=Paper=
{{Paper
|id=Vol-2094/paper2
|storemode=property
|title=VisTA: Visual Terminology Alignment Tool for Factual Knowledge Aggregation
|pdfUrl=https://ceur-ws.org/Vol-2094/paper2.pdf
|volume=Vol-2094
|authors=Anastasia Axaridou,Konstantina Konsolaki,Maria Theodoridou,Artem Kozlov,Peter Haase,Martin Doerr
|dblpUrl=https://dblp.org/rec/conf/esws/AxaridouKTK0D18
}}
==VisTA: Visual Terminology Alignment Tool for Factual Knowledge Aggregation==
https://ceur-ws.org/Vol-2094/paper2.pdf
VisTA: Visual Terminology Alignment Tool for Factual
Knowledge Aggregation
Anastasia Axaridou1[0000-0002-7598-111X], Konstantina Konsolaki1[0000-0001-8002-3250], Maria
Theodoridou1[0000-0002-4623-9186], Artem Kozlov2[0000-0002-8666-4347], Peter Haase2[0000-0002-
7561-7000]
and Martin Doerr1[0000-0002-6006-0844]
1
Institute of Computer Science, FORTH, Heraklion, Greece
{axaridou, konsolak, maria, martin}@ics.forth.gr
2
metaphacts GmbH, Walldorf, Germany
{ak, ph}@metaphacts.com
Abstract. The alignment of terminologies can be considered as a kind of “trans-
lation” between two or more terminologies that aims to enhance the communi-
cation among people coming from different domains or expertise. In this paper
we introduce the Visual Terminology Alignment tool (VisTA) that enables the
exact alignment for RDF/SKOS-like terminologies, suitable for integrating
knowledge bases, rather than information retrieval systems. The tool provides a
simple and friendly web-based user interface for the alignment between two
terminologies, while it visualizes the terminology hierarchies, enables the inter-
active alignment process, and presents the alignment result. The latter is a na-
tive RDF/SKOS graph that interconnects the two terminology graphs, support-
ing interoperability and extending the search capabilities over the integrated
semantic graph, using the broader and exact match properties.
Keywords: Terminology Alignment Tool, Thesaurus, Visual, Interactive, Exact
Matching
1 Introduction
The alignment of terminologies, also referred to as thesauri mapping, can be consid-
ered as a kind of “translation” of the terms between two or more terminologies in
order to support the communication among individuals, teams and communities com-
ing from different scientific, cultural, technical or other domains. What is actually
achieved by alignment is the integration of knowledge towards the completeness of
expressivity and a better understanding of the entities used in multiple fields of life
and science. In our approach Terminology, is a SKOS1-like description of universals
(classes, types, etc.) [1], rather than of individual items (e.g. persons and places),
employing the SKOS properties of broader and exact match.
The process of alignment is not an easy work. Several terms may be used for a
concept, as well as different concepts may be expressed by the same term. Further-
1
https://www.w3.org/2004/02/skos/
2
more, actual generalization and specialization hierarchies frequently break the seman-
tics, inserting narrower terms incompatible with the meaning of the broader term,
such as natural phenomena appearing under Activities (see example in section 3).
Things are getting worse when large vocabularies are involved and consequently
those situations can be treated by approximate matching methods producing inaccu-
rate alignment results that cannot be totally trusted. Such results always suffer from
their level of precision and recall containing an amount of erroneous or missing
matches that in the end can be resolved only by the human intervention. A deep anal-
ysis in [2] shows ways in which thesaurus creators can improve their methodology to
meet the challenges of networked access of distributed collections.
In this paper we are proposing the support of the principles of concept-based [2]
alignment with a Visual Terminology Alignment tool (for short VisTA). VisTA aims
to help the users to work on the intellectual handling of the alignment between two
terminologies based on two essential hierarchy relationships: broader-narrower and
exact match. We don’t deal with inexact match, which works well with information
retrieval but not with logical queries, and does not preserve recall and generalization
semantics required in knowledge bases. In need of an exact alignment solution,
VisTA is ideal for the alignment of small to medium sized terminologies and may
also scale up to larger cases when running on sufficiently powerful machines.
VisTA is a module that conforms to a respective component foreseen by the Syn-
ergy Reference Model2, an initiative of the CIDOC CRM Special Interest Group 3, for
manipulation of data provisioning and aggregation processes, aiming at defining the
processes needed to be executed or maintained between a data provider (the source)
and a data aggregator (the target) institution [3], such as a museum and Europeana 4.
In the next sections we present features and the interface of VisTA. In section 2 we
discuss the motivation and related work. Section 3 introduces the alignment problem
in VisTA. Section 4 describes the user interface and the features of VisTA in terms of
visualization of the alignment process and the produced result. Section 5 presents the
template mechanism that provides configurable application components. Section 6
concludes with a discussion on the tool’s current state and future work.
2 Motivation and Related Work
In the last two decades a lot of work has been published in the broader area of align-
ment, comprising schema matching, schema mapping and ontology alignment, focus-
ing on the development of approaches trying to achieve as accurate results as possible
[4]. Several frameworks have been proposed to apply automatic [5] or semi-automatic
[6] procedures based on configurable workflows in order to produce alignment, usual-
ly for large vocabularies. They sometimes provide interactive ways [7] to build the
alignment workflows by combining predefined matching algorithms. These algo-
rithms may have to be fed with input parameters in intermediate steps of the align-
2
http://www.cidoc-crm.org/sites/default/files/SRM_v1.5.pdf
3
http://network.icom.museum/cidoc/working-groups/crm-special-interest-group/
4
https://www.europeana.eu/
3
ment process to finally produce an estimation of the similarity among terms. It’s true
that large vocabularies cannot be easily handled manually by a user, so the develop-
ment of batch production solutions seems to currently be the best approach. But in
such cases, although the process accelerates, an inexact result may be produced re-
quiring further editing in order to become practically exploitable.
Some ontology alignment tools propose interesting solutions for the visualization
of the alignment [8]. The graphical presentation of correspondences with connection
paths between the entities of the aligned graphs [9] and the graph node representation
[10] are the typical solutions provided. These methods are helpful for viewing the
details of the alignment structure but suffer from overload and confusion with the
amount of interconnections displayed. Some of the alignment tools offer a web inter-
face [11-14] while others operate as standalone applications. A disadvantage of the
latter is that the user gets involved in the installation procedure, and sometimes that
discourages working with a tool. In all the above cases, the visualization of the align-
ment regards the representation of the correspondences between terms, based on the
accepted similarity values against a defined similarity threshold.
The need to align terminologies in the Cultural Heritage domain made us study the
work done in the alignment space. We concluded that there isn’t enough substantial
work dedicated to terminology alignment used in knowledge bases, since it is current-
ly considered as an inseparable part of the alignment bulk. The YAM++ online tool5
[11] and the Amalgame6 framework aim at thesauri matching. The second helps the
user to build the alignment workflow by configuring and combining the available
matching algorithms using a graphical representation of the workflow. Both tools
result in similarity estimation of the terms providing a graphical interface asking the
user to apply the correct relationship for each pair of terms, but without graphical
attribution of the actual meaning of their activity.
Table 1. General state of the available alignment methods
Exploitability
Method Supportive GUIs Input size Output accuracy
of result
Automatic Yes, for configs
Approximate, e.g. estima-
Yes, for configs. Large tion of similarity between ?
Sometimes also for terms
Semi- the manual phase
Automatic but without graph-
ical attribution of Exact, i.e. define the exact
the semantics Medium/Small Yes
relation between terms
Manual VisTA. Other?
Table 1 presents the general state of alignment w.r.t. the applied methods, the
available GUIs, the input size, the output accuracy and the exploitability of the pro-
duced result. As shown in the table, related work can be found on automatic and semi-
5
http://yamplusplus.lirmm.fr/
6
http://semanticweb.cs.vu.nl/amalgame/
4
automatic solutions, where supportive GUIs help mainly for the configuration of the
algorithms and the workflows applied to the alignment process. Nevertheless, the
result they produce is of a semantically uncertain exploitability.
Our belief is that terminology alignment of RDF/SKOS-like vocabularies can be
treated in more precise ways, especially when application requirements arise for using
knowledge bases. We mention the lack of work on the exact (accurate) terminology
matching for the cases that a “correct” [2], as a question to user convention, alignment
is needed and we offer VisTA to the terminology authors for efficient easy alignment.
VisTA supports a target-driven alignment with reconciliation of a source terminology
and using the broader and exact-match, two fundamental hierarchy relationships,
allows the users for working top-down, controlling and preserving the generaliza-
tion/specialization semantics known as is-a.
This intentionally simplified approach is materialized on a web interface, where we
tried to visualize the alignment process between two RDF/SKOS-like terminologies.
The applied hierarchy relations can be pre-configured and substituted with equivalent
RDF links from other schemas. The alignment result is a native RDF/SKOS-like
graph consisting of the necessary data interconnecting the involved terminologies.
3 The alignment problem in VisTA
In VisTA we deal with the target-driven alignment of two terminologies as an asym-
metric process aiming to the subordination of a source to a target terminology. The
process results in n×m correspondences among the terms of the two terminologies,
whereas a correspondence is regarded as a direct association between two terms.
For the successful subordination of the source and the production of consistent
knowledge, we assume three principles:
─ Terminologies and the alignment result are acyclic graphs preserving the taxonomy
subsumption (is-a)
─ The broader and narrow relations are symmetrically inversed
─ Reconciliation of the source terminology with the target but not the reverse
The alignment process results in the integration of different terminologies, under
one target terminology which is considered as the core structure. The reconciliation of
the structure of the source terminology is supported by the tool, in order to remove
any subterms of the aligned source term that violate the preservation of subsumption.
An example is presented below. The extension of the target terminology is based on
broader match and the exact match relations see section 4.2.
During the process multiple-inheritance of terms, i.e. a term can have multiple par-
ents, and subhierarchy overlaps may occur. These situations are allowed unless they
break subsumption. The constraints (rules) of the process are presented in section 4.3.
The alignment result (see section 4.4) really empowers the searching capabilities in
a semantic network, as the users of different terminologies are enabled to make que-
ries using the common target vocabulary together with their own familiar vocabular-
ies, to find more resources in their results.
5
A Use Case Example. Suppose we are building a knowledge aggregator based on the
generic Backbone Thesaurus7 (BBT) and we need to integrate data from a provider
that uses the Art & Architecture Thesaurus8 (AAT). Using the BBT as the target ter-
minology and the AAT as the source for the alignment process, the concepts of the
AAT get subordinated to BBT which’s structure is persisted. Both terminologies con-
tain a facet named activities: in BBT, the facet classifies intentional actions, while in
the AAT it encompasses areas of endeavor, physical and mental actions, etc. But the
AAT activities facet includes in the taxonomy the concept of natural phenomena (Fig.
1). We argue that the latter violates the subsumption in BBT according to the BBT
definition of activities. Hence, in order to set up an exact-match correspondence be-
tween the activities terms, it is important to exclude the inconsistent subhierarchies of
the AAT term.
Fig. 1. The AAT term natural phenomena and the BBT hierarchy
4 VisTA Overview
4.1 User Interface
An easy to use interface with two tabs on the top of the main page takes the user to
the respective working area: the Import/Export page, to manipulate terminology and
alignment graphs and the Edit page, to proceed with an alignment process.
The Import/Export page provides the appropriate options to manage terminology
and alignment data. Terminology data comprise distinct named graphs of SKOS/RDF
data that can be used as source and target terminologies in an alignment process. The
alignment data is a set of named graphs, the results of the alignment processes, each
one related to a specific pair of terminologies. Helpful utilities are available for im-
porting new terminology data and exporting the RDF graphs in multiple formats.
The Edit page provides appropriate components and functionality for performing
the alignment steps as a directional process that takes place from the source to the
target terms. For that purpose the page is divided in two main symmetric areas, the
left one exposing the source terminology interface and accordingly the right one for
exposing the target terminology.
7
https://vocabs.dariah.eu/bbt/ConceptScheme/Backbone_Thesaurus/
8
http://www.getty.edu/research/tools/vocabularies/aat/
6
An alignment process comprises drag’n’drop steps for the creation of distinct cor-
respondences. On each step the currently aligned pair of terms is checked against the
alignment rules (see 4.3). A dialog popup asks the user to choose which relation must
be applied, prompting to align a source to a target term as: exact or narrow term.
After the relation is selected by the user, the aligned source term appears on the target
tree as a new tree-node or as a label extension to the target node (see section 4.2),
depending on the selected relation, narrow or exact respectively.
4.2 VisTA Features
When developing an authoring tool, the design of proper functionality to help the user
to work efficiently should be of high consideration. The specific challenge is to ena-
ble the user to maintain a mental image, an overview, of the process and progress of
the alignment task, without being overwhelmed by the details or lost in details. VisTA
offers a convenient and friendly interface to support interactive alignment, enabling
the users to take charge of the alignment process and manipulate the data involved in
the alignment activity. The alignment is treated as a directional drag’n’drop process
from a source to a target terminology extending the target hierarchy. Visualization of
the alignment on the target terminology tree, appropriate highlighting of the terms’
state and specific accessibility options are powerful features of VisTA. The user is
fully aware of the state of the alignment activity enabled for viewing the aligned
terms in both their original structures as well as in the integrated semantic hierarchy.
Visualization of a Term. The terminology trees used in an alignment process consist
of terms each having one of the three states used for visualization:
─ Aligned (or explicitly aligned). The state for the source and target terms that are
directly participating in a correspondence in the context of the alignment process
(Fig. 2. a, b). These terms are both included in the alignment graph.
─ Implicitly aligned. The state for the source terms considered as indirectly aligned
when one of their parent terms is being explicitly aligned (Fig. 2. c). These terms
are included in the alignment graph linked to their aligned parents.
─ Not aligned. The state for the source and target terms not included in the alignment
graph. (Appear in the default black color in Fig. 2)
Visualization of the Terminologies. The representation of terminologies as tree
views enables accessibility, comparability, and association between hierarchical struc-
tures. Taking advantage of the lazy load implementation, the tree view hierarchy is
produced with two SPARQL queries. The first is responsible to fetch a multi-rooted
terminology tree based on specific RDF properties for the hierarchy relation between
broader and narrower terms. The second is responsible for fetching the children of a
node on expanding. The queries and their properties are all predefined in a main com-
ponent template. After the tree structures are created the term-nodes can be expand-
7
ed/collapsed, selected and dragged by the user for the specific purposes of the editing
and browsing.
Fig. 2. a) Aligned terms are colored in blue in their original locations in the full hierarchies. b)
Aligned source terms appear in orange at the target tree (right). c) The terms in italics (blue
and orange), in both hierarchies, are considered as implicitly aligned. d) The blue circled terms
visualize a correspondence e) The broader-narrow match is visualized with new orange tree
nodes as children of a target term e.g. aat:mental activities is child to the bbt:human interac-
tions f) The exact-match relation is visualized with the symbol “=” on a target node e.g.
bbt:functions = aat:functions (activities) g) The exact-match terms share their children in the
target tree e.g. bbt:functions = aat:functions (activities)
Creating and Visualizing Correspondences. An easy drag’n’drop mechanism from
the source to the target terms helps the user to create correspondences (Fig. 2.d). The
alignment result, as a set of correspondences, interconnects the aligned terminologies
applying the two fundamental RDF properties of the SKOS schema:
─ skos:broader, from a source to a target term in order for the source term to become
a narrow term of the target, extending thus the specification of the latter, and
─ skos:exactMatch, from a source to a target term, when the source term has an
equivalent meaning to the target term, extending its label and specification
The semantics of the alignment are visualized, so the user is able to realize the re-
sults of this activity. In the broader representation the metaphor of a new branch as a
new child of the target term, is used, carrying the source term sub-hierarchy from the
original source tree. Thus an extended target tree is produced, with new branches
coming from the source tree (Fig. 2.e). The user is allowed to edit the source term
sub-hierarchy by excluding specific terms before creating a new correspondence.
In the exact match representation (Fig. 2.f) the aligned source terms do not appear
as new branches of the target term but as a label extension of the target term joined to
it with a “=” symbol next to the existing label. In this case the target node gets also
extended by new branches coming from the sub-hierarchy of the source term. The
first-level children of the aligned source term become new growing leaves and
branches to the target node, implying this way that the equivalent terms can have the
same sub-terms in common, as shown in Fig. 2.g.
8
The sub-terms of the aligned source term are considered implicitly aligned terms.
VisTA distinguishes explicitly and implicitly aligned terms by specific highlighting
on both the source and the target terminology trees. (Fig. 2. a, b, c)
Finally, an alignment correspondence can be removed from the target tree by delet-
ing the specific explicitly aligned source term. Then, the term and its children are
removed from the target tree and the correspondence triple is deleted from the graph.
4.3 Alignment Rules
During the alignment process, when a new correspondence is to be created between
two specific terms, a check mechanism informs the user about the current state of the
terms to prevent useless and inconsistent situations. The rules applied are:
─ Check for explicit (direct) alignment relation. The source term must not be already
aligned to the same target term, otherwise the process is canceled.
─ Check for the existence of the source term in the target tree. When the source term
to be aligned is already an original term of the target terminology then alignment
of that term causes the reconciliation of the target terminology which is not al-
lowed. In this case the user is informed and the process is canceled.
─ Check for implicit (indirect) alignment relation. The source term may already be
indirectly aligned to the target term. In this case the user gets a warning whereas
the alignment is allowed.
─ Check for aligned descendants of the source term. The source term may contain
already aligned sub-terms. In this case the user gets a warning whereas the align-
ment is allowed.
4.4 The Result of the Alignment
What is actually produced by VisTA in an alignment process is a specific alignment
graph. The format of that graph is native RDF/SKOS, and it contains all the explicitly
produced information for the matching terms between the two terminologies as well
as the children hierarchy of the terms coming from the source terminology. The struc-
ture of the aligned terms coming from the target terminology is considered to be con-
stant, thus there is no need to copy their original structures into the alignment graph.
Hence, to take advantage of the alignment result e.g. in a search operation, the appro-
priate searching space is the union of the alignment and the target terminology graphs.
5 Implementation and Configuration
VisTA is based on the template mechanism of the metaphacts platform9. The Meta-
phacts knowledge graph platform delivers an extensible template mechanism based
on HTML5 Web Components10 and Handlebars11 templating engine. Out of the box
9
http://metaphactory.com
10
https://html.spec.whatwg.org/multipage/custom-elements.html#custom-elements/
9
the platform provides built-in configurable generic components for visualization,
search and authoring of RDF knowledge graphs. In addition to built-in components
the platform also offers Typescript/JavaScript based SDK that can be used to develop
custom UI components or add additional functionality to already existing ones.
The main page of VisTA is the result of the rendering of a particular component
template. The parameters included in the template can be externally modified to ex-
tend the operability to more terminology cases since the use of RDF relations cannot
be foreseen in new terminologies. Some of the configurable parameters comprise: the
definition of the RDF broad-match and exact-match relations used in the alignment
process, the SPARQL queries templates for retrieving the root terms of a terminology
tree, the children and the parents of a term in a terminology tree, the SPARQL query
template for performing the search operation, the RDF property paths used in
SPARQL queries templates for matching the hierarchy relation between a term-
subterm and the labeling of the terms.
6 Conclusions
We presented VisTA, a tool that delivers an interactive solution for the exact termi-
nology alignment problem, required in the context of data provisioning and aggrega-
tion processes. Terminologies and thesauri, contrary to other ontologies can be han-
dled in more precise ways regarding alignment in order to produce accurate and ex-
ploitable results. VisTA offers a suitable and convenient web interface to visualize the
alignment process between a source and a target RDF/SKOS-like terminology. The
produced result is a native RDF/SKOS graph that interconnects the two terminologies
towards the extension of searching capabilities over the integrated semantic graph.
VisTA has not been used on a regular basis yet. Several tests with medium sized
terminologies used as sources to be aligned to the full AAT terminology, showed a
good performance. VisTA, taking advantage of the lazy load tree implementation, has
a fine response on rendering the tree hierarchies. On large vocabularies, there are
delays observed when a new correspondence is being created (or deleted) related to
performance issues on the select/update queries of the SPARQL endpoint.
Finally, we propose VisTA not as a competitive but as a complementary supportive
solution to the work pending for the manual phase of an alignment procedure. Cur-
rently VisTA provides a from-scratch alignment process, but an extension of the tool
is planned: to use as input, except from the pair of terminologies, furthermore a pro-
posed base of the estimated similarity between terms (e.g. using the EDOAL12 align-
ment format), as the basis for the manual process. Other features supportive to the
users, planned for future versions, are: alternative views of correspondences or parts
of the alignment graph, the extension to more than the two basic relationships, the
extension of the alignment rules to other semantics.
11
https://handlebarsjs.com/
12
http://alignapi.gforge.inria.fr/edoal.html
10
Acknowledgments
This work is the result of collaboration between the British Museum, metaphacts and
FORTH in the context of the ResearchSpace13 project. We would like to thank all the
RS team and especially Dominic Oldman for his kind support as well as Mike Kelly
for his well-aimed remarks and suggestions.
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http://researchspace.org/