=Paper=
{{Paper
|id=Vol-1456/paper4
|storemode=property
|title=Interactive Visualization of Large Ontology Matching Results
|pdfUrl=https://ceur-ws.org/Vol-1456/paper4.pdf
|volume=Vol-1456
|dblpUrl=https://dblp.org/rec/conf/semweb/LiSC15
}}
==Interactive Visualization of Large Ontology Matching Results==
https://ceur-ws.org/Vol-1456/paper4.pdf
Interactive Visualization of Large Ontology
Matching Results
Yiting Li, Cosmin Stroe, and Isabel F. Cruz
ADVIS Lab, Department of Computer Science, University of Illinois at Chicago, USA
yiting.star@gmail.com, cstroe@gmail.com, isabelcfcruz@gmail.com
Abstract. We add to the widely used AgreementMaker system the ca-
pability to visualize the results of matching large ontologies with a user
interface that supports navigation and search of the ontologies. The in-
terface also supports user intervention when using a feedback loop strat-
egy where users validate candidate mappings that have been computed
automatically by matching algorithms. The interface further displays
properties of the concepts to facilitate the decision process.
1 Introduction
An ontology provides a vocabulary describing a domain of interest and a spec-
ification of the meaning of terms in that vocabulary. An increasing number of
organizations are using ontologies to organize their knowledge. However, dif-
ferent ontologies exist for the same knowledge domain. To address this issue,
ontology matching is needed, which is the process of finding the relationships,
called mappings, between concepts (classes or properties) of two different on-
tologies, the source and the target ontologies [1]. Ontology matching can be
performed automatically, manually, or semi-automatically.
A variety of algorithms, which we call matchers, have been developed for
matching. For example algorithms that are based on string similarity of the class
labels or on the structure of the ontologies. Advanced ontology matching systems,
such as AgreementMaker, use combinations of a large variety of algorithms [2, 3].
In this paper, we do not focus on any particular matching algorithm, but rather
on visualizing the results of the ontology matching process so as to enable the
involvement of users with the objective of obtaining better results. The quality of
a matching algorithm or of a combination of matching algorithms is measured in
terms of precision, recall, and F-measure, by comparing the obtained mappings
with the mappings that belong to the gold standard or reference alignment. The
OAEI (Ontology Alignment Evaluation Initiative)1 makes reference alignments
available for a variety of their tracks, which is a great asset for the ontology
matching community.
The purpose of our work is twofold. First, we want an interactive visualiza-
tion method for large ontologies. Second, we want to support visual analytics
in a semi-automatic ontology matching process. We define some of these terms
next. Semi-automatic ontology matching integrates automatic and manual meth-
ods. Those mappings that are believed to be incorrect are presented to users for
1
http://oaei.ontologymatching.org/
37
Interactive Visualization of Large Ontology Matching Results
validation [4, 5]. The workflow consists of a loop where the outcome of the val-
idation step is fed back into the ontology matching process. Visual analytics is
the science of analytical reasoning supported by interactive visual interfaces [6].
In ontology matching, visual analytics can help users validate the mappings [4].
Our focus is on ontology matching visualization, not on ontology visualiza-
tion, that is, we want to support the visualization of complex relationships be-
tween source and target ontology structures. Ontology matching visualization is
further complicated when matching large ontologies. For example, the display of
an ontology as a tree structure using the JTree class can be very helpful for small
and medium size ontologies, but is less helpful for large ontologies because of
the amount of scrolling needed to locate the different mappings. To better com-
pare and analyze the matching results, a visual representation that can scale to
large or very large ontologies is needed. In this paper, we investigate interactive
visualizations that use pie charts, which naturally scale to any ontology size.
Our paper is organized as follows. In Section 2, we cover related ontology
matching visualization approaches and in particular those that are intended for
large ontologies, be they based on graphs, treemaps, or pie charts. We also cover
interactive approaches based on matrices that support visual analytics. In Sec-
tion 3, we describe our visualization technique, starting with the design criteria.
We then describe the pie chart visualization and the comparative visualization
of matching algorithms, as well as the user interface organization. In Section 4,
we describe the use of our interactive interface that supports the validation of
mappings in a feedback loop setting and its integration with AgreementMaker.
Finally, in Section 5 we draw conclusions and point to future work.
2 Related Work
A recent survey of visualization methods for ontology matching [7] establishes a
list of requirements for those systems to support user involvement. However, the
functionality of the systems that are covered fall short of those requirements.
Therefore, there is the urgent need to develop ontology matching visualization
approaches that scale to large and very large ontologies. In what follows, we
describe briefly relevant interactive visualization methods.
2.1 Cluster Visualization
The cluster representation [8] shows both detailed and general information of
matching results and provides in addition a JTree visualization. Users can select
the level at which they want to cluster the results. For the visualization of each
ontology, this approach uses a spring-embedded graph drawing algorithm. This
method is constrained by its computation complexity, which is O(n2 × s) where
n is the number of concepts in the ontology and s the number of iterations.
Other drawbacks of the approach are that only the results of a single matching
algorithm can be visualized. The concepts of each ontology are color coded so as
to show whether they have been mapped and the level of similarity found with
classes of the other ontology.
Another graph drawing representation that was developed for the Agreement-
MakerLight system [9], which extends AgreementMaker [2] to very large ontolo-
38
Interactive Visualization of Large Ontology Matching Results
gies, provides a single visualization that also uses a spring-embedded technique
where both ontologies and the mappings between classes are displayed. How-
ever, it displays few mappings at a time [10]. This technique does not allow to
compare the results of more than one matching algorithm at once.
2.2 Treemap and Pie Chart Visualizations
The Prompt+CogZ tool supports multiple visualizations, including one based
on TreeMaps and another one that displays pie charts [11]. TreeMaps have the
advantage that they can be used to visualize large amounts of data, but fit in
a small area. Forcefully, details cannot be provided for large ontologies. Some
details are provided by a pie chart view with information for each branch of
the ontology, such as the number of candidate mappings, mapped classes, and
classes that are not mapped. We note, however, that for the display of candidate
mappings, the tool falls back on a JTree-like visualization, which uses a fish-
eye view lens to allow for the display of larger ontologies. Clearly this is overall
an advanced visualization tool. However, it does not seem to be able to show
concurrent displays of more than one matching algorithm at a time. Together
with the approach we present in this paper, this is the only other tool that
supports pie charts with the difference that our pie charts drive the navigation
across all levels of the ontologies, while their navigation appears instead to be
driven by the TreeMap visualization.
2.3 Matrix Visualization
A matrix visualization where the classes of both ontologies are placed along
the X and Y axes provides a more comprehensive view of the matching pro-
cess as compared with the aforementioned methods because it allows for the
whole mapping space to be visualized with equal detail. We know of two such
visualizations: the one provided by iMerge [12] and the one provided by Agree-
mentMaker [4]. Both systems support multiple visualizations, including a tra-
ditional JTree-like visualization for each ontology with connections between the
two ontologies showing the mappings. Like the systems already mentioned, these
two systems do not scale to very large ontologies, however AgreementMaker has
the distinct capability of allowing for the comparison of different matching al-
gorithms side by side and simultaneous navigation across the various similarity
matrices. The color intensity supported by AgreementMaker, which depicts the
matching confidence score for each mapping, adds an extra dimension to the
visualization without adding extra space. Figure 1 shows the AgreementMaker
visual interface for matrix visualization, which is called Visual Analytics Panel
because it is used to support the visual analytics process. The top toolbar con-
trols the matching process. The overall panel highlights a vector of points for the
same mapping (the signature vector). Each matrix is associated with a match-
ing algorithm [4]. AlignmentVis is an interactive user interface that supports
matrices among other visualizations, using a multi-linked view paradigm [13].
However, it is not currently targeted to very large ontologies.
In conclusion, none of the above approaches provides both for scalability and
for an interactive meaningful display that supports visual analytics for large or
very large ontologies.
39
Interactive Visualization of Large Ontology Matching Results
Fig. 1: The Visual Analytics Panel of AgreementMaker [4].
.
3 Visualization
3.1 Design Criteria
We have made the following choices and present their rationale:
– We allow for ontology navigation, exploration, and searching.
– We do not display all the mappings at once. If we did so, it would be difficult
to find a visualization whose size does not depend on the number of mappings
or on the size of the ontologies involved.
– We want to focus on the mappings one level at a time and aggregate the
results for the children of the nodes at that level. As long as navigation and
searching functions are available, users can easily locate any single ontology
node in the whole structure and see the mappings they need.
– We choose a visualization based on pie charts. The reason of this choice is
that no matter how large the data size is, the pie chart requires always the
same modest area. Given this, we can display the results of more than one
matching algorithm at a time.
– When matching two classes, the most valuable information is the confidence
score found by the matching algorithm (between 0% and 100%). The visu-
alization can give priority to those mappings that maximize the confidence
score between two nodes.
– We want to make apparent the differences between matching algorithms.
– We enable user feedback acquisition.
3.2 Pie Chart Visualization
We start by describing how we visualize the information in terms of pie charts.
For each visualization there are two pie charts, one that corresponds to a concept
or node in the source ontology graph, S, called the current node, and a pie chart
corresponding to a concept or node in the target ontology graph, T . We show the
percentage of their children whose confidence score falls in a particular range.
Figure 2 shows those ranges, namely 81%-100%, 61%-80%, 41%-60%, and ≤40%.
For example, 41% of the children nodes of S have confidence scores in the range
40
Interactive Visualization of Large Ontology Matching Results
Fig. 2: User interface design that displays matching results.
Fig. 3: Two ontology subgraphs showing a mapping between nodes A and B.
81%-100%. Figure 2 also highlights a mapping between two nodes, A that is a
child of S and B that is a child of T .
Figure 3 shows schematically subgraphs of both ontologies with roots S and
T , their children, among which there are subclasses A and B, the siblings of A
and B, and their children (and grandchildren). To enable navigation along the
ontologies, users should be able to traverse the ontology vertically from a node
to its children but also horizontally from a node to its siblings.
The next question we address from the interface viewpoint is how to combine
both types of navigation. We provide a list that represents the children of a node
and a tree view that represents the siblings of the current node. The main panel
of the user interface for the initial version of the prototype is shown in Figure 4.
In the center area there are the two pie charts previously discussed.
Immediately left of the pie charts there is a list. When users click on a pie
chart slice, the list contains the ontology nodes with confidence score within the
corresponding range, sorted by the confidence score. Clicking on a node in the
list leads to an update of the pie charts, as that node becomes the current node.
41
Interactive Visualization of Large Ontology Matching Results
Fig. 4: Main panel, where Reference and sqdsq are concepts in the OAEI Bench-
mark Track.
The leftmost part of the interface contains the tree view. It shows all the siblings
of the current node. When clicking on a node in the tree view, both the pie charts
and the lists are updated, reflecting the change of the current node. On the top
right there is a search box. Upon entering the name of the node in the search
box, the left pie chart displays that source node and the right pie chart displays
the target node that matches the source node. Accordingly, the tree and list view
on the left are updated as well. In addition, for an easier navigation we provide
additional functions such as “go to the top level”, “go to the previous level”,
and “switch between class and property”.
3.3 Algorithm Comparison
To compare the results of two matching algorithms, we need to visualize their
results at the same time. We have therefore upgraded our user interface panel
to load multiple results as shown in Figure 5. We are making full use of the
containers in JavaFX to manage the visual elements within the available space.
We use two tile panels to display two ontology pairs. The upper panel is the
main panel (colored green) and the lower one is the sub panel (colored yellow).
The tree view shows the siblings of the source node in the main panel and the
list view shows the children of that source node. Figure 6 displays the schematic
representation of the user interface, including the flow panel. The flow panel
shows the algorithms we have loaded into the application. In this example, the
main panel shows the results of the algorithm we loaded using the second button
of the flow panel. When selected, the second button is colored green, so as to
provide a color match with the main panel.
Users can choose any algorithm for the main panel or for the sub panel. The
difference between the two panels is that all lists get updated according to the
changes to the main panel.
42
Interactive Visualization of Large Ontology Matching Results
Fig. 5: Upgraded user interface to display multiple algorithm results.
Fig. 6: Schematic representation of the upgraded user interface.
When the users click on a slice of the main pie chart, all features including
all pie charts and lists are updated. To change the matching algorithm for the
main panel, users only have to select another algorithm from the flow panel.
43
Interactive Visualization of Large Ontology Matching Results
4 Interactive Ontology Matching
We list here our objectives for an interactive mechanism for matching ontologies
that can assist users in a semi-automatic ontology matching process, where users
provide feedback:
– Show candidate mappings for validation to the users; candidate mappings
are determined automatically using quality measures [4, 14, 15].
– Register the validation choices made by the users.
– Allow for class and property navigation to assist users in their validation
decisions.
– Allow users to search for a specific class and navigate through the classes.
– Support the creation of new mappings that are missed by the automatic
process.
4.1 Interactive Workflow
Because automatic matching methods do not always provide complete or correct
mappings, the combination of user validation with the automatic methods can
lead to better results than the automatic methods alone.
The interactive workflow is shown in Figure 7. It shows a “user feedback loop”
(UFL) strategy [14, 15] integrated with the visual analytics (VA) approach, in
that the results provided by the user are fed back into the matching process and
the user is helped by the interactive user interface [4]. In the figure, the visual
Key
Start Navigate
Visualize Pie mappings,
Charts compare Start UFL No AM UFL PC MS
(PC) algorithms and
properties
Yes
Load
ontologies
Visualize trees
and perform (Re)Assign
Make
matching with Yes user
determination
AgreementMaker Open confidence
(AM) Perform
feedback
Close No Mapping
panel with
No Selection (MS)
mappings
No
Finish Yes
matching Navigate
mappings,
End compare Save No
algorithms and
Yes properties
Yes
(Re)Rank
Compare
candidates for
confidence
user validation
scores across
concepts
Fig. 7: Workflow of the interactive process.
44
Interactive Visualization of Large Ontology Matching Results
interfaces provided by AgreementMaker and the new interface communicate, so
as to allow for complementary views. The user interface that is used to perform
the mapping selection is an important component, which we describe next.
4.2 User Interface for Mapping Selection
The user interface for the mapping selection must display one or more mappings
to be validated by the users. When it shows more than one mapping, users are
asked to choose among them. Navigation using the main user interface (Fig-
ure 5) provides the confidence scores to assist users in making their selection.
Figure 8a shows the schematic representation of the interface and Figure 8b
shows a snapshot of its implementation.
(a) Schematic representation.
(b) Mapping selection, where users input their
level of confidence (0-30-60-90).
Fig. 8: User interface.
4.3 Property Comparison
Automatic algorithms match classes according to a variety of lexical, syntactic,
and structural criteria [2]. In addition, they may use other criteria, which can
be incorporated into the automatic algorithms or visualized and presented to
the users. For example, the properties associated with the classes can be con-
sidered [16]. In our user feedback loop strategy when allowing users to choose
among mappings, we present the properties associated with the classes. Our in-
terface displays both the confidence scores and floating panels that display the
properties of each of the classes, as shown in Figure 9. We note that we only
display once the panel associated with the source concept, ConferenceEvent.
4.4 Integration with AgreementMaker
Using the AgreementMaker system, the source and target ontologies are visual-
ized side by side using a tree paradigm as shown in Figure 10. The control panel
45
Interactive Visualization of Large Ontology Matching Results
Fig. 9: Property clustering.
(at the bottom of the interface) allows for users to run a variety of matchers.
In this example, two automatic matchers have been activated (the Paramet-
ric String Matcher and the Vector-based Multi-word Matcher) in addition to
manual matching. The picture depicts the display of the two ontologies and the
mappings obtained in this way.
Every set of mappings in AgreementMaker is represented by the Matching-
Task class. The MatchingTask class contains the following elements: a matcher,
its associated parameters (e.g., the confidence score threshold), and the map-
pings produced by the execution of the matcher. To open our visualization sys-
tem, users can select the Pie Chart Visualization tab of the drop down menu, as
shown in Figure 10. The key point in the integration of the pie chart visualiza-
tion with AgreementMaker is that we pass all the MatchingTask instances from
AgreementMaker to the pie chart visualization. After selecting the Pie Chart
Visualization tab, another tab shows up and the pie charts will be initialized
automatically (see Figure 5). Users can easily switch between the tree and pie
chart visualizations by clicking on the available tabs at the top of the display.
5 Conclusions and Future Work
We devised a visualization tool for large ontology matching that integrates seam-
lessly with the widely used AgreementMaker system. It supports advanced navi-
gation, interaction, and analysis and decision making features. In particular, for
navigation, our tool supports the visual representation of the source and target
ontologies and mappings between classes in those ontologies. It allows for the
46
Interactive Visualization of Large Ontology Matching Results
Fig. 10: Integration with AgreementMaker.
detailed access to pairs of classes to match, while providing ready access to other
parts of the ontologies. Our tool displays confidence scores between classes and
provides an overview of the confidence scores for the children of those classes.
For interaction, our tool supports user-driven navigation of classes and prop-
erties, the ability to search for a specific class, and to traverse the ontologies
vertically (children of a class) and horizontally (siblings of a class).
Finally, for analysis and decision making, our tool displays several possible
mappings to the users, so that they can choose among them, as part of a user
feedback loop strategy that combines automatic with manual matching methods.
Clearly, there are several directions for future work. The first one is that
we would like to extend the comparison of matching algorithms to more than
two at a time. It may be the case that no single visualization strategy works
separately, especially for very large ontologies. AgreementMaker already provides
several different strategies [4, 2, 13]. Experiments would be needed to determine
the usability and effectiveness of the different strategies when used separately or
in coordination with one another.
Acknowledgments
This research was partially supported by NSF Awards IIS-1143926, IIS-1213013,
and CCF-1331800. We would like to thank one of the anonymous reviewers,
whose questions helped improve the final version of the paper.
47
Interactive Visualization of Large Ontology Matching Results
References
1. Euzenat, J., Shvaiko, P.: Ontology Matching. Springer-Verlag, Heidelberg (DE)
(2007)
2. Cruz, I.F., Palandri Antonelli, F., Stroe, C.: AgreementMaker: Efficient Matching
for Large Real-World Schemas and Ontologies. PVLDB 2(2) (2009) 1586–1589
3. Cruz, I.F., Palandri Antonelli, F., Stroe, C.: Efficient Selection of Mappings and
Automatic Quality-driven Combination of Matching Methods. In: ISWC Interna-
tional Workshop on Ontology Matching (OM). Volume 551 of CEUR Workshop
Proceedings. (2009) 49–60
4. Cruz, I.F., Stroe, C., Palmonari, M.: Interactive User Feedback in Ontology Match-
ing Using Signature Vectors. In: IEEE International Conference on Data Engineer-
ing (ICDE). (2012) 1321–1324
5. Shi, F., Li, J., Tang, J., Xie, G., Li, H.: Actively Learning Ontology Matching
via User Interaction. In: International Semantic Web Conference (ISWC). Volume
5823 of Lecture Notes in Computer Science., Springer (2009) 585–600
6. Bertini, E., Lalanne, D.: Investigating and Reflecting on the Integration of Auto-
matic Data Analysis and Visualization in Knowledge Discovery. SIGKDD Explo-
rations Newsletter 11(2) (May 2010) 9–18
7. Ivanova, V., Lambrix, P.: User Involvement for Large-Scale Ontology Alignment.
In: International Workshop on Visualizations and User Interfaces for Knowledge
Engineering and Linked Data Analytics. (2014)
8. Lanzenberger, M., Sampson, J.: AlViz - A Tool for Visual Ontology Alignment.
In: Conference on Information Visualization (IV), IEEE Computer Society (2006)
430–440
9. Faria, D., Pesquita, C., Santos, E., Palmonari, M., Cruz, I.F., Couto, F.M.: The
AgreementMakerLight ontology matching system. In: International Conference on
Ontologies, DataBases, and Applications of Semantics (ODBASE), Springer (2013)
527–541
10. Pesquita, C., Faria, D., Santos, E., Neefs, J.M., Couto, F.M.: Towards Visualizing
the Alignment of Large Biomedical Ontologies. In: Data Integration in the Life
Sciences (DILS). (2014)
11. Falconer, S.M., Storey, M.A.D.: A Cognitive Support Framework for Ontology
Mapping. In: International Semantic Web Conference/Asian Semantic Web Con-
ference (ISWC/ASWC). Volume 4825 of Lecture Notes in Computer Science.,
Springer (2007) 114–127
12. El Jerroudi, Z., Ziegler, J.: iMERGE: Interactive Ontology Merging (poster). In:
International Conference on Knowledge Engineering and Knowledge Management
Knowledge Patterns (EKAW). (2008)
13. Aurisano, J., Nanavaty, A., Cruz, I.F.: Visual Analytics for Ontology Matching
Using Multi-Linked Views. In: ISWC International Workshop on Visualizations
and User Interfaces for Ontologies and Linked Data (Voila!). (2015)
14. Cruz, I.F., Loprete, F., Palmonari, M., Stroe, C., Taheri, A.: Pay-As-You-Go
Multi-User Feedback Model for Ontology Matching. In: International Conference
on Knowledge Engineering and Knowledge Management (EKAW). Springer (2014)
80–96
15. Cruz, I.F., Loprete, F., Palmonari, M., Stroe, C., Taheri, A.: Quality-Based Model
for Effective and Robust Multi-User Pay-As-You-Go Ontology Matching. In: Se-
mantic Web Journal. IOS Press (2015)
16. Hu, W., Qu, Y.: Falcon-AO: A Practical Ontology Matching System. Web Seman-
tics: Science, Services and Agents on the World Wide Web 6(3) (2008) 237–239
48