluster is not de-highlighted and retains its original color (purple, DDC=936),
which is a potentially relevant DDC class worth further examination (Goal 3).
Fig. 4. From the legend, any topic can be selected and highlighted within the map.
In this example 940 – History of Europe is selected and the corresponding nodes are
highlighted (in red). Remaining nodes are dimmed. Using this feature users can easily
select concepts and discover how they are connected to each other.
The visualization can recommend novel concepts that are difficult to discover
using conventional search and browsing methods. Figure 5 shows the new dis-
covery of concepts that are relatively further in the DDC hierarchy. It zooms
into the DDC=790: Recreation and performing arts area in the map, which is
intuitive enough to anticipate that sports related information should be under
this category. It shows 10 DDC=790 classes (DDC=790, 791, 792, 793, etc. in red
color) that the user might have intended to examine in the first place. In addition
Concept Visualization Based Information Retrieval 7
to them there are several connected classes that are not under the 790 category.
The system can lead users to examine DDC=600 concepts (i.e., 613, 617, and
636 in green) which are under the broader technology classes (DDC=600) but
connected to the initial 790 classes. By examining the documents under one of
the 600 concepts (Figure 5) it can be verified that one of the sites “SR: Olympic
Sports” contains relevant information about Olympic games statistics and his-
tory. Because DDC=613 is under the technology category (DDC=600) that may
be seemingly unrelated to the search task, it may be challenging for a user to be
motivated to examine the category. Only after starting the visual exploration of
the chains of DDC classes from easier ones (i.e., 700: Recreation), more difficult
and novel categories can be discovered (Goal 4).
Fig. 5. Zoomed into ’790: Recreation and performing arts’ and their neighbor area (top-
right from the overview visualization). The legend is updated accordingly and shows
new clusters of concepts. There are three Technology (600) related nodes (green) and
the documents about the concepts can be examined by clicking on the node. It discovers
a new document “SR: Olympic Sports” that may be relevant to the task. By clicking
on the document title one can open the website and can confirm it contains Olympic
related statistics and history.
3 Conclusions
In this paper we introduced a visual information retrieval approach based on
DDC classification. We annotated 263,550 records from three digital libraries
with automatically generated DDC classes and implemented a information re-
trieval system featuring a graph visualization that connects similar DDC con-
cepts based on the document co-occurrence between them. We showed the ad-
vantages of our approach by a real use-case scenario. The example demonstrated
8 J. Ahn, X. Lin, and M. Khoo
that the approach could support interactive and dynamic knowledge visualiza-
tion and could promote the discovery of concept clusters and unknown concepts.
Our future research plans include a full-fledged user study to learn about the
advantages and disadvantages of the approach from real users.
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