=Paper=
{{Paper
|id=Vol-200/paper-3
|storemode=property
|title=A Matchmaking-based Ontology Evolution Methodology
|pdfUrl=https://ceur-ws.org/Vol-200/02.pdf
|volume=Vol-200
|dblpUrl=https://dblp.org/rec/conf/caise/CastanoFM06
}}
==A Matchmaking-based Ontology Evolution Methodology==
https://ceur-ws.org/Vol-200/02.pdf
A Matchmaking-based Ontology Evolution
Methodology?
Silvana Castano, Alfio Ferrara, and Stefano Montanelli
Università degli Studi di Milano
DICo - Via Comelico, 39, 20135 Milano - Italy
{castano,ferrara,montanelli}@dico.unimi.it
Abstract. In this paper, we present the H-Change methodology we
have specifically conceived for evolving independent ontologies in open
networked systems. Furthermore, we describe the change detection tech-
niques based on semantic matchmaking for determining the most ap-
propriate location where to frame new incoming knowledge within an
existing ontology. Change assimilation techniques for evolving ontology
metadata to incorporate the new incoming knowledge at different inte-
gration levels are also outlined.
1 Introduction
The introduction of the Semantic Web vision and the success of open networked
infrastructures, such as P2P and Grids, have been produced a new attention to
the problem of distributing information resources and data over a high number
of independent peers [1]. In order to semantically enhance the capability of re-
trieving and sharing this large amount of resources, the use of ontology-based
networked systems, either Web-based or P2P-based, are seen as a wide reposi-
tory of knowledge, where peers interact each other and new concepts and data
descriptions are continuously acquired by peers and/or can change during time.
In such a scenario, the ontologies themselves have to be open and independent,
in that concept descriptions are given also in terms of concepts defined else-
where in the system and shared by the peers. A key requirement in such an
open and dynamic scenario, is to equip the peers with appropriate methods and
techniques for automatically evolving their ontology knowledge by assimilating
new concepts that have been acquired from the network.
In this paper, we first describe the H-Change methodology specifically con-
ceived for evolving independent ontologies in open networked systems. Then, we
focus on the change detection techniques based on semantic matchmaking for
determining the most appropriate location where to frame new incoming knowl-
edge within an existing ontology. Change assimilation techniques for evolving
ontology metadata to incorporate the new incoming knowledge at different in-
tegration levels are also outlined.
?
This paper has been partially funded by NoE INTEROP, IST Project n. 508011 -
6th EU Framework Programme.
The paper is organized as follows. In Section 2, we describe the H-Change
methodology. Matchmaking-based change detection techniques are discussed in
Section 3. In Section 4, we provide some considerations on the presented H-
Change methodology and we discuss some applicability issues. Finally, related
work and concluding remarks are provided in Section 5 and 6, respectively.
2 The H-CHANGE methodology
In open networked systems, like P2P and Grids, peers act as independent agents
with their own knowledge (i.e., peer ontology) and interact each other by submit-
ting discovery queries and by replying with relevant knowledge. Peers have no
prior reciprocal knowledge and gradually discover the location and the contents
of the other peer ontologies by i) submitting to the system a number of discovery
queries, with one or more target concepts of interest; and ii) by analyzing the
replies provided by the other peers to these queries (independency requirement).
Moreover, each peer has the responsibility to autonomously evolve/enrich its
own ontology in a consistent way according to the relevance of the incoming
knowledge acquired from the network (autonomy requirement). To this end, the
H-Change methodology (see Figure 1) is defined for evolving independent on-
tologies in open networked systems and it is articulated in the following phases.
new
incoming Change detection
concepts
Acquisition Ontology Change
matching localization
matching
results
Peer candidate assimilation
ontology
concepts strategy
Restructured
Change assimilation peer
ontology
Change Assimilation & Validation
implementation restructuring
restructured
results
Evolved
peer
ontology
Fig. 1. The H-Change methodology
1. Acquisition. The acquisition phase involves the definition of one or more
discovery query and their submission to the system with the aim to locate
the external ontologies capable of providing relevant knowledge. In response
to the submitted discovery queries, a set of new incoming concepts are re-
ceived and need to be considered in order to evolve/enrich the peer ontology.
New incoming concepts constitute the proposed ontology changes, already
represented according to an ontological specification.
2. Change detection. The change detection phase has the goal of evaluating
the impact of a new incoming concept on the existing peer ontology knowl-
edge. To this end, the ontology matching and the change localization phases
are defined.
– Ontology matching. The ontology matching phase exploits ontology match-
making techniques to semantically compare the new incoming concepts
against the peer ontology in order to produce a set of matching results
containing the discovered semantic affinities.
– Change localization. The change localization phase exploits the match-
ing results in order to detect i) the best assimilation point in the peer
ontology for each new incoming concept, namely the candidate concept;
and ii) the assimilation strategy to apply for the insertion according to
a threshold-based mechanism.
3. Change assimilation. The change assimilation phase is responsible for
applying the peer ontology changes previously detected. This phase is com-
posed of the change implementation and the assimilation and restructuring
phases, as follows.
– Change implementation. The change implementation phase modifies a
candidate concept by assimilating a new incoming concept in order to
produce a restructured concept. The kind of restructuring operations
and resulting restructured concepts depends on the selected assimilation
strategy.
– Assimilation and restructuring. The restructured concepts are intro-
duced in the peer ontology and the changes become effective. In this
phase, consistency check is also performed on the resulting restructured
peer ontology.
4. Validation. The validation phase enforces conventional recovery facilities
to ensure that any change performed in the change assimilation phase can
be backtracked if required. In this phase, the restructured peer ontology be-
comes effective and a final evolved peer ontology is produces as a result.
This phase can be implemented by adopting, for instance, ontology version-
ing techniques (see Section 6).
Expected user interactions. The H-Change methodology enforces an inter-
active approach, allowing the designer to validate and choose among proposed
alternative choices. To limit the amount of manual activity required, default
choices in each phase of the H-Change methodology are expected. In particu-
lar, two kinds of interactions with the designer are expected in the H-Change
tool support:
– Parameter settings: the designer is required to properly set all the involved
parameters (i.e., the matching threshold in the change localization phase).
A default value is provided and the designer can modify it if required.
– Result inspection and validation: the designer is asked to validate the results
of the automated change detection and change assimilation phases. However,
the proposed solution can be always modified to manually force a specific
design choice if required.
As an example, we consider the peer P together with a portion of its peer ontology,
called PublicationOntology, as represented in Figure 2 1 . In this example, peer P is
receiving two new incoming concepts with their related context, namely Volume
and Library, in response to a discovery query. In this scenario, ontology evolu-
Peer ontology
contains
PublicationOntology Thematic
Section ≤1 title
=1 publisher
volumes =1 Volume
author ≥1
year =1 ≥1 language
Publication
author
Magazine Publication
contains Person
number =1
New incoming concepts
Journal Book Catalog
title =1 contains Library
author ≥1
=1 title ≤1 title Volume
contains address =1
Chapter contains
Collection peer P
Legenda
≥n property name
Quantified property name Cardinality
Concept Concept Name property property ≤n property name
restrictions property name restrictions
=n property name
SubClassOf Restriction Restriction
relation domain range
Fig. 2. The peer P with a portion of its peer ontology PublicationOntology and two new
incoming concepts
tion regards the capability of peer P to autonomously decide whether and how it
can acquire the new externally received knowledge, evolving its own peer ontol-
ogy PublicationOntology accordingly. In order to address such requirements, the
H-Change methodology relies on the H-Match ontology matchmaking tech-
niques we have developed in the framework of the Helios project for matching
independent ontologies [2, 3].
1
In Figure 2, a graphical representation of the OWL PublicationOntology is provided.
The complete OWL specification is available at http://islab.dico.unimi.it/ontologies/-
PublicationOntology.owl
3 Using matchmaking techniques for change detection
In this section, we first describe the H-Match matchmaking techniques and
then we discuss the role of matchmaking for evaluating the relevance of new
incoming concepts and for selecting the most appropriate change assimilation
strategy.
3.1 Semantic matchmaking with H-MATCH
H-Match performs ontology matching at different levels of depth by deploy-
ing four different matching models spanning from surface to intensive matching,
with the goal of providing a wide spectrum of metrics suited for dealing with
many different matching scenarios that can be encountered in comparing con-
cept descriptions of real ontologies. H-Match takes two ontologies as input and
returns the mappings that identify corresponding concepts in the two ontologies,
namely the concepts with the same or the closest intended meaning. H-Match
mappings are established after an analysis of the similarity of the concepts in
the compared ontologies. In H-Match we perform similarity analysis through
affinity metrics to determine a measure of semantic affinity in the range [0, 1].
A threshold-based mechanism is enforced to set the minimum level of seman-
tic affinity required to consider two concepts as matching concepts. Given two
concepts c and c0 , H-Match calculates a semantic affinity value SA(c, c0 ) as
the linear combination of a linguistic affinity value LA(c, c0 ) and a contextual
affinity value CA(c, c0 ). The linguistic affinity function of H-Match provides a
measure of similarity between two ontology concepts c and c0 computed on the
basis of their linguistic features (i.e., concept names). For the linguistic affinity
evaluation, H-Match relies on a thesaurus of terms and terminological relation-
ships automatically extracted from the WordNet lexical system. The contextual
affinity function of H-Match provides a measure of similarity by taking into
account the contextual features of the ontology concepts c and c0 . The context
of a concept can include properties, semantic relations with other concepts, and
property values. The context can be differently composed to consider differ-
ent levels of semantic complexity, and four matching models, namely, surface,
shallow, deep, and intensive, are defined to this end. In the surface matching,
only the linguistic affinity between the concept names of c and c0 is considered
to determine concept similarity. In the shallow, deep, and intensive matching,
concept similarity is determined by considering both linguistic and contextual
affinities. In particular, the shallow matching computes the contextual affinity
by considering the context of c and c0 as composed only by their properties. Deep
and intensive matching extend the depth of concept context for the contextual
affinity evaluation of c and c0 , by considering also semantic relations with other
concepts (deep matching model) as well as property values (intensive matching
model), respectively. The comprehensive semantic affinity SA(c, c0 ) is evaluated
as the weighted sum of the Linguistic Affinity value and the Contextual Affinity
value, that is:
SA(c, c0 ) = WLA · LA(c, c0 ) + (1 − WLA ) · CA(c, c0 ) (1)
where WLA is a weight expressing the relevance to be given for the linguistic
affinity in the semantic affinity evaluation process.
According to the matching model definitions, we note that the surface model
is suited for poorly structured ontologies with very simple concept descriptions,
the shallow and the deep models are suited for dealing with schematic ontolo-
gies with taxonomic concept descriptions, and the intensive model is suited for
articulated ontologies with rich concept descriptions. H-Match has been exten-
sively tested on several real ontology test matching cases in order to evaluate the
matching models with respect to performance and quality of results [2]. By an-
alyzing the obtained results, we note that the most accurate and precise results
are achieved with the deep and intensive matching models provided that the
ontology descriptions are detailed enough. For ontology evolution purposes, we
are interested in exploiting the H-Match results to discover the best insertion
points where to assimilate a given new incoming concept. To this end, since we
focus on OWL ontologies, the intensive matching model has been selected for
the change detection phase, in that it guarantees the highest level of accuracy
in finding the concept that best matches a new incoming concept by consider-
ing all the contextual features characterizing the involved concepts. A detailed
description of H-Match and related matching models is provided in [2].
3.2 Change detection and assimilation
In the change detection phase of the H-Change methodology, we exploit the re-
sults of H-Match in order to detect the candidate concept of the peer ontology
that represents the best assimilation point for a new incoming concept and we
choose the most appropriate strategy to adopt in the next phase of H-Change
for implementing the assimilation (see Figure 3). Given a new incoming concept
c0 and a peer ontology O, H-Match returns the ordered list of the top-k match-
ing concepts in O with respect to c0 . For the evolution purposes, since we are
interested in the best assimilation point for c0 in O, the matching concept at the
top of the matching results list is always chosen as the candidate concept c for
the assimilation of c0 . The assimilation of c0 in O can be implemented according
to two strategies, called assimilation-by-merging and assimilation-by-alignment.
Assimilation-by-merging. The assimilation-by-merging strategy is adopted
for assimilating c0 in O when c0 and the candidate concept c have a high level of
semantic affinity. The idea is that a high level of semantic affinity means that c0
and c have the same or strongly similar intended meaning and denote the same
or similar real object in a domain. For this reason, we always import the descrip-
tion of c0 into O and we choose to add c0 to O (i.e., add operation) or to unify
them into a new, unique concept c (i.e., unify operation). This strategy enforces
a heavy assimilation, in that the peer ontology knowledge is always modified and
restructured to explicitly incorporate the definition of c0 .
Assimilation-by-alignment. The assimilation-by-alignment strategy is adopted
when there is a lower level of semantic affinity between c0 and c. In this case,
Matching Strategy selection
New incoming
concept c'
SA(c,c') ≥ s
Merge
H-MATCH
Selection
of the
SA(c,c')
candidate
concept c
Alignment
SA(c,c') < s
Peer ontology
Fig. 3. The change detection phase
there is a weak correspondence between c0 and c that denotes a weak similarity
between the intended meaning of the two concepts. For this reason, the change
represented by c0 is not considered to be strong enough for modifying the current
concept descriptions. However, we keep track of the change by defining a mapping
m between c and c0 that is defined as a triple of the form m = hc, c0 , SA(c, c0 )i,
where SA(c, c0 ) denotes a measure in the range (0,1] of the semantic affinity
holding between c and c0 . We distinguish between a local mapping (i.e., map
operation), where the definition of the new incoming concept is imported as
a local concept by the peer, and a distributed mapping (i.e., dmap operation),
where the definition of the new incoming concept is not imported by the peer
and only a reference to the external peer ontology providing c0 is maintained.
This strategy enforces a light assimilation, in that the peer ontology knowledge
is kept unaltered and an appropriate mapping is set to align the peer ontology
with the external knowledge provided by c0 .
To enforce a semi-automated approach, in H-Change the choice of the as-
similation strategy is performed according to a threshold-based mechanism. A
threshold s represents the minimum level of semantic affinity required in order
to adopt the assimilation-by-merging strategy; based on the experimental results
of matching, high values for s have to be preferred (e.g., s ∈ [0.7, 0.9]).
Example. As an example of change detection, we consider the new incoming
concept Volume of Figure 2 and the peer ontology PublicationOntology of Figure 2.
In this example, we set a detection threshold s = 0.7 and Volume is matched
against the peer ontology PublicationOntology using the H-Match intensive model
and WLA = 0.5. The top-k matching concepts returned by H-Match for Volume
are shown in Figure 4. The candidate concept for Volume is the top concept in
the list, namely Book. Using a threshold s = 0.7, the assimilation-by-merging
strategy is then selected. With the same procedure, we determine the candidate
concept for the other new incoming concept Library of Figure 2. Since SA(Library,
Collection) = 0.62, the assimilation-by-alignment strategy is selected for the new
incoming concept Library.
SA(Volume,Book) = 0.875
SA(Volume,Journal) = 0.681
SA(Volume,Publication) = 0.610
SA(Volume,Magazine) = 0.539
Fig. 4. Top-k matching concepts for Volume against the peer ontology PublicationOn-
tology
4 Considerations and applicability issues
Matchmaking-based change detection techniques have the role to evaluate the
relevance of the new incoming concept with respect to the peer ontology and to
suggest the most appropriate change assimilation strategy according to the level
of identified semantic affinity.
When the assimilation-by-merging strategy is selected, add and unify oper-
ations are available to assimilate a new incoming concept that has a strong
relevance for the peer ontology. The designer can choose the most appropriate
operation between add and unify, driven by the semantic affinity value SA(c, c0 ).
Concept addition is the default operation and it is always applicable for the
assimilation-by-merging strategy implementation. In presence of very high val-
ues of SA(c, c0 ) the designer evaluates the opportunity of applying concept uni-
fication. The unify operation is implemented by a set of unification rules [4]. The
unification rules are conceived to address all the possible cases of incompatibility
or inconsistency among the definition of the two concepts to be unified. The new
incoming concept is imported with a heavy impact on the peer ontology, thus
the H-Change assimilation and restructuring phase has the role to preserve the
peer ontology correctness and consistency by performing a number of restruc-
turing operations. In this phase, also the instances already present in the peer
ontology need to be validated in order to verify that their descriptions are still
compliant with the changed concept definitions, and appropriate techniques can
be adopted to this end [5]. By handling OWL ontologies, the consistency check
can be enforced by exploiting well known reasoning tools, such as Racer [6]. On
the basis of the results, the designer can revise the restructured ontology until
its consistency is reached.
When the assimilation-by-alignment strategy is selected, map and dmap op-
erations are defined to assimilate a new incoming concept that has a limited
relevance for the peer ontology. The type of mapping to be used for implement-
ing the assimilation-by-alignment strategy is chosen by the designer depending
on the level of matching SA(c, c0 ). A threshold-based mechanism is adopted for
suggesting a default choice. By handling OWL ontologies, distributed mappings
can be implemented by using a XML namespace. A local/distributed mapping
keeps track of the new incoming concept with a light impact on the peer ontology
in terms of restructuring operations that are required.
For a detailed description of change assimilation operations and techniques,
the reader can refer to [4]
Applicability to open networked systems. When working in open net-
worked systems, the choice of the correct assimilation strategy depends on the
relevance of the new incoming concept for the peer ontology, and thus on the
usage that the new incoming concept is expected for. As shown in Table 1,
assimilation-by-merging techniques are suited for knowledge sharing scenarios
where a peer is interested in extending/enriching its ontology knowledge. In this
respect, add/unify operations ensure that the most relevant incoming concepts
are assimilated with a high level of integration with the existing peer ontology
knowledge. In this way, peers that need to cooperate on the basis of emerging and
dynamic requirements are enabled to exchange their knowledge and to negotiate
an agreement (i.e., a shared ontology) in order to enforce semantic collaboration
and sharing. On the other side, assimilation-by-alignment techniques are suited
for knowledge discovery scenarios where a peer is interested in maintaining in-
dications about the location of external ontologies capable of providing relevant
knowledge with respect to a given target. In this respect, add/dmap operations
allow to align the peer ontology knowledge with the external peer ontologies by
requiring a minimal modification of the peer ontology by defining a semantic
reference (i.e., local or distributed mapping) between a local concept and a new
incoming concept. Such references are then exploited to enforce semantic rout-
ing strategies by allowing to forward an incoming search query to those peers of
the system that have similar and relevant contents in their peer ontologies with
respect to the query target, thus enforcing knowledge discovery.
Assimilation-by-merging Assimilation-by-alignment
Recommended target • Knowledge sharing • Knowledge discovery
Assimilation operations • Add/unify • Map/dmap
and impact on the peer • Extended/enriched peer • Aligned peer ontology
ontology knowledge ontology knowledge knowledge
Enforced applications • Semantic collaborations • Semantic routing schemes
and sharing schemes
Table 1. Applicability of the assimilation strategies
5 Related work
In recent years, ontology evolution research work has mainly focused on the
problem of evaluating the impact of a modification of an ontology as a conse-
quence of requirement changes [5, 7]. In [8], ontology evolution is defined as a
complex operation that combines both organizational and technical aspects. To
this end, the authors propose a six-phase evolution process capable of dealing
with the ontology changes required by the mutated business requirements. The
six-phase process ensures the consistency of the ontology and possible depen-
dent artifacts after that any modification has been applied. Such an approach
has been extended in [9] in order to handle the evolution of multiple, distributed
ontologies. In this respect, a pull synchronization mechanism is used to avoid
possible inconsistencies while preserving the autonomy of each system node. In
particular, an evolution log ontology is defined to track the history of changes
applied to the ontology and to allow users to undo the changes that caused un-
desired effects. The overall approach has been implemented within the KAON
infrastructure for business-oriented ontology management 2 . Another framework
for ontology evolution and change management in a distributed and dynamic
environment is presented in [10]. The proposed framework is based on the on-
tology of change operations for providing a formal description of the ontology
modifications to be applied in order to perform a given evolution task. The
ontology of change operations is defined for the OWL knowledge model and
contains the basic change operations and the complex change operations. A ba-
sic operation describes the procedure for modifying only one specific feature
of the OWL knowledge model (e.g., type and cardinality restriction change),
while a complex operation describes an articulated change procedure and is
composed of multiple basic operations. Some examples of basic and complex
operations actually supported in the framework are presented. Moreover, a set
of rules and heuristics are defined to create new complex operations by com-
bining basic ones. A number of change representation formalisms are supported
with the aim to foster the implementation of robust and efficient tools for ontol-
ogy management. In such a context, the requirements that an ontology editor
should address for ontology evolution are discussed in [11]. In particular, the
authors emphasize that an editor should provide a set of evolution operations
according to the supported ontology models (functional requirement) and that
changes should be discovered semi-automatically by analyzing user behavior (re-
finement requirement). During the evolution process, the editor has to reflect the
user preferences (user supervision requirement) by providing advanced facilities,
such as change-visualization and inconsistency-detection (transparency and us-
ability requirements). Moreover, history of changes needs to be supported to
eventually undo any change applied to the ontology (auditing and reversibility
requirements). Due to the complexity of the evolution task, not all the previous
requirements have been fully considered for the implementation of the existing
ontology evolution tools that still only provide basic functionalities. The recent
success of distributed and dynamic infrastructures for knowledge sharing has
raised the need of semi-automatic/automatic ontology evolution strategies. An
overview of some proposed approaches in this direction is presented in [12], even
if very few concrete results have appeared in literature. In most recent work, for-
mal and logic-based approaches to ontology evolution are also being proposed.
In [13], the authors provide a formal model for handling the semantics of change
2
http://kaon.semanticweb.org/
phase embedded in the evolution process of an OWL ontology. The proposed
formalization allows to define and to preserve arbitrary consistency conditions
(i.e., structural, logical, and user-defined). Finally, the problem of ontology evo-
lution can be considered as a special case of the more general and well studied
problem of belief change. In [14], some of the most important concepts of the
belief change literature have been revised in order to apply them to the ontology
evolution context.
Original contribution of H-CHANGE. The main contribution of H-
Change is that it has been conceived to be suited not only for the local evo-
lution of a peer ontology, but also for evolving independent ontologies in open
and networked contexts, where distributed concepts definitions emerge dynam-
ically through interactions of independent peers and they are maintained by
the involved community of peers still through network interactions. State of the
art approaches mainly focus on consistent modification of an ontology, while
assuming that change detection is manually performed based on the designer
domain knowledge. In this respect, an additional contribution regards the fact
that H-Change provides semi-automated techniques for change detection and
assimilation, driven by H-Match matchmaking techniques.
6 Concluding remarks and future work
In this paper, we have presented the H-Change methodology and related tech-
niques for evolving open and independent ontologies in networked scenarios. In
future work, we plan to integrate the H-Change techniques within the Helios
system we have developed for ontology knowledge sharing in P2P systems [3].
A prototype tool is under development, which exploits H-Match as a match-
making engine to provide an interactive environment, to assist the designer in
evolving a peer ontology on the basis of incoming results of knowledge discovery
and sharing queries submitted to the system. Such a prototype tool will be used
for extensive experimentation on real ontologies test cases with the aim to assess
the effectiveness of the H-Change approach under different evolution scenarios.
Furthermore, ontology evolution issues are tightly connected with ontology ver-
sioning that is defined as the ability to handle changes in ontologies by creating
and managing different variants of it [15]. We are working on the approaches
proposed in the literature with the aim to adapt the existing solutions to be
implemented in the H-Change validation phase.
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