EVONTO
Joint Evolution of Ontologies and Semantic Annotations
Anis Tissaoui, Nathalie Aussenac-Gilles, Nathalie Hernandez
IRIT, Université Paul Sabatier, 118, route de Narbonne, Toulouse, France
Philippe Laublet
STIH, Maison de la recherche, Université de Paris Sorbonne, 28 Rue Serpente, 75006 Paris, France
Keywords: Ontology and terminology evolution, Semantic annotation, Protégé Plug-in.
Abstract: In dynamic contexts, ontologies and their lexical component (termino-ontologies or TOR) have to
frequently adapt to domain evolutions, new uses and new user needs. Among all depending data, ontology-
based semantic annotations also are regularly updated to annotate new documents or to reflect new points of
view. Within the TextViz ontology-based annotation framework, we propose the EvOnto tool and method
that supports a coherent joint change management of termino-ontologies and semantic annotations as well
as quality criteria to evaluate automatic text annotations and to detect lacks in the ontology.
1 INTRODUCTION
Early definitions of ontologies insisted on their
consensual and stable content that would guarantee a
sharable conceptualization of domain concepts and
knowledge. In the Semantic Web, semantic
annotations take the form of indexes based on
ontology concepts and/or relations. In this scope,
ontologies play a key role: they provide the domain
vocabulary used to tag or describe the content of
unstructured web pages.
Ontology based semantic annotations require that
they are rich enough to describe document content;
that concepts have a large variety of labels to
anticipate lexical variations of terms. Then
ontologies have to be frequently adapted to the new
data that they describe, to the new applications that
use them and to domain evolutions (Maedche,
2002). Changes on an ontology aim at making it
more appropriate to model domain knowledge and
its uses (Flouris et al., 2006). This process may be
complex and challenging, even more for large
ontologies or in dynamic contexts. It raises practical
issues, like how to appreciate the right changes that
are needed, how they impact the ontology uses and
what are the consequences of a change on other
ontology elements. Research on ontology evolution
produced a taxonomy of ontology changes,
identified the main stages of this process, proposed
versioning tools, the logical consequences of change
on the overall ontology (Stojanovic, 2004) (Klein,
2004). Still it has to take into account a large variety
of reasons that lead to update ontology content.
Our research follows this trend but we focus on
some specific features. First, we manage termino-
ontologies, i.e. ontologies with a lexical component,
where terms appear as entities in the ontology in
addition to conceptual classes. Second, ontology
change is driven by the annotation needs, when new
data (textual documents for us) require annotation.
Third, we want to reduce the negative impact of
each evolution (on the ontology or the annotation)
by showing these impacts at each step. For instance,
negative impacts could be to run a useless new
annotation or to produce lower quality annotations
from a new semantic structure.
Managing terms and semantic annotations in the
ontology evolution process extends the classical
issues of change management: how can we ensure
the consistency of the ontology and the anotations
when one of the two is modified? which is the
optimal consequence of a given change and how to
guide the ontology engineer in its selection? How to
reduce the impact of a change on the TOR and the
annotations? how to detect evolution needs from
226
Tissaoui A., Aussenac-Gilles N., Hernandez N. and Laublet P..
EVONTO - Joint Evolution of Ontologies and Semantic Annotations.
DOI: 10.5220/0003658602260231
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 226-231
ISBN: 978-989-8425-80-5
Copyright
c
 2011 SCITEPRESS (Science and Technology Publications, Lda.)

new uses of the ontology? All these questions are the
basis for defining the EvOnto (Evolution of
Ontology) method and tool. EvOnto enriches the
TextViz annotation platform (Reymonet et al.,
2009), a Protégé plug-in dedicated to the semantic
annotation of domain specific text collections.
In this paper, we motivate and detail the EvOnto
method. We first present the Dynamo project and
some specific constrains on annotations and on the
TOR model. Then we report a short state of the art
about ontology evolution. The core of the paper is
dedicated to the presentation of the EvOnto method
and support tool, illustrating the two processes: from
new annotations to ontology changes and back, from
ontology evolution to annotation update.
2 THE DYNAMO PROJECT
The overall purpose of the DYNAMO project
(DYNAMic Ontologies for information retrieval,
http://www.irit.fr/DYNAMO) is to design a method
and to implement a set of tools that manage domain
ontology evolution and its use for semantic
annotation and search in dynamic context. Changes
in the context may mean new domain knowledge to
be taken into account, new documents to be added to
the searched collection or new user needs or queries.
One of the innovations in DYNAMO is the joint
specification of two modules, one for ontology
evolution and one for semantic annotation and
search. The goal is twofold: on the one hand, be able
to take into account the document collection
evolution to adapt the ontology, and, on the other
hand, to manage the annotation update in keeping
with any ontology change. A key feature of
DYNAMO is to involve three application domains
proposed by 2 companies and a research lab: (i)
research in archaeology of techniques, (ii) diagnosis
and fault repair of car electronic components and
(iii) diagnosis and management of software errors.
DYNAMO is concerned with using and
managing the evolution of enriched ontologies with
a lexical component – or terminology -, that we will
call from now on termino-ontological resources
(TORs). TORs contain representations of domain
concepts, relations and properties as well as terms
that designate these concepts. Terms are considered
both as linguistic formulations of concepts and as
means to keep track of concepts or concept instances
in documents. In DYNAMO, the annotation process
relies on mapping terms with the language used in
text. Annotations are graphs connecting term and
concept instances, terms being anchored in text at
precise locations.
To benefit of the web standards, the DYNAMO
TOR meta-model relies on OWL-full (Reymonet et
al., 2009). obir:Term and obir:Domain
Class are two meta-classes that specialize owl:
class. The denote relation from Term to
Domain Class may connect one or several
terms to one or several meanings. Each obir:
Term instance is a term occurrence that
designates a concept instance. In Textviz,
Protégé interfaces have been adapted to manage
obir:term in addition to standard OWL classes
and properties.
3 ONTOLOGY EVOLUTION
Ontology evolution appears in the literature in the
scope of ontology maintenance as part of more
global methods like KAON (Maedche et al., 2003).
Several findings propose guidelines and tools (i) to
identify change needs (Cimiano et al., 2005) or to
detect new knowledge that leeds to some change in
the ontology (Klein, 2004), (Plessers et al., 2005) (ii)
tools that implement change application (Stojanovic,
2004) (Flouris, 2006); (iii) checking rules to
maintain the ontology consistency (Stojanovic,
2004) (Djedidi, 2009) or (iv) versioning (Klein,
2004). Other studies defined an overall evolution
process that includes both the analysis of the
ontology evolution impacts and the propagation of
any change on all the applications and artefacts
depending on the modified ontology (Stojanovic,
2004) (Klein, 2004) (Luong, 2007). (Flouris 2006)
identifies the features that differentiate evolution
support tools from version management tools. In
EvOnto we focus on assisting ontology evolution,
not versioning.
Some tools are particularly interesting for us:
KAON (http://kaon.semanticweb.org) is one of the
pioneer ontology edition and engineering platform
from text. It integrates an ontology evolution support
(Maedche et al., 2003). Several change types are
defined as subtypes of the ChangeLog class:
addEntity, deleteEntity, ModifyEntity, which bear
on a single structure. KAON does not deal with
complex changes like splitting or merging concepts.
ECCO offers a collaborative and contextual
environment to build ontologies. It is one of the
blocks of the CoSWEM (Corporate Semantic Web
Evolution Management) evolution management tool
(Luong et al., 2007). CoSWEM manages the way
vocabularies can be enriched from text when these
EVONTO - Joint Evolution of Ontologies and Semantic Annotations
227

vocabularies are used for text semantic annotation.
A history log file keeps track of the process followed
to build or update an ontology thanks to RDF meta-
data. More recently, the EVOLVA plug-in of the
NEON tool-kit for ontology engineering provides
facilities to enrich domain ontologies. EVOLVA
reuses texts and parts of reusable resources like
existing ontologies and databases (Zablith., 2009).
EVOLVA extracts terms from text corpora, which
leads to define new concepts.
4 THE EvOnto METHOD
EVOLVA and CoSWEM share similar goals as
ours, but none of then is able to both manage TORs
and keep an ontology consistent with annotations.
This is why we have proposed the EvOnto tool and
method for ontology and annotation evolution.
EvOnto is intended to be used by a single person
(the knowledge engineer in the following) in charge
of a coherent change management of termino-
ontologies and semantic annotations. Text
collections have a reasonable size (from 1000 up to
5000 texts in each of the three case studies of the
DYNAMO project). They are very homogeneous
(each document has the same structure and the same
type of content). Depending on the case study, either
the whole document or only some paragraphs are
annotated.
EvOnto relies on three original features: it pays
special attention to terms that contribute to define
annotations; it defines quality criteria to evaluate the
result of automatic text annotation and to detect
lacks in the ontology; it assumes that the ontology
quality is evaluated through its use for annotation.
EvOnto leads to define “minimal” and “document-
driven” domain ontologies: they are detailed enough
to provide an optimal annotation of the text
collection, but they do not pretend to fully describe a
domain.
The method defines a cyclic process: after the
annotation of new documents, EvOnto proposes to
check their quality and indentify needs for the
ontology evolution. Various TOR change operators
are proposed and, for each of them, EvOnto allows
to tune the consequences of this change within the
TOR and back to the annotations.
The first process is data driven and deals with the
impact of new annotations on the TOR following
three steps.
Firstly, documents are added to or withdrawn
from the text collection. Document withdrawal can
lead to no longer use some concepts and terms for
annotations. Up to now, no special device is
provided to automatically check for unused items.
But EvOnto makes it possible to “view the uses” of
any a selected term or concept: it displays the list of
all the documents annotated by this item.
Secondly, after adding new documents to the
collection, the knowledge engineer can launch the
annotation module. The originality of EvOnto is to
propose to define quality criteria for the annotations,
and to evaluate new annotations according to these
criteria. A criterion gathers a set of concepts and/or
relations that are expected to be found in each text
annotation. An annotation will not be valid unless at
least an instance of these concepts/relations or one
of their sub-concepts is used for annotation. In
general, these concepts are very high level classes in
the ontology. Checking if annotations are compliant
with the criteria results in a score for each document
that reflects how well it is described by the
annotations.
Thirdly, the knowledge engineer can browse the
document list, starting with those with lower scores.
Although this process is manual, it is efficiently
guided because the missing expected concepts as
well as the part of text without annotation are well
identified. These lacks may lead to changes in the
ontology, in general to the addition of new terms to
existing concepts or the addition of new sub-classes
to the expected ones in the annotation criteria.
We have presented here how new annotations
may drive ontology evolution in EvOnto. We will
detail the steps of the reverse process, when
modifications in the ontology require updating
annotations.
Ontology evolution is driven by the knowledge
engineer. We specified the EvOnto method so that it
could help him to be aware of the impact of any
change both on the TOR and the annotations, and to
let him adapt the change consequences case by case
if needed. The process takes place in 4 stages.
EvOnto supports (1) the selection of the entity to be
modified and the selection of a type of change
selection, (2) the decision making about the
consequences or selection of an evolution strategy
and (3) the impact adaptation. Then (4) it propagates
the change on the annotation in a way such as it
avoids an overall new annotation and it reduces side
effects.
To express an evolution need, we have identified
all the possible types of changes to be made on a
TOR, and made explicit their meaning (Tissaoui,
2009). This new typology of change extends the one
proposed by Stojanovic in 2004 in a way that makes
it suitable for Reymonet’s TOR meta-model (2009).
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In particular, changes may occur on terms or on
term-concept relations if the domain vocabulary
evolves. Changes may be either elementary (bearing
on one type of meta-model structure, like
DeleteConcept or CreateTerm) or composite if
several structures are involved (like merge or split).
Each type of change may lead to various options
when deciding what to do with related structures in
the TOR We call an evolution strategy a coherent
way to manage the impact of a change on all the
related structures with the one(s) that is (are) being
modified. For instance, a possible strategy when
deleting a concept is to delete all its sub-classes and
all related terms. We have defined strategies that
extend the one proposed by Luong (2007) and
Stojanovic (2004) in order to deal with the
terminological component of a TOR.
For each change and each evolution strategy, the
corresponding consequences of this change can be
shown to the knowledge engineer before these
consequences are actually performed. In EvOnto we
have defined this stage as a decision process. A first
innovation at this stage is that consequences on
annotations are also taken in to account. EvOnto lists
all the document tagged with the modified item
(direct consequences on annotations) as well as the
documents tagged by related structures that will be
modified as a consequence of the initial evolution
(we call these un-direct consequences on
annotation). A second innovative option here is to
let the knowledge engineer adapt all the
consequences that are not appropriate according to
him. Strategies offer a global and rapid solution that
can be adapted more precisely according to the
evolution semantics.
5 EvOnto TOOL
The method is implemented in a support tool, the
EvOnto plug-in, which includes the TextViz
annotation and TOR management tool. In the
following, we will illustrate the two processes
presented above on one of the three case-studies of
the DYNAMO project. The document collection is
made of bug-tracking reports, and the TOR
represents the main concepts of the software
maintenance domain. This TOR includes its high
level concepts (Trigger_Event, Default,
Component), some of the associated terms (T_
Default, T_Problem and T_Bug for ins-
tance) and semantic relations like Concerns_
Default, Affects-Component, Causes,
Located_in.
To ensure an efficient sematic search, each bug
report file must be annotated with at least the
following data:
 an instance of each one of the two concepts:
Default and Component, or of their sub-
classes;
 An Affects_Component relation between
these instances.
The EvOnto interface allows to capture
annotation quality criteria. The user can select
concepts. It means that he expects that each
document should contain one or several instances of
these concepts or one of their sub-classes. He can
also select the expected relations between these
concepts. Once a set of criteria has been defined, it
is matched to the current annotations. Results are
displayed and the annotations not fulfilling the
criteria are indicated. For each document where one
or several concepts are missing, the knowledge
engineer can display its content and select words
that have not been annotated yet to define new
concepts or to add terms to the ontology.
The DeleteConcept is an elementary change
operation. EvOnto opens a new window that
displays all the consequences of this change on the
TOR and the annotation according to the default
strategy for DeleteConcept, which is to attach
the concept subclasses and related terms to the father
concept of the deleted one. These consequences are
defined so that they keep a high consistency between
the TOR and the document annotations. The
knowledge engineer can select another strategy and
check its consequences, or he may decide to give up
the modification.
Whatever the selected change, concept and
strategy, the information is distributed in 4 areas:
name of the current change and the modified
concept (Changed Concept); information about the
selected strategy (or the default one); situation of
this concept in the concept hierarchy (concept
Browser) and its related terms (classes starting with
T_ in the Term Browser); all the change
consequences (Lexical and Conceptual Information),
with the upper part dedicated to consequences on the
TOR, and the lower part that lists the text with
annotations that could have to be changed.
In the running example, three strategies are
possible for DeleteConcept:
 Attach the subClasses to the superClasses
(default strategy),
 Attach the subClasses to the DomainThing,
 Delete the subClasses.
Then either the knowledge engineer validates
EVONTO - Joint Evolution of Ontologies and Semantic Annotations
229

this strategy and all the previous changes, or he
selects another strategy. Whatever the strategy, he
can decide to modify only a part of the
consequences. The ADJUST CONSEQUENCES
option is dedicated to support such fine grain
changes.
The process is almost the same for a
composite change. The major changes are the
proposed strategies and the adjustment interface.
At the time being, ten change operations have
been implemented with the corresponding strategies
and the adequate window to adapt the consequences
of the change.
6 PROPAGATION ON
ANNOTATIONS
The consecutive evolution of annotations takes place
in two stages:
 Detection of Inconsistent Annotations after
the ontology has evolved; these annotations
are those referring to one of the modified
terms, concepts or relations;
 Modification of Inconsistent Annotations in
keeping with the new ontology content.


ModifiedTOR
Detectionofinconsistentannotations
Modificationofdocumentannotations
Termsearch
indocuments
SPARQLQueryonthe
instancegraph
Automaticannotation
EvolutionStrategies
Figure 1: Propagation of TOR evolutions on annotations.
Two methods have been identified for the first
stage. The first one requires to browse the
annotation graphs and to look in these graphs for
modified items in the ontology. For instance, any
RDF triple in which appears a deleted concept is no
longer valid. To search for this type of data, we
defined SPARQL queries that search the set of all
annotation graphs.
The second method consists in browsing the
documents themselves instead of the annotations.
The ideas would be to look for the terms denoting
any of the changed entities in the TOR.
The second stage of the propagation process
aims at fixing the annotations once the
inconsistencies have been identified. If the second
method is used for checking the documents that have
inconsistent annotations, the only possible way to fix
the annotations is to run again the automatic
annotation algorithm with the new TOR. The
computation time is short enough to make this an
easy solution. Nevertheless, many annotations can
be improved manually, in particular the conceptual
relations in the annotation graph. Because the
implemented relation extraction solution is very
basic, some of the found relations are trivial and
manually modified. In that case, running automatic
annotations may lead to lose all the manual work. It
would also require a new manual checking of all the
documents and their annotations, which is quite time
consuming.
The first method (detection of inconsistent
annotations) makes it possible, in most cases, to
locally modify the graphs in a “surgical way”. We
apply here annotation evolution strategies (AS).
Each strategy is mapped to one of the TOR
evolution strategies. The goal of a strategy is to fix
the inconsistencies due to changes in semantic
annotations. Each change operation is associated a
set of rules that will modify the annotations.
We have started to evaluate how helpful EvOnto
is when evolving an ontology and a document
collection. The tool makes it possible to take better
justified decisions, but we have to check whether
these decisions are more accurate than without
EvOnto. Indeed, it is quite complex to evaluate an
interactive tool like EvOnto. We have not yet carried
out a full evaluation of the overall process, but we
have tested several measures on the results obtained
in two of the DYNAMO case-studies: one in the
domain of software bug tracking and the other one in
the domain of car electronic fault diagnosis.
We have carried out several SplitClass
modifications and compared the time required to
make such changes in EvOnto and in Protégé
without any evolution tool. We have not yet
considered the impact on the annotations because we
could not have made such modifications with
Protégé. The time required to perform a change and
its consequences in the TOR can be estimated to
about 1 minute using EvOnto, 2 minutes using
Protégé. The analysis of the change log obtained in
both cases shows that EvOnto takes into account all
the the types of changes and their consequences,
where as Protégé only manages 3 types of changes:
C0 is deleted and new1 and newC2 are created.
In addition, EvOnto provides a qualitative
improvement of TOR and annotation evolution. The
tool anticipates all the consequences of a change on
the TOR and on the annotations, which avoids
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230

forgetting some of the impacts of a change. For
instance, in Protégé, it is up to the user to check all
the ObjectProperties in which a modified concept is
involved as domain or range, on to anticipate by
moving sub-classes before deleting their super class.
Moreover, in Protégé, taking into account
annotations is completely set apart. In TextViz, it is
very easy to perform a new annotation after each
evolution, or to carry out local modifications by
propagating changes.
7 CONCLUSIONS
We have proposed EvOnto, a method and tool for
ontology evolution that takes into account its use for
semantic annotation. EvOnto implements several
principals for a consistent evolution of ontologies
and semantic annotations. Our study brings several
innovations compared with previous works. First,
we are interested in ontologies with a lexical
component (TORs), defining according to a meta-
model where terms are represented as classes.
Second, EvOnto assists the evolution process by
providing the knowledge engineer with information
on the consequences of a change before it is
implemented. These consequences take into account
the structures linked to the one modified in the TOR
as well as the semantic annotations using this
structure. This information supports decision making
and avoid costly trial and attempts.
We go on improving EvOnto by adding new
change operations and their corresponding strategies
to manage the consequences in the TOR and on
annotations. Most of our effort now is dedicated to
the evaluation of EvOnto. This evaluation raises
issues related to the time required to build an
ontology, to the difficulty to judge the quality of an
ontology and even more of semantic annotations.
Thanks to the three case studies of the DYNAMO
project, we have data, ontologies and domain experts
to carry out several evaluation experiments.
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