With the increase in Web of Data (WOD) many new challenges regarding exploration, interaction, analysis and discovery have surfaced. One of the basic building blocks of data analysis is classi cation. Many studies have been conducted concerning Formal Concept Analysis (FCA) and its variants over WOD. But one fundamental question is, after these concept lattices are obtained on top of WOD, how the user can interactively explore and analyze this data through concept lattices. To achieve this goal, we introduce a new tool called LatViz, which implements several algorithms for constructing concept lattices and allows further navigation over lattice structure. LatViz proposes some remarkable improvements over existing tools and introduces various new functionalities such as interaction with expert, visualization of Pattern Structures, AOC posets, concept annotations, ltering concept lattice based on several criteria and nally, an intuitive visualization of implications. This way the user can e ectively perform an interactive exploration over a concept lattice which is a basis for a strong user interaction with WOD for data analysis.
In the last decade, there has been a huge shift from the Web of Documents
to the Web of Data (WOD). Web of Documents represents data in the form of
HTML pages which are linked with other HTML pages through hyperlinks. This
web of documents has evolved into WOD where all the information contained
is represented in the form of entities and relations allowing the semantics to be
embedded in the representation of these data. These data are in the form of
a (node-arc) labeled graph belonging to several domains such as newspapers,
publications, biomedical data etc. The growth in the publication of data sources
in WOD has made it an important source of data, which has led towards many
challenges pertaining to e ective utilization of this data. WOD mainly represents
data in the form of Resource Description Framework (RDF)1. There are several
1 http://www.w3.org/RDF/
ways such as data dumps and SPARQL queries to access these data, which can
be utilized for several applications, one of which is visualization and interactive
exploration for data analysis purposes. Several visualization tools have been
developed for this purpose, one of which is LODLive2 [
In order to provide the user with the ability to perform data analysis and
knowledge discovery over such kind of data, there is a need to perform
classication, where the obtained classes are further made available to the user for
exploration and subjective interpretation. In the current study we use Formal
Concept Analysis as the basis for classi cation. Several studies have already been
conducted using FCA and its variants over RDF graphs or its generalization to
knowledge graphs. Out of these studies so far RV-Xplorer [
The paper is structured as follows: Section 2 introduces a motivating example. Section 3 introduces some of the important functionalities of LatViz, while in Section 4, we discuss some of the related tools already developed and nally Section 5 details the future perspectives of the current work. 2
Let us consider that an expert is searching for papers published by a particular team in conferences or journals related to his/her eld of research. In order to locate the papers of his/her interest (s)he has to search for speci c keywords or authors in the local portal. For getting the view of which kind of papers are contained (s)he has to run a broad query and then narrow down his/her
query to obtain papers on speci c keywords or authors or group of keywords or authors. The expert will end up running several queries to get what (s)he wants. Moreover, if the expert wants to know the collaborations of the team with other members of the research community outside the team, as well as the diversity and the specialization of the team members, this cannot be directly obtained by simple querying. To obtain such kind of knowledge there is a need to introduce a support for data analysis. Based on this scenario, we show how the expert can be guided thanks to an adapted visualization tool to obtain such information of interest with the help of concept lattices. 3 3.1
LatViz for Interactive Exploration of Concept Lattices
The display of LatViz resembles Conexp3, which provides basic functionalities
for building a concept lattice. LatViz implements two algorithms for building a
concept lattice from a binary context, one of which is introduced in [
The concept lattice for the scenario in section 2 was created by mapping the RDF data to a formal context K pG; M; Iq. The subjects of the triples were considered as the set of objects G, the objects in the RDF triples i.e., keywords and authors were considered as the set of attributes M . In this example, the RDF triples were created from the publications of the Knowledge Discovery (KDD) team in LORIA. The number of objects in the context are 343 and attributes are 1516. Very often huge concept lattices are obtained based on the context size. LatViz provides several interactive operations allowing for reduction of exploration space of the expert. To-date this is the most interactive tool having many unique functionalities such as handling numeric data with the help of interval pattern structures, AOC-posets, ltering concept lattice and implications which provides support for data analysis. Other functionalities such as annotating the lattice, level-wise display of a concept lattice etc. are discussed in many contexts but are not yet directly implemented in the commonly used tools. In the following we detail each of these functionalities for data analysis. 3.2
AOC-poset is a partially ordered set of the attribute and object concepts, rst
introduced in [
AOC-Posets actually reduce exploration space but still a huge number of concepts remain to be observed. LatViz allows the creation of concept lattice levelwise by interaction. When an expert clicks on the top concept, LatViz computes and displays the rst level. After that the expert can select the concept for continuing the exploration, then LatViz computes the next level for that concept. In Figure 2, the top image shows the rst level of the concept lattice built by selecting the top concept. Then the expert can view the contents of each concept on mouseover. In the running example, expert locates the concept with all the papers of Amedeo Napoli (i.e., K#2), which shows that the total number of documents written by Amedeo Napoli are 152. On selecting this concept, the next level of the lattice originating from the selected concept is computed (shown in the bottom image in Figure 2). 3.4
In case of huge concept lattices sometimes it is hard to keep track of the ordering relations between the concepts. LatViz allows the expert to only highlight sub/super-concepts of a concept. For example, if the expert wants to display all the publications along with the collaborations of the author Amedeo Napoli, (s)he can highlight the associated sub-lattice of the attribute concept of \Amedeo Napoli". Figure 3 shows the highlighted sub-lattice in brown. An expert can highlight the super-concepts connected to a concept. If the expert is looking for all the papers having some keywords common with the paper Knowledge Organization and Information Retrieval Using Galois Lattices having one or more of the keywords in the intent of the concept then (s)he can highlight the sub-lattice of super-concepts associated to it (see Figure 4). 3.5
This functionality was partially implemented in RV-Xplorer [
There are two categories of ltering provided by LatViz; one is for the concept
lattice created with the binary data and the other one is provided for the pattern
concept lattice built with the help of interval pattern structures.
Filtering Concept Lattice. After a concept lattice is built by applying FCA,
expert is allowed to set several ltering criteria such as stability, lift, extent size,
intent size and nally speci c object or attribute names. Let us consider that in
the running example, the expert is looking for the papers published by Amedeo
Napoli on the topic of pattern structures and FCA. A lter on the number of
attributes in the intent is set to 3. The ltered concept lattice obtained over the
complete lattice is shown in Figure 8. It further shows the authors with who
Amedeo Napoli has worked i.e., Sergei O. Kuznetsov and Mehdi Kaytoue. This
part of concept lattice shows the community of authors working with Amedeo
Napoli on the topic of pattern structures.
4 http://portal.core.edu.au/conf-ranks/
Filtering Pattern Concept Lattice. Interval Pattern Concept Lattices can also
be ltered by specifying the number of attributes to be considered, the upper
and the lower limits for the intervals in the intent of each attribute along with
stability, lift and extent size. Let us consider the pattern concept lattice in
Figure 7 which is hard to interpret. To make it more readable based on what an
expert wants, (s)he is allowed to specify lters. For example, if the expert is
looking for a paper published in a conference of a rank 1-4 in the year 2012
2015 and has the number of pages not less than 2 and no more than 42 then
the respective lters can be set for the values of all three attributes. The
ltered pattern concept lattice will then only contain the part of lattice needed by
the user. Figure 7 shows the concept containing group of papers published from
2014-2015 in conferences with rank 2 having number of pages 2-42.
One of the many proposed visualization techniques for implications includes
table-based views. The columns in the table represent rule ID, LHS and RHS
of the rule, support and con dence measures. These views were used because
of the simplicity of storage. However, as the number of rules can be too many
it is not very evident for the expert to focus on interesting rules at a simple
glance. Another way of visualizing association rules are Matrix Views, where
rows represent the LHS and columns represent the RHS of the rules. Support
and con dence are displayed by di erent colors in the intersection of the LHS
and RHS. In case of a formal context, the number of objects/attributes can
be very big leading to problems in displaying the matrix. By carefully taking
into account the above drawbacks, we nally settle on visualizing implications
with the help of scatter plots, where the x-axis shows the increasing support
and the y-axis shows the increasing lift (as we are considering implications the
con dence of the rule is always 100%). Such kind of display helps the expert
to single-out the rules (s)he wants to visualize based on the values of support
and lift. Figure 10 shows implications of the running example, x-axis keeps the
support in percentage and y-axis keeps lift. The number on top of the circle
shows the number of rules existing in the same point in the plot. On mouse over,
expert can view the implications.
In [
LatViz is a tool built for allowing expert interaction for data analysis
purposes. It provides many new functionalities for reducing the exploration space of
the expert and enable him to interpret the results. As a future perspective, we
also want to implement other variations of pattern structures such as Pattern
Structures introduced for structured set of attributes discussed in [