=Paper=
{{Paper
|id=None
|storemode=property
|title=How do Computed Ontology Mappings Evolve? - A Case Study for Life Science Ontologies
|pdfUrl=https://ceur-ws.org/Vol-890/paper1.pdf
|volume=Vol-890
|dblpUrl=https://dblp.org/rec/conf/semweb/GrossHTR12
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==How do Computed Ontology Mappings Evolve? - A Case Study for Life Science Ontologies==
https://ceur-ws.org/Vol-890/paper1.pdf
How do computed ontology mappings evolve?
- A case study for life science ontologies
Anika Gross1,2 , Michael Hartung1,2 , Andreas Thor1 , and Erhard Rahm1,2
1
Department of Computer Science, University of Leipzig
2
Interdisciplinary Center for Bioinformatics, University of Leipzig
{gross,hartung,thor,rahm}@informatik.uni-leipzig.de
Abstract. Mappings between related ontologies are increasingly used
to support data integration and analysis tasks. Changes in the ontolo-
gies also require the adaptation of ontology mappings. So far the evolu-
tion of ontology mappings has received little attention albeit ontologies
change continuously especially in the life sciences. We therefore analyze
how mappings between popular life science ontologies evolve for different
match algorithms. We also evaluate which semantic ontology changes pri-
marily affect the mappings. Our results can be valuable for users working
with ontology mappings, e.g., one can learn from past ontology/mapping
changes and their correlation to estimate possible mapping changes if
new ontology versions become available.
Keywords: mapping evolution, ontology matching, ontology evolution
1 Introduction
Ontologies are heavily used, e.g., to uniformly annotate and categorize objects.
Different ontologies of the same domain often contain overlapping and related in-
formation. For instance, information about mammalian anatomy can be found in
NCI Thesaurus [20] and Adult Mouse Anatomy [1]. Ontology mappings (ontol-
ogy alignments) are used to express the semantic relationships between different
but related ontologies, e.g., by linking equivalent concepts of two ontologies.
Mappings between related ontologies are useful in many ways, in particu-
lar for data integration and enhanced analysis. For instance, such mappings are
needed to merge ontologies to create an integrated ontology such as the cross-
species Uber anatomy ontology [30]. Furthermore, mappings can help finding
objects with similar ontological properties as interesting targets for a compar-
ative analysis. Ontology curators can further find missing ontology annotations
and get recommendations for possible ontology enhancements based on map-
pings to other ontologies.
Ontologies underly continuous modifications so that new ontology versions
are released periodically [14]. New versions typically incorporate enhanced knowl-
edge, such as additional concepts, relationships, and attribute values. Existing
information can also be revised or even deleted. Such ontology changes can inval-
idate previously determined ontology mappings [7] so that they may have to be
re-determined to remain useful. Unfortunately, determining ontology mappings
is an expensive process even with the help of semi-automatic ontology match-
ing techniques [8,25] that still involve a manual verification of correspondences
and a parametrization effort. The importance on determining and adapting on-
tology mappings is underlined by the popular Ontology Alignment Evaluation
Initiative (OAEI) [23]. OAEI provides real-world test data sets, in particular for
matching the Adult Mouse Anatomy Ontology against the anatomy part of NCI
Thesaurus. Unfortunately, the reference mapping of the anatomy task is based
on 5 year old ontology versions1 so that its quality for the current ontology
versions remains unclear.
The evolution of ontology mappings has received very little attention so far,
especially for the life science domain. For example it is unknown to what de-
gree and how mappings between popular life science ontologies change and how
ontology changes affect ontology mappings. There are many ways to compute
mappings and it is not clear to what degree different match methods result in
differently stable ontology mappings. Such information is expected to be useful
for deciding about whether a previous ontology mapping is still reliable and up-
to-date or whether one has to perform an expensive adaptation of the mapping.
To address these questions and issues we make the following contributions:
– We introduce a general versioning scheme which allows for studying the
evolution of both, ontologies and mappings. (Sec. 2)
– We propose a generic change model to measure and compare the degree of
change for ontologies and mappings. The model supports analyzing the im-
pact of ontology evolution on mapping evolution, e.g., what ontology changes
lead to the addition or deletion of correspondences in the mapping. (Sec. 3)
– We apply our model to three life science scenarios and evaluate how mappings
between popular life science ontologies evolve. We also investigate mapping
evolution for different match techniques. (Sec. 4)
In Sec. 5 we describe related work and conclude in Sec. 6.
2 Versioning Scheme for Ontologies and Mappings
2.1 Prelimiaries
In general an ontology O = (C, R, A) consists of concepts C which are interre-
lated by directed relationships R. Each concept has an unambiguous identifier
such as an accession number. A concept typically has further attributes a ∈ A to
describe the concept, e.g., name, synonyms, or definition. A relationship r ∈ R
forms a directed connection between two concepts and has a specific type, e.g.,
is a or part of. An ontology mapping (ontology alignment) MO1,O2 is a set
of correspondences (c1, c2) whereby each correspondence interconnects two con-
cepts c1 ∈ O1 and c2 ∈ O2 of the two ontologies. The mapping semantics
depends on the intended use case but we assume that all correspondences of a
mapping express the same semantic type, e.g., same-as or is-related-to.
1
As of 2012, the current reference ontology mapping has been created in 2007.
2
MO11,O21 = M1
O11 O21
diff(O11,O12) mdiff(M1,M2) diff(O21,O22)
MO12,O22 = M2
O12 O22
... ... ...
MO1k-1,O2k-1 = Mk-1
O1k-1 O2k-1
diff(O1k-1,O1k) mdiff(Mk-1,Mk) diff(O2k-1,O2k)
MO1k,O2k = Mk
O1k O2k Current version k
Fig. 1. General versioning scheme with multiple ontology and mapping versions
Since a purely manual creation of ontology mappings is a tedious and labor-
intensive task such mappings are usually determined by semi-automatic ontol-
ogy matching techniques (see Sec. 5 for Related Work). Most matching ap-
proaches are metadata-based, i.e., they use the ontology representations them-
selves to find related concepts, in particular the names of concepts and con-
textual information like the names of the parent or child concepts within the
ontologies. In our evaluation, we will analyze mapping changes for three typical
metadata-based matchers (Sec. 4).
2.2 Versioning Scheme
We define an ontology version Ov = (Cv , Rv , Av ) as a snapshot of an ontology
O released at a specific point in time. For simplicity we enumerate the versions
with ascending numbers v = 1, 2, . . . rather than using the actual release dates.
Ontology changes affect previously determined ontology mappings so that
these mappings should be continuously adapted. Fig. 1 illustrates the general
versioning scheme we adopt in this paper. There is a series of versions (v =
1 . . . k) for a pair of ontologies O1 and O2 that are connected by an ontology
mapping MO1,O2 . For simplicity we determine ontology mappings only between
ontologies of the same version number, i.e., we create mappings Mv only between
ontology versions O1v and O2v referring to the same specific point in time.
The difference between two ontology and mapping versions is denoted by
dif f (Ov , Ov+1 ) and mdif f (Mv , Mv+1 ), respectively. The next section explains
dif f and mdif f in more detail.
3 Change Model for Ontologies and Mappings
We first describe our change model for ontologies and mappings and categorize
the changes into different groups. We also propose simple change ratio indica-
tors to assess the evolution intensity between successive ontology and mapping
3
Change operation Type
Insertion of a new concept to Ov+1
Insertion of a subgraph to a concept
Insertion of new relationship in Ov+1 Information extension
Addition of an attribute (to an existing concept)
Mark concept as non-obsolete
Deletion of a concept in Ov
Removal of a subgraph
Deletion of an relationship in Ov Information reduction
Deletion of an existing attribute
Mark concept as obsolete
Split concept of Ov into multiple concepts in Ov+1
Merge concepts of Ov into a single concept in Ov+1
Concept substitution Information revision
Move concept
Change attribute value
Table 1. COntoDiff change operations (including their categorization in three groups)
for ontology evolution Ov 7→ Ov+1 .
versions. We then propose indicators to assess the impact of ontology changes
on ontology mappings.
3.1 Ontology Changes
We start by defining what changes can occur between successive ontology ver-
sions Ov and Ov+1 . Our model is based on the COntoDiff algorithm described
in [13]. COntoDiff computes the difference dif f (Ov , Ov+1 ) between an old and
a new version of an ontology and consists of the set of change operations that
– when applied to Ov – transform the old into the new version. Basic change
operations are concept and attribute additions or deletions. COntoDiff also de-
termines more complex changes such as merging or splitting of concepts or the
addition/deletion of subgraphs.
Table 1 lists all considered change operations and additionally categorizes
them into one of three groups. The first group contains information extending
operations that add information in Ov such as new concepts, relationships or
attribute values. The second group, information reduction, includes change op-
erations that remove information from Ov . All other operations including split
and merge changes belong to the revise group.
For a quantitative change analysis we assign concepts both from Ov and Ov+1
based on their change operations to one of the following sets:
– Extension set: Ext(Ov7→v+1 ) = set of concepts in Ov ∪ Ov+1 where all
concept-related change operations are information extending.
4
Fig. 2. left: Example evolution of two ontologies and a mapping. Concepts b1 and e2
have been revised, d2 ∈ O2 has been removed, and g1 , f1 , and f2 have been added
during the evolution from version v = 1 7→ 2. The mapping change between O1 and
O2 comprises two new correspondences ((b1 , b2 ), (f1 , f2 )) and two removed correspon-
dences ((b1 , c1 ), (d1 , d2 )). right: Impact matrix of ontology and mapping changes.
– Reduction set: Red(Ov7→v+1 ) = set of concepts in Ov ∪ Ov+1 where all
concept-related change operations are information reducing.
– Revision set: Rev(Ov7→v+1 ) = set of concepts in Ov ∪Ov+1 that are involved
in at least one change operation but belong neither to Ext nor to Red. Each
concept is thus related to a revise operation or is related to both extending
and reducing operations.
All other concepts remain unchanged, i.e., they are not affected by any
change operation. Fig. 2 illustrates an evolution example for two ontologies
O1 and O2. For example, the evolution from O21 to O22 might contain three
change operations: insertion of concept f2 , deletion of concept d2 , and an at-
tribute value change for concept e2 . The three concepts are thus assigned to
Ext, Red, and Rev, respectively, i.e., Ext(O217→2 ) = {f2 }, Red(O217→2 ) = {d2 },
and Rev(O217→2 ) = {e2 }. All other concepts of Fig. 2 are not affected by the
change operations.
The size of the three concept sets Ext, Red, and Rev quantitatively charac-
terizes the degree of change during the evolution from Ov to Ov+1 . We therefore
define the ontology change ratio as follows:
|Ext(Ov7→v+1 ) ∪ Red(Ov7→v+1 ) ∪ Rev(Ov7→v+1 )|
OCR(Ov7→v+1 ) =
|Ov ∪ Ov+1 |
The ontology change ratio for O2 of our running example (Fig. 2) is thus
OCR(O217→2 ) = |{f2 , d2 , e2 }|/|{a2 , b2 , c2 , d2 , e2 , f2 }| = 0.5.
3.2 Mapping Changes
For ontology mapping evolution we employ a simple model that distinguishes
between the addition and deletion of correspondences. Thus, between two con-
5
secutive mapping versions Mv and Mv+1 we consider whether a new correspon-
dence has been added (Add) or a previous one has been removed (Del). We
group changed correspondences into the following sets:
– Addition set: Add(Mv7→v+1 ) = Mv+1 \Mv
– Deletion set: Del(Mv7→v+1 ) = Mv \Mv+1
All other correspondences appear in both mapping versions and are thus
unchanged. Based on the introduced sets we define the mapping change ratio
as follows:
|Add(Mv7→v+1 ) ∪ Del(Mv7→v+1 )|
M CR(Mv7→v+1 ) =
|Mv ∪ Mv+1 |
In the example of Fig. 2 there are two new correspondences, i.e., Add(M17→2 ) =
{(b1 , b2 ), (f1 , f2 )}. and two deleted correspondences, (b1 , c2 ) and (d1 , d2 ). Since
there is one unchanged correspondence (a1 , a2 ), the mapping change ratio
M CR(M17→2 ) equals 4/5.
3.3 Impact of Ontology on Mapping Changes
To determine how ontology changes influence or trigger mapping changes it is
useful to interrelate the different kinds of ontology changes and mapping changes.
For this purpose, we interrelate the three sets of changed concepts (Ext, Red,
Rev) with the two sets of changed correspondences (Add, Del). We will define
six corresponding indicators and use them for both analyzing mapping evolution
(see Sec. 4).
The impact ratio is the share of changed concepts that actually had an
impact on the correspondences. For any set of ontology changes OCh (Ext, Red,
or Rev) and mapping changes MCh (Add or Del) it is defined as follows:
|{c ∈ OCh |∃c0 : (c, c0 ) ∈ MCh ∨ (c0 , c) ∈ MCh }|
IR(OCh , MCh ) =
|OCh |
For example, to determine which fraction of additive ontology changes led to
new correspondences we determine the impact ratio for OCh = Ext(O117→2 ) ∪
Ext(O217→2 ) and MCh = Add(M17→2 ). For the example in Fig. 2, two (f1 and
f2 ) out of the three Ext-concepts appear in the set of added correspondences,
i.e., the changes in these two concepts had an impact on the mapping. Therefore
IR(Ext, Add) equals 32 .
One would expect that Ext concepts mostly lead to correspondence additions
whereas Red concepts usually account for correspondence deletions. However,
as we will see in our evaluation (see Sec. 4), Ext concepts may also trigger
correspondence deletions and Red concepts may lead to new correspondences
depending on the match technique.
6
4 Analysis of Mapping Evolution
After introducing the experimental setup, we analyze ontology and mapping
evolution for different life science scenarios. We then compare mapping evolution
for different match strategies and evaluate the impact of ontology changes on
mapping changes.
4.1 Setup
We consider three mapping scenarios:
– Anatomy: map Adult Mouse Anatomy Ontology (MA) to the anatomy part
of NCI Thesaurus (NCITa)
– Molecular Biology: map the two Gene Ontology[10] sub-ontologies Molecular
Functions (MF) and Biological Processes (BP)
– Chemistry: map Chemical Entities of Biological Interest (ChEBI) [5] to NCI
Thesaurus (NCIT)
For each input ontology we map 10 versions on a half year basis between 2006-06
and 2010-12 with each other. We use the following meta-data based matchers to
compute the confidence (similarity) for any concept pair of two ontologies:
– Name: String (trigram) similarity of concept names
– NameSyn: Maximal string (trigram) similarity of names and synonyms
– Context: String (trigram) similarity of the concatenated parent, concept, and
children names
In this study we focus on the evolution of ontology mappings and do not
evaluate the quality of matching. The choice of match strategies is based on
previous studies where matching on concept names and synonyms achieved high
quality especially for anatomy ontologies [11,12]. To obtain precise results we
need to select the most likely correspondences exceeding a certain confidence
threshold. We applied a default confidence threshold of 0.6 ; for the NameSyn
matcher, we also considered a stricter threshold of 0.8. Moreover, for each input
ontology concept, we only select the top correspondences in a small delta range
(MaxDelta selection [6]).
4.2 Ontology and Mapping Evolution
Fig. 3 gives an overview about the ontology and mapping sizes as well as their
growth between June 2006 and Dec. 2010. For Anatomy, the combined size of
concepts in domain and range ontology (|C|) grew only slightly by a factor 1.1
to almost 10,000 concepts. By contrast, |C| increased by 60 - 70 % to 30,000
and 120,000 concepts for Molecular Biology and Chemistry. In two of the three
scenarios (Anatomy and Molecular Biology), the mappings grow similarly strong
as the ontologies while the Chemistry mappings grew by up to a factor 6. The
especially high mapping growth for the Context matcher seems influenced by its
7
ontologies Name 0.6 NameSyn 0.6 NameSyn 0.8 Context 0.6
|C2006-06| growth |M2006-06| growth |M2006-06| growth |M2006-06| growth |M2006-06| growth
Anatomy 8,806 1.1 1,496 1.1 1,636 1.1 1,264 1.1 1,272 1.0
Molecular Biology 18,974 1.6 852 1.1 1,531 1.7 251 1.6 465 1.6
Chemistry 69,005 1.7 1,353 3.9 3,242 3.2 1,930 3.7 277 6.1
Fig. 3. Ontology and mapping growth factors. Number of concepts (|C2006−06 |) and
number of mapping correspondences (|M2006−06 |) in the first considered version. |C| is
the sum of domain and range ontology size for each match problem. Growth factors
compare the first (2006-06) and last (2010-12) considered version.
very small mapping size which in turn is caused by its need to find similar names
not only for the concepts but also for their parent and child concepts. Compar-
ing the results for NameSyn with two different thresholds, we find that a higher
threshold produces smaller mappings and achieves only a relatively small cover-
age, especially for Molecular Biology. For Molecular Biology, the Name matcher
proved to determine the most stable mappings.
Fig. 4(a) shows ontology change factors (see Sec.3.3) between succeeding ver-
sions for the three domains during the 5-year observation period. For Anatomy
there were only few changes compared to the other two domains. Molecular Bi-
ology shows high change rates until 2007 (nearly 40%). From 2008 on, change
rates are comparable to those of Chemistry (around 20%). Fig. 4(b) illustrates
more detailed mapping evolution results for NameSyn 0.6 in Molecular Biology.
In general, correspondence additions dominate leading to a final mapping size of
more than 2,500 correspondences. But there has also been a considerable num-
ber of deletions. In 2007-12 nearly 500 correspondences were removed from the
mapping. This shows that there can be very heavy mapping changes.
4.3 Comparison of Match Strategies
To analyze the mapping stability for different match strategies in more detail,
we examine a possible correlation between ontology and mapping changes over
time. We therefore compute ontology and mapping change factors for all three
1.0 addCorr
Add(Mv→v+1) delCorr
Del(Mv→v+1) |Corr|
|Mv+1|
Anatomy
Molecular Biology 1,000 3,000
0.8
mapping size
Chemistry 800 2,500
|changes|
0.6
OCR
2,000
600
0.4 1,500
400
1,000
0.2 200 500
0.0 0 0
(a) (b)
Fig. 4. (a) Ontology change factors. (b) Mapping evolution for NameSyn 0.6 matcher
in Molecular Biology example.
8
OCR MCR(Name 0.6) MCR(NameSyn 0.6) MCR(NameSyn 0.8) MCR(Context 0.6)
0.10 0.10 0.5 1.0
(a) 0.08
0.06
0.04 (b) (c)
0.08 0.02
0.00 0.4 0.8
0.06 0.3 0.6
0.04 0.2 0.4
0.02 0.1 0.2
0.00 0.0 0.0
addCorr
Add(M ) delCorr
Del(M ) |Corr|
|M |
Fig. 5. Ontology and mapping change factors for three life science domain examples
(a) Anatomy, (b) Molecular Biology, (c) Chemistry
match scenarios and the four match strategies (Fig. 5 a-c). For Anatomy, on-
tologies and mappings only slightly changed (see y-axis range), while the other
two scenarios experience a surprisingly high degree of mapping changes between
10 and 80 %. Except for Chemistry we observe a strong correlation between the
ontology change factor (black continuous line) and the mapping change factors
of the different match strategies(colored dashed lines). The Name matcher was
relatively stable in general while the Context matcher was most heavily influ-
enced by ontology evolution. This especially holds for Chemistry where 80% of
the Context mappings changed in 2008. The reason for the relative instability
of Context is mainly in its use of more ontological information that can change,
i.e., changes on both parent and child concepts have an influence. For instance,
moving a concept from one parent concept to another might completely change
a concept’s context. For Molecular Biology the mappings, (especially NameSyn),
changed heavily in 2007-12, although the maximum ontology evolution already
occurred in 2007-06. This results from successive modification of GO-BP and
GO-MF in 2007. The combined changes in both sub-ontologies seem to have led
to numerous mapping changes in 2007-12.
4.4 Impact of Ontology on Mapping Changes
Fig. 6 illustrates the real impact of ontology changes (Ext, Red, Rev ) on mapping
changes (Add, Del ). We exemplarily show results for NameSyn 0.6 and computed
the average over all versions. The table shows the number of changed concepts
as well as the ratio having impact on mapping changes (IR). First, we can ob-
serve that a high number of ontology extensions, reductions and revisions has no
impact on the ontology mappings (>80%). This is due to a limited match cover-
age since changed ontology parts that are not covered by the ontology mapping
do not result in mapping changes. Second, extending ontology changes (Ext)
primarily cause correspondence additions and no or only few correspondence
deletions for all three scenarios. Third, Red concepts are primarily involved in
correspondence deletions but also in some additions. The latter might result from
specific matcher characteristics. Imagine a concept loses a synonym and also the
correspondence based on this synonym. This can enable a new correspondence
9
IR Ext IR Red IR Rev
|Ext| |Red | |Rev |
→Add →Del →Add →Del →Add →Del
Anatomy 95 18.7% 0.1% 7 0.0% 7.8% 89 6.8% 4.1%
Molecular Biology 2,359 4.6% 0.7% 223 2.4% 8.8% 2,209 3.5% 2.1%
Chemistry 8,377 11.7% 1.2% 366 3.5% 5.3% 6,441 8.1% 4.0%
Fig. 6. Impact of ontology concept changes (Ext, Red, Rev) on mapping changes
(Add, Del) for NameSyn 0.6. Average values for absolute change number (|Ext|, |Red|,
|Rev|) and impact association ratios (IR(OCh , MCh ) displayed as percentage) over all
considered versions
by relating the concept to another one than before. Thus, a synonym deletion
can lead to a correspondence deletion and addition in one evolution step. Fi-
nally, revised concepts (Rev ) trigger both, Add and Del. This is intuitive since
revised concepts might have been extended and reduced in one evolution step
(e.g., attribute addition and deletion). In general, ontology revisions account for
a high share of mapping changes while deletions play only a minor role.
4.5 Summary
We evaluated ontology and mapping evolution for three real-world life science
domains (Anatomy, Molecular Biology and Chemistry) and took four match-
strategies into account. The analysis results show that especially Molecular Bi-
ology and Chemistry underlie heavy ontology extensions and revisions whereas
Anatomy is relatively stable. Since existing knowledge is mainly extended or
revised, we find only few ontology reducing changes for all domains. Ontology
evolution heavily influenced mappings computed by different metadata-based
match strategies. Especially, the structural matcher Context produced rather
unstable results whereas mappings based on the Name matcher are relatively
stable. As expected, ontology extensions primarily lead to correspondence addi-
tions and information reducing ontology changes primarily lead to the removal
of correspondences. Ontology revisions play an important role and result in both
the addition and deletion of correspondences.
5 Related Work
In the last decade, ontology matching to semi-automatically create ontology
mappings has become an active research field (see [8,24] for overviews). In
the life sciences especially the matching of anatomy ontologies [31] and molec-
ular biological ontologies [2] has attracted considerable interest. Most match
approaches focus on improving the quality of computed mappings by applying
different matchers (e.g., based on the name/synonyms of concepts, the ontol-
ogy structure or associated instances) in a workflow-like manner. For comparing
available match systems w.r.t. their quality the OAEI [23] provides gold standard
mappings, e.g., between MA and NCIT.
10
Previous work on ontology evolution (see [9,15] for surveys) focused on on-
tology versioning [18], the evolution process itself [26] as well as the detection of
changes between ontology versions [21]. Few approaches investigate how changes
in ontologies should be propagated to dependent artifacts such as instances or
annotations. For example, the ontology evolution process proposed in [27] in-
cludes a change propagation phase where performed changes are propagated to
other ontologies that are based on the modified ontology.
The evolution of ontology mappings has received only little attention so far.
In our previous work [14] we studied the evolution of mappings without consid-
ering interdependencies to ontology evolution. In a further study [29] we focused
on the stability of correspondences created by an instance-based matcher and
proposed measures which allow for a classification of (un)stable correspondences.
[7] discusses possible techniques to maintain mappings in an automatic way.
In contrast to previous work this study focuses on the impact of ontology
on mapping changes, i.e., we investigate (1) how ontology mappings change and
(2) study how ontology changes correlate with mapping changes for different
matchers. In our evaluation we use real-world data sets from several life science
domains. The mapping versions under investigation were created with previ-
ously evaluated matchers such as name or name/synonym using the GOMMA
system [17].
6 Conclusion and Future Work
We studied the evolution of ontology mappings and analyzed the ontology changes
triggering mapping changes as well as the influence of different match techniques.
Our analysis covered three life science mappings and three match strategies.
Except for anatomy ontologies, we observed that ontology mappings based on
common match strategies using name and synonym information often experience
heavy changes. The results indicate a significant correlation between ontology
and mapping changes depending on the utilized match strategy and mapping
coverage.
In future work, we plan to investigate how known ontology changes can be
used to semi-automatically adapt ontology mappings without a completely new
mapping determination.
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