INTEGRATING SOCIAL WEB WITH SEMANTIC WEB
Ontology Learning and Ontology Evolution from Folksonomies
Ademir Roberto Freddo and Cesar Augusto Tacla
Universidade Tecnológica Federal do Paraná, Av. Sete de Setembro 3165, Curitiba, Paraná, Brazil
Keywords: Social Web, Semantic Web, Ontology Learning, Ontology Evolution, Folksonomy, Ontology Alignment.
Abstract: In this paper, we present an approach for integrating Social Web with Semantic Web by combining the
easiness of annotation of resources in the Social Web and the expressiveness of ontologies to describe the
resources in the Semantic Web. Our approach combines ontology learning and ontology evolution
techniques to provide an integrated Web. Besides, we show how ontology alignment can be used to enrich
ontologies in this context.
1 INTRODUCTION
The Semantic Web requires resources to be
annotated with machine understandable metadata
(Berners-Lee et al, 2001) being ontologies the
knowledge representation technique most used
nowadays to describe such metadata.
Even knowledge engineers and experts have
some difficulty of maintaining consistency between
resources and the ontologies. It is necessary to catch
the changes of web resources and keep update the
ontology. Besides, the lack of an imagined class or
wrong classification when users annotate a Web
resource with applications built on ontologies is a
recurrent problem.
The knowledge acquisition bottleneck has
limited ontology use. Ontology development starts
with an initial ontology which is later revised,
refined and filled with details (Heflin et al, 1999;
Noy and McGuinness, 2001). Besides, new
information which was previously unknown or
unavailable needs to be added to the initial ontology.
Nowadays changes to ontologies have to be captured
and introduced by knowledge engineers (Zablith et
al, 2008).
On the other hand, in the Social Web, social
tagging systems such as Flickr
(http://www.flickr.com) for photo sharing, and
delicious (http://delicious.com) for social
bookmarking are becoming more popular in the
Web. The reason for their immediate success is the
fact that no specific skills are needed for annotating.
Users annotate, assign tags (any keyword, label),
and categorize web resources easily and freely
without using or even knowing taxonomies or
ontologies. In the social tagging systems, user’s
resources and associated tags constitute the
personomy. The collection of personomies
constitutes a folksonomy (Jaschke et al, 2008). The
folksonomy is dynamic as long as users learn new
things and review their personomies, including and
excluding their tags. Creating personomies is easy
and does not require expert users. However, it let
users to introduce ambiguities. Thus, in
folksonomies content retrieval activities such as
searching are limited, because results can present
low recall and precision. So, in the Social Web the
meaning of the tagging data has limited useful
reasoning with the data.
1.1 Folksonomy and Ontology
Folksonomy is created by open and uncontrolled
systems (social tagging systems) where users can
annotate resources with different tags depending on
their social or cultural backgrounds, expertise and
perception of the world (Belelman et al, 2006;
Golder and Huberman, 2005; Peterson, 2006; Wu H.
et al, 2006).
Ontology is “an explicit and formal specification
of a conceptualization” (Gruber, 1993). Ontologies
specify common conceptualizations, independent of
data model, so people can align their systems
semantically by adopting ontologies.
247
Roberto Freddo A. and Tacla C. (2009).
INTEGRATING SOCIAL WEB WITH SEMANTIC WEB - Ontology Learning and Ontology Evolution from Folksonomies.
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pages 247-253
DOI: 10.5220/0002303102470253
Copyright
c
SciTePress
In contrast to ontology, folksonomy does not
explicitly state shared conceptualizations. It is a
free-form annotation of web resources, done by
users, and without the constraints of a predefined
taxonomy or ontology (Wu H et al, 2006). Thus, in
the Social Web, the meaning of the tagging data is
completely unspecified. We need technology for
reasoning with the folksonomies in such a way that
computations can discover and conclude new things
(Gruber, 2008).
The integrated Web combines the facilities in
annotating web resources with tags that
characterizes Social Web applications with
ontologies to better describe the resources in the
Semantic Web. According to Tim Berners-Lee
(Berners-Lee et al, 2001) "the Semantic Web is not a
separate Web but an extension of the current one, in
which information is given well-defined meaning,
better enabling computers and people to work in
cooperation”.
1.2 Contribution and Organization of
the Paper
Our approach aims at extracting structured data
(ontologies) from unstructured data (folksonomies).
As result of our approach, we obtain an ontology
whose elements are linked to their source tags
enabling to trace folksonomy changes back to the
ontology.
The folksonomies in Social Web can provide
new impulse to Semantic Web in by reducing the
burden on users and engineers in tasks related to
knowledge engineering (e.g. knowledge acquisition,
Web resource annotation, and ontology
construction). On the other hand, the Semantic Web
can improve inferences and provide better query
results in Social Web.
The remainder of the paper is organized as
follows. Section 2 describes the basic definitions and
current developments in ontology learning and
ontology evolution from folksonomies. In section 3,
we describe the approach to ontology learning and
evolution. Section 4 presents a case study. Finally,
Section 5 gives a conclusion and presents envisaged
works.
2 STATE OF THE ART
In this section we describe the state of the art in
ontology learning and evolution.
2.1 Ontology Learning
According to Maedche and Staab (2001), “ontology
learning greatly facility the construction of
ontologies by the ontology engineer. It is a task of
(semi)-automatically construct an ontology by using
machine learning or data mining algorithms that are
applied on data”.
In folksonomies, the use of tags by people with
common interests tends to converge to a shared
vocabulary. Following this intuition, a variety of
approaches have been proposed to discover shared
conceptualizations that are hidden in a folksonomy.
Some of them (Schmitz, 2006; Wu et al., 2006;
Belelman et al., 2006; Mika, 2005) analysis the co-
occurrence of tags. Schmitz (2006) finds candidate
subsumption relations, Wu et al (2006) and
Belelman et al. (2006) create clusters of tags, and
Mika (2005) builds graphs relating tags. However
these cited approaches focus on finding groups of
related tags rather than identifying the semantics of
those relations.
Another set of recent approaches (Angeletou et
al, 2007; Basso and da Silva, 2008; Specia and
Mota, 2007) build ontologies from folksonomies
thus going beyond the mentioned approaches that
identify implicitly inter-related tags. Angeleton et al
(2006) propose a method to enrich the tag space of
folksonomies by exploring ontologies. Basso and da
Silva (2008) presents a proposal for the ontology
construction/evolution from the folksonomies based
on WordNet. Specia and Mota (2007) propose the
integration of folksonomies and ontologies to enrich
tag semantics – identify semantic relation between
tags using ontologies available on the semantic web.
These approaches has some limitations:
difficulty in finding online ontologies; syntactic
mapping to link tags to ontology concepts; different
ontologies reflect different views which often lead to
contradictory; limited reasoning because of plain
structure of folksonomies; and they not focus on the
ontology evolution.
In social tagging systems (e.g.
http://www.flickr.com, http://delicious.com) there is
no formal semantic and no formal agreement on the
representation of the tagging process. This means
that every system uses a different format to publish
its tagging data.
Thus, other approaches (Knerr, 2006; Gruber,
2007, Kim et al, 2007) propose a solution for tag
data representation. Gruber’s conceptual model
(Gruber, 2007) describes tagging as a relation
between an object (resource – bookmark, picture), a
tag, a tagger (a person or agent that created the link
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
248
between the tag and the object) and a source (the
space where the tagging action has been performed –
flickr, delicious). Knerr (2006) develop an ontology
using the tripartite tagging (user, resource, tag)
model Newman et al, (2005). The Social Semantic
Cloud of Tags (SCOT) (Kim et al, 2007) ontology
aims to describe the structure and the semantics of
tagging data and to offer social interoperability of
the data among heterogeneous sources.
2.2 Ontology Evolution
Ontology evolution is defined by (Haase and
Stojanovic, 2005) as the “timely adaptation of an
ontology to the arisen changes and the consistent
management of these changes”. Ontology evolution
is a process that supports the enrichment of the
ontology by adding new entities (concepts,
properties, and instances) or by modifying existing
entities when new knowledge is acquired.
Ontology changes may come from explicit and
implicit requirements (Cimiano and Volker, 2005).
Explicit requirements are generated by ontology
engineers. Implicit requirements are reflected in the
behavior of the system and can be induced by
applying change discovery methods from existing
data (Cimiano and Volker, 2005; Stojanovick,
2004). Stojanovick (2004) defines three types of
change discovery: structure-driven deduces changes
from the ontology structure itself, usage-driven
identifies changes from the usage patterns creates
over a period of time, and data-driven generates
changes by modifications to the underlying data
(text documents, database) that represents the
knowledge modeled by an ontology.
The current state of the art in ontology evolution,
as well as a list of existing tools that aid the process
can be found in (Haase and Sure, 2004). Some of
these approaches are simple ontology editors, like
Protégé (Noy et al, 2000) and OilEd (Bechhofer et
al, 2001). One set of approaches (Alani et al, 2006;
Bloehdorn et al, 2006; Novacek et al, 2007;
Novacek et al, 2008, Ottens and Glize, 2007;
Cimiano and Volker, 2005; Zablith, 2007, Zablith et
al, 2008) in ontology evolution identify potential
novel information that should be added to the
ontology by exploiting the changes occurring in the
various data sources. Such approaches do not
consider folksonomies as an information source
where ontology changes can be discovered. In this
work, this is exactly the case, as we focus on change
capturing from data-driven implicit requirements.
Our collaborative approach for ontology evolution
starts with an existing ontology (base or domain
ontology) and supports the acquisition of new
knowledge from folksonomies when users change
their tags as long as their vocabularies change. We
evolve ontologies exploiting folksonomy versioning
and linking learned ontology entities to the source
tags in the folksonomy.
3 SOCIAL WEB WITH
SEMANTIC WEB
This section presents the approach to ontology
learning and evolution from folksonomies.
3.1 Ontology Learning from
Folksonomies
The ontology learning process from folksonomies is
articulated in the following phases (Figure 1):
populating the tag ontology, identifying relations
between tags, and interacting with user.
Figure 1: Ontology learning from folksonomies.
The populating tag ontology phase consists of
building a representation for folksonomy tags based
on entities defined in SCOT, an ontology to describe
the structure and the semantics of folksonomies.
The SCOT ontology uses concepts and
proprerties of Newman’s model (Newman et al,
2005).
After populating SCOT ontology, we identify for
each pair of tags, ((tag
1
,tag
2
), (tag
1
,tag
3
), (tag
1
,tag
n
),
(tag
2
,tag
3
), (tag
2
,tag
n
), (tag
3
,tag
n
)), the tag type
(whether concept or instance), and the relation
between them based on properties defined in SCOT
such as textual description, synonym, and spelling
INTEGRATING SOCIAL WEB WITH SEMANTIC WEB - Ontology Learning and Ontology Evolution from
Folksonomies
249
variant. In order to identify tag types, we use
Text2Onto (Cimiano and Volker, 2005). For tag
relations, we use WordNet (Miller, 1990) for
identifying meronyms, hyponyms, synonyms, and
hyperonyms, and the Hwang’s work (2007) for
performing analysis of classes’ hierarchy.
A tag can be described as an instance or a
concept in the base ontology. Thus, it is possible to
obtain the following types of pairs: (instance,
instance), (instance, concept), and (concept,
concept). In pairs of type (instance, concept), we
verify if the tag instance can be considered an
instance of the concept tag. For example, for the pair
of tags (Porto, University), university is the concept
and Porto is an instance (University of Porto). So,
we add to the base ontology the concept University
and the instance Porto. For pairs (concept, concept),
we identify meronym, hyponym, and hyperonym
relations. Finally, for pairs (instance, instance), we
identify the concepts related to the instances. In this
case we create a new concept or use an already
identified concept. The approach to ontology
learning from folksonomies is semi-automatic. It
suggests to the user concepts, instances, properties,
and relations between concepts. In the interacting
with user phase, the user takes all decisions
concerning the creation of concepts, instantiation of
concepts and relations. After the user decision
taking, the base ontology is created. Each entity in
the base ontology is linked by means of the
reference metaproperty to the source tag in order to
maintain the traceability with the folksonomy.
3.2 Ontology Evolution from
Folksonomies
Figure 2 shows our approach to evolve ontologies
from folksonomies. A folksonomy is produced by
user tagging activity in any social tagging system.
Tags represent the domain according to the users’
perspective and they are formally described using
SCOT ontology. The task ontology contains the
rules to extract data from folksonomies and to
populate the SCOT. The base ontology is created by
ontology engineers or some ontology learning
process. Our purpose is to respond to changes in the
folksonomies to update the base ontology. If the
folksonomy is changed, the base ontology may also
be modified. Our approach starts with reading a
folksonomy in order to extract new and relevant
information to be added to or removed from the base
ontology. As the SCOT is populated with
information from the folksonomy, the addition or
removal of instances means possible changes to be
done in the base ontology.
Figure 2: Approach to ontology evolution.
3.2.1 Add Concepts in Base Ontology
When new tags are added to the folksonomy, the
base ontology may have to be updated with new
entities. After identifying the changes, we identify
similarity relations between the extracted tags and
the entities in the base ontology (Figure 3) using an
ontology alignment method. With this method, we
know which parts of the base ontology are affected
by the changes in the folksonomy.
Besides, we identify the proper position where
the new entity should be added. In this work we use
the partial ontology alignment method named
Partial Ontology Alignment Method - POAM
(Freddo et al., 2007). For example, a new instance
and its associated properties in the SCOT ontology
are compared to the other SCOT’s instances
obtaining a set of similar instances. We search in the
base ontology the concepts linked to the instances in
the set by the reference metaproperty. At this
moment, the user has to decide which relation the
new instance has with the linked concepts of the
base ontology: none, subsumption, equivalence,
sibling or instance of.
3.2.2 Remove Concepts in Base Ontology
Each concept in the base ontology is referenced by
one or more instances of SCOT’s concepts. When a
tag is removed from the folksonomy, we verify
which concepts are linked to this tag. If the tag is
linked only to one concept in the base ontology, we
suggest to the user to remove the concept from the
base ontology. If the tag is linked to two or more
concepts in the base ontology, we do not remove the
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
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concept. We only update the reference meta-
property. As in the ontology learning, this approach
to ontology evolution creates suggestions for
changes which may be help knowledge engineers in
taking final decisions.
Figure 3: Ontology evolution and ontology alignment.
4 CASE STUDY
A case study is presented to demonstrate the
complete approach comprising ontology learning
and evolution.
4.1 Case Study to Ontology Learning
from Folksonomies
We demonstrate the ontology learning with the tags
hotel, accommodation, room, luxury, Paris were
retrieved from some folksonomy in the tourism
domain. We use these tags to populate the SCOT
ontology. Figure 4 shows the SCOT ontology where
each instance of the tag concept has some properties
such as reference_tag, equivalentTag,
spelling_variant, used_by, and associatedTag_in.
The reference_tag property maintains the
traceability between a tag in the folksonomy and the
instance of the tag in SCOT.
After populating the SCOT ontology, we identify
the relation between pair of tags. In this example, we
have the following pairs: (hotel, accommodation),
(hotel, room), (hotel, luxury), (hotel, Paris),
(accommodation, room), (accommodation, luxury),
(accommodation, Paris), (room, luxury), (room,
Paris), and (luxury, Paris).
Figure 4: Instances of the SCOT’s tag concept.
Figure 5: Initial ontology.
Figure 5 shows the initial ontology with some
relations such as instance_of, has, and is a. Each tag
can be an instance or concept in the initial ontology.
With the suggestions represented in the initial
ontology (Figure 5), the user builds the base
ontology. In the base ontology, each concept has
properties and meta-properties (properties in SCOT
are meta-properties for concepts in the base ontology
and temporary concept). For instance, the concept
hotel has properties hasCity, hasRoom, and
hasClassification. The same concept has meta-
properties reference_tag, equivalentTag,
spelling_variant, etc.
4.2 Case Study to Ontology Evolution
from Folksonomies
We demonstrate the ontology evolution approach
with the inclusion of new tags (hostel, address) in
folksonomy (Figure 6). The new tags are instances
of SCOT’s tag concept. Based on the properties
defined for the tag concept in the SCOT ontology,
each tag can be seen as a temporary concept with
meta-properties. Thus, we align hostel and address
with concepts in the base ontology based on the
INTEGRATING SOCIAL WEB WITH SEMANTIC WEB - Ontology Learning and Ontology Evolution from
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251
POAM’s similarity value computed with several
metrics including property similarity ones. The
POAM detects the following alignments: hostel and
hotel, and address and city (Figure 7).
tag ontology
SCOT
Instances of
tag concepts
equivalentTag
hostel
address
spelling_variant
used_by
associatedTag_in
reference_tag
…...
…...
properties
Populate tag
ontology (SCOT)
folksonomy
hotelaccommodation
room luxury Paris
hostel address
Base
ontology
accommodation
hotel
room
luxury
Paris
city
hasClassification
hasCity
is a
instance_of
hasRoom
hostel
address
Ontology
Alignment
temporary
concepts
meta-properties
Figure 6: Evolving an ontology from new tags in the
folksonomy.
Figure 7: Similarity between temporary concepts and
concepts in the base ontology.
Hostel and address are temporary concepts that
were aligned with concepts city, accommodation,
room, hotel and luxury in base ontology. Based on
the found alignments, the user knows the proper
position where the concepts hostel and address are
added. Then we identify the relations between
following pair of tags: (hostel, hotel), (hostel,
accommodation), (address, city).
5 CONCLUSIONS
In Social Web, we have users building their
personomies online. However, the meaning of tags is
completely unspecified. Ontologies can describe
semantically such data.
By combining the facilities in annotating Web
resources in the Social Web and the expressiveness
power of Ontologies to describe resources in the
Semantic Web, we could provide an integrated Web.
In this work we describe an approach for
combine the Social Web and the Semantic Web.
According to Gruber (2008), “the challenge for the
next generation of the Social and Semantic Web is to
find the right match between what is put online and
methods for doing useful reasoning with the data”.
Motivated by the challenges of ontology
engineering and inspired by the success of social
web applications, we presented an approach to
ontology learning and evolution from folksonomies.
We use ontology alignment to support ontology
enrichment (add or remove entities) when changes
are detected in the folksonomy.
The implementation of this approach is currently
in progress. In a near future, we intend to evaluate it
analyzing the inferences that become possible with
the integration, the precision and recall in queries,
and the degree of user overloading with tasks related
to knowledge engineering related to the ontology
development and evolution.
ACKNOWLEDGEMENTS
The authors thankfully acknowledge the financial
support granted for this study by Fundação
Araucária (AMO 9902 – conv. 234/2007).
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