=Paper=
{{Paper
|id=Vol-431/paper-6
|storemode=property
|title=A community based approach for managing ontology alignments
|pdfUrl=https://ceur-ws.org/Vol-431/om2008_Tpaper6.pdf
|volume=Vol-431
|dblpUrl=https://dblp.org/rec/conf/semweb/CorrendoAS08
}}
==A community based approach for managing ontology alignments==
https://ceur-ws.org/Vol-431/om2008_Tpaper6.pdf
A Community Based Approach for Managing
Ontology Alignments
Gianluca Correndo, Harith Alani, Paul Smart
University of Southampton,
Electronic and Computer Science Department
[gc3, ha, ps02v]@ecs.soton.ac.uk,
WWW home page:http://ecs.soton.ac.uk
SO17 1BJ, United Kingdom
Abstract. The Semantic Web is rapidly becoming a defacto distributed
repository for semantically represented data, thus leveraging on the added
on value of the network effect. Various ontology mapping techniques and
tools have been devised to facilitate the bridging and integration of dis-
tributed data repositories. Nevertheless, ontology mapping can benefit
from human supervision to increase accuracy of results. The spread of
Web 2.0 approaches demonstrate the possibility of using collaborative
techniques for reaching consensus. While a number of prototypes for col-
laborative ontology construction are being developed, collaborative on-
tology mapping is not yet well investigated. In this paper, we describe a
prototype that combines off-the-shelf ontology mapping tools with social
software techniques to enable users to collaborate on mapping ontologies.
1 Introduction
The transformation of the Web from a mere collection of documents to a queryable
Knowledge Base (KB) is one of the most prominent targets of Semantic Web
(SW) [1]. To help reach this goal, knowledge repositories need to publish semantic
representations of their data models to enable other machines to understand and
query their content. To this end, much research and development has focused on
building tools and capabilities for ontology and KB construction. However, sup-
port for distributed teams to remotely and continuously collaborate on building
and updating ontologies and knowledge repositories is still underdeveloped.
Defining an ontology for representing data semantics is usually a costly and
time consuming task. Furthermore, knowledge evolves over time which adds to
maintenance cost. That is why more and more often successful proposals for
information sharing involve user’s feedback exploiting a network effect. If an
ontology is meant to reflect the views of a specific community and support their
knowledge sharing tasks, then the community itself should be empowered to
express, formalise, share and mantain a set of ontologies for supporting such
tasks [2]. Some ontologies need to be agreed upon by the user community, and
this agreement process must be supported by tools and methodologies to allow
users to express their views and opinions freely.
The rise of social Web 2.0 applications has demonstrated how general Web
users can actively contribute and share all sorts of data and information, such as
images, videos, bookmarks, opinions, diaries and experiences. Adopting a similar
approach on the SW means supporting users to dynamically and collaboratively
build ontologies, add semantics to data, discuss and share views and suggestions,
etc. Good and colleagues [3] showed how SW users can successfully collaborate
to negotiate and build good quality ontologies when provided with a tool that
supports such activities. User-contributed content can also be beneficial for engi-
neering ontology mapping activities, most of which rely on automated linguistic
and statistical methods that make use of lexicographic clues and structural in-
formation but rarely take into account user input [4]. In this paper we describe a
prototype and its underlying approach for facilitating gradual ontology mapping
by supporting social collaboration and reuse of mapping results. More specifi-
cally, our approach allows the following:
– Alignment of local ontologies to shared ones: users can align local models,
used for bridging data sources, to shared ontologies by using a number of
automated ontology mapping tools. These tools are flexibly plugged into our
system;
– Social interaction and collaboration: users can discuss ontology alignments
and propose changes through a number of social services, such as discussion
and voting facilities;
– Reuse of ontology alignment information: users can add to, and correct, the
alignments suggested by automated ontology mapping tools, or suggested
by other users. User feedback and mapping information are logged by the
system and reused to improve the accuracy of future alignments on similar
concepts;
2 Related Work
The need to make explicit and publish the semantics of the data is becoming
increasingly central since more information systems are becoming largely de-
coupled and separately managed. To this end, the vision of the SW is moving
towards a scenario where the task of creating and mantaining ontologies, that
formalise data semantics, is going to be handed to the community that actually
uses them [2]. In accordance with this vision, the models for making data seman-
tics explicit and exchangeable can be the fruit of a collaborative effort by the
community members whom will share the responsibility of ontologies creation
and maintenance. Such an effort must be supported by tools and methodologies
that allow latent models to emerge as a product of a collaborative effort and
dialogue.
Our work taps on the intersection of different but overlapping areas in on-
tology engineering: collaborative construction and management using social net-
working tools, data web and sharing of ontology fragments. We briefly highlight
the main contenders in these areas and elaborate on their relationship with our
work.
Historically speaking, investigations into enhancing user knowledge through
collaboration and sharing goes back to the early nineties [5]. Ontolingua [6] is
an early proposal in this area, which provides some basic support for users to
reuse and extend shared ontologies. Another example is the model discussed by
Euzenat in [7], where users can build their local ontologies, get them approved by
the community, and get support by a discussion protocol which conveys users’
rationales for changes in a formal schema. The Semantic Web has taken this
approach further by providing the tools and languages to construct networked
semantic representational layers to increase understandability, integration, and
reuse of information.
The rise of Web 2.0 approaches has then demonstrated the effectiveness and
popularity of collaborative knowledge construction and sharing environments
that adopted lighter version of ontologies, where the emphasis is put on the
easiness of sharing knowledge rather than creating or adopting static formal
ontologies [8,9]. Harnessing Web 2.0 features to facilitate the construction, cu-
ration, and sharing of knowledge is currently pursued by different communities.
Collaborative Protègè [10] was recently developed as an extension to Protègè to
support users to edit ontologies collaboratively, by providing them with services
for proposing and tracking changes, casting votes, and discussing issues, thus
infusing classical ontology editing with a number of popular social interaction
features. Another ontology editor with collaborative support is Hozo [11], which
focusses on managing ontology modules and their change conflicts. Good and
colleagues demonstrated how good quality ontologies can be built quickly in a
collaborative fashion[3]. Other approaches use social tagging as the main driver
for enacting collaborative lightweight ontology building (e.g [12,13]). Similarly,
other tools are focussing on editing instance data, like OntoWiki [14] and DBin
[15] which are prime examples of tools for community-driven knowledge creation.
Most of the tools listed above focus on supporting users to collaboratively con-
struct ontologies or to collaboratively populate an ontology with instance data.
Unlike these tools, however, our proposed system, OntoMediate, extends the
collaborative notion to support the task of ontology mapping, where users can
collaborate and interact to map their existing ontologies and maintain a quality
mapping asset within the community. An approach similar to OntoMediate, that
addresses ontology mapping within communities, is the Zhadanova and Shvaiko
[16] method. The authors proposed to use similarity of user and group profiles
as a driver for suggesting ontology alignments reuse. The focus of that work was
on building such profiles to personalise reuse of ontology mappings. In Onto-
Mediate, we are exploring the use of collaborative features (discussions, voting,
change proposals) to facilitate the curation and reuse of ontological mappings
by the community, to facilitate a social and dynamic integration of distributed
knowledge bases. The use of collaboration for achieving consensus on terms’
semantics is largely justified because of the social nature of ontologies. In or-
der to mediate possibly conflicting concept’s description, user feedback must be
taken into account and discussion within the community must be fostered. Our
approach is novel in the way it addresses the task of aligning ontologies, by ex-
tending and enhancing automatic mapping tools with a full community support.
In our approach, alignments are seen as a resource, built and shared by a com-
munity. The community is able to investigate, argue, and correct the individual
mappings, using various supporting services provided in OntoMediate.
3 The OntoMediate Approach
In the OntoMediate[17] project we are studying how social interactions, collabo-
ration and user feedback can be used in a community in order to ease the task of
ontology alignment and ontology mapping sharing. Focus of our research is how
to ease the integration of data sources using ontologies and ontology alignments
in order to provide an agreed semantics to integrated data.
The implemented prototype is a Web application developed with J2EE and
AJAX technologies. The system manages OWL ontologies that are parsed using
the Jena API1 . The system has been designed to be extended via its APIs and
is composed of three main subsystems:
– Ontologies and datasets manager;
– Ontology alignment environment;
– Social interaction environment.
3.1 Ontologies and Datasets Manager
This part of the system allows users to register (as well as unregister) the datasets
they intend to share with the community and the ontologies that describe their
data vocabulary. The ontologies that are loaded onto the system, need to be
aligned with one or more shared ontologies in order to enable querying of the
published data by the community. The system currently supports different stor-
age types for the ontologies and/or datasets:
– URL: only the URL is stored and the ontology is accessed (read only) re-
motely;
– Cached file: the ontology file is uploaded to the system and stored in a file
server;
– Jena RDBMS : the ontology file is uploaded to the system and stored in a
relational database using the Jena database back-end;
– SPARQL endpoint : the document is remotely accessed using the SPARQL
protocol2 .
Once an ontology is registered with the system, the owner (or everyone if
the ontology has been shared within the community) can browse it by using a
flexible frame-like interface. The ontology browser displays the hierarchy of con-
cepts, as well as detailed information for the focused concept (selected concept).
The detailed information includes: labels, superconcepts, subconcepts, equivalent
concepts, concept description (from the rdfs:comment annotations), properties
and their constraints.
1
http://jena.sourceforge.net
2
http://www.w3.org/TR/rdf-sparql-protocol/
3.2 Ontology Alignment Environment
The full automation of ontology alignment is not an easy task [18]. The factors
that affect the computation and accuracy of ontology alignments are so delicate
that we can not afford not to take into account user input as a contributing
factor of paramount importance. It is for this reason that, implementing an en-
vironment for aligning ontologies, great attention has been made to the usability
issues that could affect this task [19].
Our system provides an API for automated ontology alignment tools to be
plugged in and also maintains data structures to store parameters needed by
a particular tool to execute (e.g. threshold values or available tool options).
The API allows for easy integration of new alignment tools, when they become
available, by means of wrappers - some tools have been already integrated with
our system (e.g. CROSI mapping system [20], INRIA Align [21] and Falcon OA
[22]). These tools allow the system to support the alignment task by proposing
to the user some initial candidate mappings. The results from different tools
can be merged and the decision of which combination of tools to use can be
parameterised together with the configuration used to invoke each tool. The
merge of results from different tools is achieved by a weighted mean of each
contribution and it is implemented as a normal alignment tool plugged into the
system (i.e. different merging alghoritms can be coded and plugged in).
Once the automated mapping has been executed, the results are displayed
in a proper interface for reviewing and for searching further alignments. The
ontology alignment interface is split into two main panels, the left panel for the
source ontology and the right panel for the target ontology, whereas the bottom
space is used for summarising the mappings found for the focused source concept.
The interface has two view modalities: Hierarchical and Detailed.
In the Hierarchical view the two taxonomies are centered on the source con-
cepts that have been mapped to a target concept, both of which are highlighted.
The user can browse both taxonomies and create new mappings by dragging a
source concept and dropping it into a destination concept. When the user fo-
cusses on a mapping, he/she can switch to a detailed view and the description
of the source and target concept are shown side by side.
In the Detailed view, the user can map the properties using the same drag
& drop facility used for mapping the concepts. The users can also explicitly
reject some automatically proposed mappings. This choice will be recorded by
the system and will be used to filter future mappings towards this target concept,
thus increase future ontology alignment precision. Alternative interface designs
for ontology mapping, such as the one presented in [23], will be considered for
future version of the system.
3.3 Social Interaction Environment
This functionality allows users of a community that deal with similar data -
and therefore have a mutual interest to maintain good quality alignments - to
socially interact with each other. The aim of the social interaction is to exploit
community feedback in order to enhance the overall quality of the ontology align-
ment and achieve agreement on semantics of concepts by means of community
acceptance. This subsystem displays to the user three views: Ontology view;
Mappings view and Forum view.
Fig. 1. Discussion environment - Ontology View - Post
The Ontology view (see Figure 1 top-left corner) displays an enhanced tax-
onomy browser for the selected shared ontology. The enhancements concern the
user activities affecting the shared concepts, visualising additional information
(e.g. number of incoming mapping per concept are reported in brackets like the
number of post exchanged in the forum discussing such mappings). Moreover,
the interface allows to inspect the set of labels used for equivalent concepts (i.e.
the ones provided with the alignments) in local ontologies (see the Additional
labels text field in Figure 1). The user or administrator can edit such labels and
add them to the shared concept to enrich the concept description with users’
contributions. The new mapping, and the edited/added labels, will be logged in
a database to be reused later to improve the recall of future ontology alignment
tasks (section 4.2).
When the user selects a concept that has some user mappings associated with
it, he/she can switch to the Mappings view that displays information about
the local mappings for the focused concept. The user can then inspect a sum-
marised description (i.e. subconcepts, superconcepts, properties etc.) of the local
concepts and decide if they are relevant to the focused target concept or initiate
a discussion thread in order to change them. The change proposal is composed
of a thread post, that describes in natural language the content of the proposal,
and a formal description of the operation to discuss. The proposed change can
affect a number of alignments and may lead, if the proposal is accepted, to the
relocation of such alignments to a different target concept. If the target concept
refferenced in the change operation is not yet present in the ontology, a new one
will be created within the hierarchy in accordance with the input given by the
users in the forum. The possibility to create new concepts to host user align-
ments provides a way to reshape (even if only by additions) the target ontology
in function of the (meta)data provided by users.
The system provides a forum for the discussion of the users’ proposals (see
Figure 1 bottom-right corner). Every time a user proposes a change using the
mappings view, a new thread is created in the forum and other users are free
to debate the proposal, reply the proposal with a new one or simply agree or
disagree with it. The user’s vote is computed for update the proposal statistics
(i.e. number of votes, percentage of approvals and disapproval) that is promptly
displayed along the proposal.
The new action item associated with a target concept is notified to every
interested user by means of RSS feeds whose the interested users can subscribe
to. Once a proposal has reached a critical mass (e.g. when the majority of users
affected by the change have expressed their opinion) it will be endorsed, or
submitted to the administrator in order to judge it and reach a final decision.
4 Working Example
In order to better explain our approach and show how users’ feedback can be
used in order to improve the ontology matching task, we report on a small
example in the chemical domain and the findings of a working experiment. In this
example, two users want to share information on hazardous chemical compounds.
They each create an ontology that reflect the nature and structure of their
data sources (in our example the users deal with data about Landmines and
Hazardous Components, see Table 1).
Table 1. Domain ontologies used in the experiment
Name Domain n◦ Concepts Main Concepts
Shared Ontology
Chemical Chemistry 130 Element, Compound, Ex-
plosive
Local Ontologies
Landmine Explosive devices 830 Country, Explosive De-
vice, Material
Hazardous Hazardous materials and 89 Explosive, Flammable,
Components devices Container
4.1 Alignment task
This tiny community is provided with a shared domain ontology where a set of
entities and relationships relevant to the chemical domain is defined (see Table
1). The two users need to align their local ontologies to the shared one in order
to exchange information and integrate their data. To fulfill this task, the users
use off the shelf automatic tools with the Ontology Alignment environment
(see section 3.2). The automatic ontology alignment tools provide an initial set of
alignments that the users can revise, using the system interface explicitly stating
the correct alignments and the incorrect ones. With the same interface, the users
can then browse the two ontologies and provide manual alignments if required.
At the moment only equivalence relation is supported for expressing alignments
but the adoption of more expressive primitives is under study. In this scenario
the local ontologies act as ”contexts” of their respective data sources (following
the nomenclature used by Bouquet et al. [24]) while the shared ontology is meant
to provide an ontological formalisation of the domain to enable the actual data
integration. They are the objects that catalyse the consensus process.
4.2 Reuse of information from mappings
The alignments provided by the alignment task will be reused to improve au-
tomatic future alignments toward the same target ontology. Lexical labels from
users’ ontologies can be adopted by the shared model as rdfs:label that can be
considered in future automatic alignment tasks in an attempt to improve perfor-
mance and accuracy of automatic mapping tools. Within the chosen domain (i.e.
hazardous chemical compounds, but the assumption holds in other domains),
different labels can represent the same concept (e.g. the explosive HMX is also
known as Octogen or Cyclotetramethylene-tetranitramine, see Table 2 for a sum-
mary of the labels logged from the alignment activity). The working assumption
is that, gathering all the labels related to a concept from local representations,
and learning which alignments must be avoided in the future (e.g. rejected by
users), can help to increase the performance of automated alignments. As an ex-
ample, assuming the two users of this example have subsequently aligned their
ontologies, the labels collected from the first alignment (see Table 2) can be used
for improving the performances of the second. Manual mappings discovered by
the first user (e.g. Black Powder ≡ Gun Powder or Nitromethane ≡ Nitrocar-
bol ) can in fact helping the discovery of target concepts that would be missed
otherwise by automatic tools. Such additional user’s labels can in fact bring, if
integrated in the shared model, to an increase in automated tools precision and
recall for subsequent alignments.
4.3 Social interaction
Browsing the definition of the shared ontology, the users can revise each other’s
alignments to check that the definition of the local concepts is relevant to the
targeted shared concept. The self curation of the shared alignments is an impor-
tant premise of the approach; users that are interested in integrating their data
Table 2. Alignments based on past users activity
Source concept ≡ Target concept
Discovered by system and proposed to user
Black Powder ≡ Gun Powder Black Iron Oxide ≡ Magnetite
Magnesium ≡ Mg Nitromethane ≡ Nitrocarbol
Red P ≡ Red Phosphorus White P ≡ White Phosphorus
Learnt from user input to be wrong and rejected
Red Iron Oxide ≡ Iron Oxide Nitromethane ≡ Nitroethane
or in querying the integrated knowledge base have a main concern in browsing
such alignments, providing feedback and starting corrective operations whenever
needed.
Automated ontology alignment tools usually fail to catch the difference among
lexically similar concepts such as Nitromethane and Nitroethane. Despite their
lexical and chemical similarity, it is very important to distinguish the two (the
first can be used as an explosive while the second can not). For this reason,
once a user has found the incorrect alignment (i.e. Nitromethane ≡ Nitroethane)
inspecting the local concept definition, he/she can select the faulty alignment
and initiate a change process. Along with the incorrect mapping, the user can
provide the URI of the suggested correct target concept (i.e. Nitrocarbol, a syn-
onym of Nitromethane) and issue a change proposal. If no suitable concept can
be found in the target ontology the user can suggest the creation of a new one
providing its location in the targeted hierarchy. The proposal will be posted in
the forum dedicated to the maintenance of the shared concept alignment asset.
The community can be alerted of the change proposal by RSS feed subscription
(every target concept has a feed where new posts are published, and every in-
terested user can register to the feed) and inspect the change proposal, discuss
it on the forum, replying to the post or just expressing dis/agreement with the
content of such proposal.
4.4 Alignment asset management
Once the two ontologies have been aligned with the shared model, they can be
exploited for assuring a meaning preserving information exchange between the
components of the community. The discussion fostered in the social environment
and the constant supervision by the users upon the ontology alignments help in
mantaining agreement and awareness on terms’ semantics within the community.
5 Discussion
Collaborative ontology mapping has a great potential in enhancing performance
and in sharing results of automatic mapping tools. The system presented in this
paper supports users in their ontology mapping activities and logs their feedback
to further enhance the output of automated ontology mapping tools. Moreover
it provides social features for community driven mapping revisioning and limited
support for shared ontology evolution.
Ontology mapping is inherently difficult, and can be influenced by various
issues. For example, some mappings can be user or context dependent, in
which case a mapping that has been approved by some users may not neces-
sarily suit others. Mapping popularity can be used to weight each ontology
alignment. The degree of popularity of a specific alignment can be taken into
account when displaying alignment suggestions to the user. Storing user profiles
to personalise mappings has been proposed elsewhere [16].
When reusing mapping results, it is important to prevent error propaga-
tion. It is important to build a user interface in such a way to discourage blind
reuse of mappings. OntoMediate allows the community to flag, discuss, and
democratically change incorrect mappings, but this is of course dependent on
users spotting erroneous mappings. If a mapping is reverted, it will be impor-
tant to readjust its popularity accordingly.
In addition, mappings that receive repeated change proposals or become
subject to long and intense discussions may be regarded as controversial or
debatable mappings. Such mappings may also need to be handled with care
when used or reused suggesting administrators to create appropriate ontological
description to better characterize those particular local concepts.
OntoMediate uses off the shelf automatic ontology mapping tools, and hence
the complexity of its mappings are largely based on those of the mapping tools.
The current implementation of OntoMediate allows users to manually map enti-
ties expressing simple one to one mapping. More complex mappings, such as map-
ping a union of classes or linking properties by means of transforming functions,
is not currently supported. However, it has been reported that when engineering
ontologies collaboratively, complex OWL constructs are often not required [9].
Ontology mapping is a not an easy task, and hence users will not expected
to link their ontologies without a clear added value. The ultimate goal of
OntoMediate is to facilitate distributed querying and integration of knowledge
bases in a community. Therefore, in addition to displaying concept mappings,
it will be important to also display some information about the knowledge that
each mapped ontology brings to the table. Showing what data a specific mapping
or a whole ontology is bringing to the community might encourage others to (a)
see the general value of this mapping and hence offer their expertise and help to
map the new ontology correctly, and (b) map their ontologies to others if they
have not already done so (e.g. to link their data to the new repository).
The approach we focused on in OntoMediate is based on a small to medium
size community, sharing interests and goals that can benefit from integrating
their data. In OntoMediate, it is presumed that an overall administrator can
act as the ultimate curator of the system. For such an approach to scale up to
the Web as a whole, the wisdom of the community will have to be the final
ruler. Wikipedia is a fine example of how this can work, and the Linked Data
initiative is a first step to creating a wide network of linked semantic data [25].
However, demonstrating added value will be more difficult once the community
is too large and diverse, and hence it will probably breakup into sub communities
with similar requirements.
6 Summary and Future Work
This paper presented a prototype for supporting ontology mapping with commu-
nity interactions, where users can collaborate on aligning their ontologies, and
manually-driven alignments can be stored and reused later. Our initial experi-
ment showed good potential of increasing both precision and recall in ontology
mapping when reusing past mapping results. Next, we plan to run much larger
experiments to further test the validity of the approach, and the usability of the
services and features that it provides. We have lately implemented services that
exploits the managed alignments for translating queries and data. In the near
future we will also implement services to allow users to submit formula to me-
diate between concepts or data that might not be directly mappable (e.g. when
the concepts are culture-dependent, or when data property values are function
of different other values). Additionally, we will next focus on building the capa-
bility to allow users to perceive, and query, the integrated KBs, thus increasing
added value. The ontology alignments and the social network will be exploited
to focus the search task. We will make the system available to the public online
in the next few weeks.
7 Acknowledgements
This work was partially funded by a grant awarded to General Dynamics UK
Ltd. and the University of Southampton as part of the Data and Information
Fusion Defence Technology Centre (DIF DTC) initiative. The views and con-
clusions contained in this document are those of the authors and should not be
interpreted as representing the official policies, either expressed or implied, of
the UK Ministry of Defence, or the UK Government.
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