=Paper=
{{Paper
|id=Vol-225/paper-2
|storemode=property
|title=Exploiting the Structure of Background Knowledge Used in Ontology Matching
|pdfUrl=https://ceur-ws.org/Vol-225/paper2.pdf
|volume=Vol-225
|dblpUrl=https://dblp.org/rec/conf/semweb/AleksovskiKH06
}}
==Exploiting the Structure of Background Knowledge Used in Ontology Matching==
https://ceur-ws.org/Vol-225/paper2.pdf
Exploiting the structure of background knowledge used
in ontology matching
Zharko Aleksovski1,2 , Warner ten Kate1 , and Frank van Harmelen2
1
Philips Research, Eindhoven,
2
Vrije Universiteit, Amsterdam
Abstract. We investigate the use of a background knowledge ontology in ontol-
ogy matching. We conducted experiments on matching two medical ontologies
using a third extensive one as background knowledge, and compare the results
with directly matching the two ontologies. Our results indicate that using back-
ground knowledge, in particular the exploitation of its structure, has enormous
benefits on the matching. The structure of the background ontology needs closer
examination to determine how to use it in order to obtain maximal benefit.
1 Introduction
The problem of ontology alignment (also known as ontology integration, semantic in-
tegration, ontology mapping, etc.) plays a central role in the development of knowledge
based systems. New technologies such as Semantic Web make it easier to use ontolo-
gies in the information systems. These trends have driven the development of new on-
tologies, which in turn has resulted in an increasing amount of ontologies becoming
available in the recent years. Essential to an ontology is its reusability, which implies
one needs to integrate it into the system using it. Problem arises if the ontology to be
integrated uses a different vocabulary from the system using it.
This problem initiated a lot of research on ontology matching lately, see [1–3].
Various approaches have been developed. They mainly focus on two aspects: lexically
matching the elements of the ontologies, and using the structure of the ontologies. The
first uses string-based and linguistic methods to detect relatedness between elements
based on string similarity of their labels, and the second uses the relations within the
ontologies to detect similarities. Elements in the ontologies that are related but have
neither lexical nor structural similarity remain undetected. Motivated by this issue, we
focused on using background knowledge. We followed the intuition that a background
ontology which comprehensively describes the domain of the source and target ontolo-
gies will provide a way to find matches missed by other approaches.
In earlier papers, we showed that the use of background ontology can compen-
sate for lack of structure and lexical overlap, and increasing the amount of background
knowledge (multiple ontologies) improves the matching result, see [4, 5]. In this work
we investigate the benefits and problems of using a comprehensive domain ontology
as background knowledge. We conducted experiments of matching one medical ontol-
ogy to another, while using a much larger and detailed ontology of the same domain as
background knowledge. The results of our experiments confirmed that the background
knowledge can significantly boost the performance of the matching process. In particu-
lar, maximal benefit is achieved when combining different pieces of knowledge within
the background knowledge. However, these pieces need careful consideration when
combining them.
The paper is organized as follows: in Section 2 we describe the general scheme of
our approach, that is how we use an ontology as background knowledge in ontology
integration. In Sections 3 and 4 we describe a case study and a set of experiments to
test our expectations. In Section 5 we report on the results of these experiments with
evaluation on validity. In Section 6 we discuss representative matches of the different
experiments, and Sections 7 and 8 conclude the paper with discussion on related and
future work.
2 Our approach: Using a background knowledge ontology in
ontology matching
In our approach we match two ontologies using a third as background knowledge. We
call the ontologies being matched the source and target, see Figure 1. We make use
of the background knowledge by first relating the concepts from the source and target
ontology to the background knowledge, and then checking if these concepts are related.
Hence, this process proceeds in two steps: anchoring and deriving relations.
Anchoring is matching the source and target concepts to the background knowl-
edge. In general, this process can be performed by using an existing ontology matching
technique. Besides the concept’s labels one can also use the structure of the ontologies.
In the anchoring we are not only interested in finding the corresponding equivalent
concepts. As we will see in our experiments later, other kind of relatedness with the
concepts in the background knowledge can be useful as well.
Deriving relations is the process of discovering relations between source and target
concepts by looking for relations between their anchored concepts in the background
knowledge. Both the source and target concept’s anchors are part of the background
knowledge, and checking if they are related means using the reasoning service in the
background knowledge ontology. Combining the anchor relations with the relations
between the background knowledge concepts derives the relation between source and
target concepts, which is what we are looking for.
To explain this process in the context of medical ontologies, a realistic example is
the following: the source concept SRC:Brain is anchored to background knowledge
concept BK:Brain, and the target concept T AR:Head is anchored to a background
knowledge concept BK:Head. The background knowledge reveals a relation BK:Brain
part-of BK:Head, and we derive a relation that source concept SRC:Brain has a nar-
rower meaning than the target concept T AR:Head. Using background knowledge was
crucial in this case; the match was not found by directly matching the source to the
target ontology, SRC:Brain is classified under SRC:Central nervous system which is
in no way related to the concept T AR:Head.
As the example suggests, of particular interest in our approach is exploiting the
structure of the background knowledge ontology. It is done in the deriving relations
step, when checking for relatedness between the anchored concepts in the background
Background knowledge
ontology
Anchoring Anchoring
Deriving relations
Indirect match
Source Target
ontology ontology
Fig. 1. Matching source to target ontology using background knowledge.
knowledge ontology. Now we introduce the formal definitions of all the components in
this scheme, which we will use in the rest of the paper.
Concept is a class of things grouped together due to some shared property. It is
named with one or more labels which are synonymous to each other. Besides the labels,
concept is also determined by its relations to other concepts. We will refer to a concept
in two ways: with capital italic letters X, Y ... when referring to an arbitrary concept,
X O or X ON T when referring to a concept from a specific ontology, and by using its
label, for example Temporal lobe, and ON T :Temporal lobe or O:Temporal lobe when
referring to a concept from a specific ontology.
Relation is a triple (X, relation, Y ), where X and Y are concepts, and relation ∈ T,
where T is the set of all types of relations. We will also write it as X ∼ Y with ”∼”
being the symbol of the relation. Examples of relations used in this paper are: X ≡ Y ,
- the two concepts have the same meaning, and X Y (with inverse: ), also written
as X is-narrower-than Y - the first has narrower meaning than the second. Other relations
are used in the existing ontologies as well, see Section 4. Relations can be established
between concepts from the same and also from different ontologies.
Ontology is a pair of sets: ON T (C, R). C is a set of concepts, R is the set of
relations among these concepts. We will refer to an ontology using shortened form
of its name written in calligraphic letters, like ON T . When referring to C or R of a
specific ontology, we will write them as C ON T and RON T
Ontology match is a function of two ontologies that returns a set of relations be-
tween their concepts:
f : (SRC, T AR) → {(X, relation, Y )|X ∈ C SRC , relation ∈ T, Y ∈ C T AR } (1)
Specific types of ontology matches of interest to our approach are the following two:
Anchorings are two ontology matches from the source and target ontology to the back-
ground knowledge respectively, and Deriving relations is an ontology match between
the source and the target ontology which is an indirect matching that uses their anchors
to the background knowledge, and the background knowledge itself.
3 Our case study
Hypothesis: Using comprehensive domain ontology as background knowledge can
significantly boost the performance of an ontology matching process.
To test this hypothesis and investigate the problems that occur when our matching
scheme is used in practice, we conducted a set of experiments matching existing on-
tologies available on the Semantic Web. We matched the anatomy parts of CRISP and
MeSH using the FMA ontology as a background knowledge. CRISP and MeSH were
choosen randomly, and FMA because it extensively covers the anatomy domain.
Background knowledge:
FMA (75,000)
Anchoring: Anchoring:
Direct Direct
match Deriving relations: match
using Isa + Part-of
relations
Source: Indirect match Target:
CRISP MeSH
(738) (1,475)
Fig. 2. Matching CRISP to MeSH ontology using FMA as background knowledge.
The test data used in the experiments.
Source ontology: CRISP3 (Computer Retrieval of Information on Scientific Projects)
is a biomedical document classification system. It contains 738 concepts organized in a
hierarchy. The relations in its hierarchy are established based on the classified document
sets. The hierarchy contains two relations: broader-than and its inverse narrower-than,
meaning superset and subset between the corresponding document sets respectively. In
our experiments we used the part of CRISP describing anatomy.
Target ontology: MeSH4 is the National Library of Medicine’s controlled vocabu-
lary thesaurus intended for classification of documents. The part which we used in the
experiments is the anatomy sub hierarchy. It contains is 1475 concepts, and is based on
broader-than and its inverse narrower-than relations, the same as CRISP.
Background knowledge ontology: FMA, as stated in its description5 : ”The Foun-
dational Model of Anatomy is a domain ontology that represents a coherent body of
explicit declarative knowledge about human anatomy.” The version of FMA used in our
experiments dates from the end of 2005. It contains 75000 concepts interconnected with
around 160 different relation types. We used the main two hierarchies: isa and part-of.
3
http://crisp.cit.nih.gov/
4
http://www.nlm.nih.gov/mesh/
5
http://sig.biostr.washington.edu/projects/fm/AboutFM.html
Testing our hypothesis by conducting experiments.
We performed five experiments matching CRISP to MeSH. In the first we matched them
directly, and in the other four we matched them indirectly using the FMA ontology as
background knowledge. The direct matching served as a baseline, against which we
compared each of the indirect matchings. With the intention to distile the added value
of using background knowledge, we analyzed the additional matches discovered by
indirect matching. Furthermore, there were some cases of matches found in the direct
and not in the indirect matching. We discuss these in Section 6.
4 The experiments
We performed five experiments. In Experiment 1 we matched CRISP to MeSH directly
and in the other four we matched them indirectly using FMA as background knowl-
edge. In Experiment 2 we exploited each isa and part-of relation in FMA separately; in
Experiment 3 we used isa and part-of relations with their transitive closures; in Experi-
ment 4 we used isa and part-of combined, and in Experiment 5 we restricted to specific
combinations of them to induce the matches. The result of each experiment was an on-
tology match between CRISP and MeSH concepts, using one of the three relations: ≡
(equivalent), (narrower-than), (broader-than). Now, we explain the direct and then the
indirect matchings in detail.
Experiment 1: Direct matching was performed in two steps: lexical and structural.
In the lexical phase we matched CRISP to MeSH using the concept’s labels. We cleaned
the labels of interpunction, general words like the, of, and etc., we accounted for word
order and singular/plural forms of the same words. When matching two concepts X and
Y , we concluded X ≡d Y if a pair of their labels matches6 . Further, we used partial
matches as well, if X has a label consisting of a superset of words of a label of Y we
concluded X d Y , and analogously X d Y if X has label of subset of the words of
a label of Y . In other words, we used the partial lexical matches following the intuition
that additional words in a label additionally constrain the meaning of that concept. This
way, for example, we concluded that CSP: Mesenteric artery d MSH: Artery.
In the structural phase of direct matching we used the structure of CRISP and MeSH
to further induce matches by combining the relations from CRISP and MeSH and the
lexical matches. For example, from the two relations:
– CSP: Brain ≡d MSH: Brain
– MSH: Brain MSH: Temporal lobes
we can induce the relation
– CSP: Brain d MSH: Temporal lobes
We extended the set of lexical matches with the matches implied by the structure of
CRISP and MeSH. The following rules were used to extend the result set7 :
6
The small d letter in the right upper corner means that the relation is a direct match, letter a
means it is an anchor relation, and letter i means that it is an indirect match.
7
X C , X M , X F stand for an arbitrary concept from CRISP, MeSH and FMA respectively
– if (X C d Y M ) ∧ (Y M Z M ) induce (X C d Z M )
– if (X C Y C ) ∧ (Y C d Z M ) induce (X C d Z M )
– if (X C d Y M ) ∧ (Y M Z M ) induce (X C d Z M )
– if (X C Y C ) ∧ (Y C d Z M ) induce (X C d Z M )
These rules also used ≡ relations, where X ≡ Y was considered as X Y and
X Y . The rules were exhaustively applied on the result set.
Indirect matching followed the scheme that we described in Section 2. It was per-
formed in two steps: first anchoring CRISP and MeSH to FMA, and then deriving rela-
tions between CRISP and MeSH using FMA as background knowledge, Figure 2.
In the anchoring we used the same direct matching technique described for match-
ing CRISP to MeSH directly. Both CRISP and MeSH were anchored to FMA. The
result was set of matches with three different kinds of relations: X ≡a Y , X a Y ,
X a Y , where Y is a concept from FMA, and X is in CRISP or MeSH.
When deriving the relations we used the following rules:
– if (X C a Y F ) ∧ (Y F Z F ) ∧ (Z F a QM ) induce (X C i QM )
– if (X C a Y F ) ∧ (Y F Z F ) ∧ (Z F a QM ) induce (X C i QM )
where we used the relations isa and part-of for , and their inverse has-kind and has-
part for . However, in FMA there are no broader-than and narrower-than relations, but
their specializations: isa and part-of with their inverses has-kind and has-part. We con-
ducted four different experiments of indirect matching while using FMA as a back-
ground knowledge. The experiments differ in the way isa and part-of relations from
FMA were used and combined when deriving broader-than and narrower-than relations
which are then used in the two rules stated above to derive the indirect matches between
CRISP and MeSH concepts.
Experiment 2: Indirect matching by using FMA isa and part-of relations with-
out transitive closure. We induced a relation between the FMA concepts if they were
directly related with isa or part-of relation. We used the following rules:
– (X F isa Y F ) induce (X F Y F )
– (X F part-of Y F ) induce (X F Y F )
When a relation X F Y F was induced, we added its semantic equivalent Y F X F
as well. We did this in all the indirect matching experiments.
Experiment 3: Indirect matching by using FMA isa and part-of relations with
their transitive closures. Relation between two FMA concepts was induced when they
were related with the transitive closure of isa or part-of relations. We used the following
rules:
– (X1F isa X2F isa ... isa XnF ) induce (X1F XnF )
– (X1F part-of X2F part-of ... part-of XnF ) induce (X1F XnF )
Experiment 4: Indirect matching by using the transitive closure of FMA isa
and part-of relations combined. In this experiment we completely merged isa and part-
of relations and then used the transitive closure of the resulting relation. We used one
single inference rule:
– (X1F rel1 X2F rel2 ... reln−1 XnF ) where reli ∈ {isa, part-of} induce (X1F XnF )
After analyzing the results of Experiment 4 it appeared that false positive matches were
created due to using isa relation before part-of in the process of inducing matches, see
Section 6 for clarification. To overcome this negative effect we conducted the next ex-
periment.
Experiment 5: Indirect matching by using the transitive closure of FMA isa
and part-of relations without using isa before part-of . What we did in this experiment
was avoiding the use of isa relation before part-of. We used one single inference rule:
– (X1F part-of X2F ... Xk−1
F
part-of XkF isa Xk+1
F F
... Xn−1 isa XnF ) induce (X1F XnF )
5 Results and evaluation
We present now the results of the experiments. First we explain the numbers presented
in the tables, then we interpret and explain their meaning, and finally we provide eval-
uation on the results.
An important issue in presenting the matching results is that in one set of matches
many of them may be implied by the others, in combination with the structure of the
ontologies. For example, all the concepts in CRISP are found more specific than the root
concept in MeSH, whereas having equivalence between the two root concepts already
implies all those matches. Similarly, having a match between two concepts contains
implicit knowledge about their sub and super-concepts. To make a fair trade-off between
the two cases of having all the possible matches and having only the minimal set of
matches that implies all the rest, we decided on a result set that is in between.
In each matching experiment we did the following: We started from the set of
all matches, including the implied. For each source concept we took the set of all its
matches, and then minimized that set by discarding the matches which are implied by
the rest of the set. The minimal set is not sensitive to the order of discarding the implied
matches. The union of these minimized sets was the final result. This trade-off match-
ing set extracts the minimal knowledge from the matching result for each of the source
concepts separately.
5.1 Results of direct and indirect matchings
In the anchoring phase we matched CRISP and MeSH to FMA directly. The results are
shown in Figure 3. The equivalence relations were established as 1-1 matches, while
narrower-than and broader-than as many to many. Looking for equivalences only al-
ready produced successful anchoring: 65.5% of CRISP and 70.6% of MeSH concepts
were anchored to their equivalent concepts in FMA. This success comes from the rich-
ness of FMA. On the other hand, for many there were no equivalent concepts in FMA
because of disagreement on the coverage of anatomy domain. In CRISP there is a con-
cept CSP: Muscle movement which is not an anatomical part of the human body, and as
such does not exist in FMA. Still, as shown in the last column on Figure 3, nearly 99%
of the concepts from both CRISP and MeSH were anchored due to using the structure
Anchoring ≡ Anchored
concepts concepts
Anchoring CRISP to FMA 738 483 (65.5%) 607 1,474 730 (98.9%)
Anchoring MeSH to FMA 1,475 1,042 (70.6%) 1,545 2,227 1,462 (99.1%)
Fig. 3. Anchoring CRISP and MeSH to FMA
of CRISP and MeSH. For example, CSP: Muscle movement was anchored as narrower-
than FMA: Muscle because within CRISP it is narrower-than CSP: Muscle.
Figure 4 summarizes the results of the five experiments. Comparing the indirect to
the direct matching, the indirect matchings found many more narrower-than and broader-
than relations than the direct matching. It appeared that the concepts in CRISP and
MeSH can be related in many more ways which can not be found by using only the
structure of these ontologies alone. In our case FMA contributed the missing knowledge
which resulted in such an improvement over the direct matching.
Matches of CRISP to MeSH ≡ ≡ + + increase
Exper. 1: Direct 448 417 156 1,021
Exper. 2: Indir. isa and part-of 395 516 405 1,316 29%
Exper. 3: Indir. isa and part-of closure 395 933 1,402 2,730 167%
Exper. 4: Indir. isa and part-of mixed and closure 395 1,511 2,228 4,143 306%
Exper. 5: Indir. isa and part-of isa only after part-of 395 972 1,800 3,167 210%
Fig. 4. Matching CRISP to MeSH directly and indirectly
The last column in Figure 4 shows the increase of amount of matches of the indirect
matching when compared to the direct matching. The indirect matching of Experiment
2 produced 29% more matches than the direct matching. So, using only the direct isa
and part-of relations between the concepts in FMA already outperformed the direct
matching. When using the transitive closure of isa and part-of (Experiment 3) we ob-
tained increase of 167%, or nearly 2.7 times more matches than the direct matching.
When arbitrarialy mixing isa and part-of with their transitive closure we got increase of
306%, or 4 times more matches than the direct. The fifth experiment, when combining
the isa and part-of in a resticted way, there was an increase of 210% which is 3.1 times
more matches than the direct. It produced 26% less matches than the fourth, and 19%
more than the third experiment.
These numbers show that using background knowledge produces enormously more
matches than direct matching. Without combining the relations within the background
knowledge it is already better than the direct matching, then combining the relations
in the background knowledge produces much more matches, and combining different
relations within background knowledge produces the maximal number. Of course, these
numbers do not say anything about the quality of these matches. This will be discussed
in the next section. In particular, if the relations are combined arbitrarily then there is
big increase in the amount of matches but also false positive matches are created, but
when combining them in a specific way we retain the precision of the matches while
again considerably increasing the recall.
When looking at Equivalent (≡), the indirect matching found slightly less relations.
All the indirect matchings discovered the same amount of equivalences because the
only way to find equivalence indirectly is to have both concepts anchored to the same
concept in background knowledge. The equivalences found directly and not indirectly
were caused by concepts which existed in CRISP and MeSH but not in FMA. In the next
section we take a closer look at such a case. In few cases equivalences were detected
indirectly and not directly because their labels were found as synonymous only through
the background knowledge.
5.2 Evaluation of results
To test for correctness of the matches that we produced with the different experiments,
we randomly choose 30 CRISP concepts, and inspected their matches by manually
browsing the Wikipedia8 pages describing these concepts. The evaluation is presented
in Figure 5.
≡ Total Correct(%)
Exper. 1: Direct 17 18 3 38 38 (100%)
Exper. 4: Indir. isa and part-of mixed and closure 14 39 59 112 105 (93.7%)
Exper. 5: Indir. isa and part-of not isa after part-of 14 37 50 101 101 (100%)
Fig. 5. Evaluation of the matchings CRISP to MeSH directly and indirectly on 30 random CRISP
concepts
In the last column of Figure 5 is shown the correctness of the matches produced by
the different experiments. Only in Experiment 4 there were wrong matches found where
the precision dropped to 93.7%, and in the other experiments it was 100% meaning that
all the discovered matches were correct. The evaluation of Experiment 2 and 3 was
left out because they produced subsets of Experiment 5 which already produced 100%
correct matches.
Clearly, 30 concepts is not a sufficient number to get to a full evaluation. However,
all these cases were closely examined and gave us the impression that this evaluation
depicts the correctness of the experiments. The number of matches on these 30 concepts
resembles the ratio as found on the whole test set.
In lack of gold standard, the evaluation phase turned out not to be straight forward.
We had to make a choice what to consider correct and what not. Namely, some matches
are arguably correct because of the nature of the relation constitutional-part-of. For exam-
ple, the ulnar artery is constitutional part of the elbow, but it also stretches through the
whole arm, and therefore it is not part of the elbow only. We call these matches shared.
8
http://wikipedia.org/
Yet, having the relation between ulnar artery and elbow is a useful one, somebody look-
ing for medical resources about an elbow is interested in the arteries passing through
the elbow as well, see Wikipedia for more details on this example. We explored the
matches or produced in Experiment 5, and found out that 30 matches are shared,
while the other 57 are not. This means that even if inspecting the matches rigidly by
discarding shared matches, the background knowledge still produces a large gain in the
matching results.
6 Analysis of discovered matches
We selected three representative cases of matches from the result sets. We will discuss
matches found by the indirect matchings and not by the direct, then take a look at the
causes for finding incorrect matches when arbitrary mixing the isa and part-of relations
in the background knowledge, and finally we discuss matches found by the direct but
not by the indirect matching.
FMA: Head Using isa and then part-of relation
produced false matches in Experiment 4
part of
FMA: Brain
FMA: Bronchus
part of
FMA: Forebrain is a
FMA: Right main bronchus
FMA: Telencephalon part of
part of
part of
FMA: Cerebral hemisphere FMA: Loose connective tissue
FMA: Temporal lobe part of FMA: Dental pulp is a
Anchor: Anchor: Anchor: Anchor:
equivalent equivalent equivalent equivalent
Indirect match: Incorrect indirect match:
narrower-than narrower-than
CSP: Temporal lobe MSH: Head CSP: Dental pulp MSH: Bronchus
Fig. 6. Two indirect matches: on the left the correct match Temporal lobe i Head, and on the
right incorrect match Dental pulp i Bronchus.
Case 1: Matches found by indirect and not by the direct matching. CSP: Tem-
poral lobe i MSH: Head is a representative case of these matches, and is shown on
the left hand side in Figure 6. Temporal lobes are parts of the brain, and consequently
parts of the human’s head. In the structure of MeSH and CRISP they are classified under
the Brain which is classified under Central Nervous System, and are not connected in
any way with the Head. Therefore relation to the Head is impossible to establish using
direct matching, and the background knowledge is crucial in discovering the match.
Case 2: Incorrect match produced by arbitrary mixing of isa and part-of hier-
archies. When using isa and then part-of in the inference, some of the matches were
incorrect. An example is shown on the right hand side in Figure 6 finding CSP:Dental
pulp i MSH:Bronchus. Each of the two relations Dental pulp isa Loose connective
tissue part-of Right main bronchus remain correct when generalizing to Dental pulp
Loose connective tissue, and Loose connective tissue Right main bronchus, but their
transitive closure does not hold any more: Dental pulp Right main bronchus is incor-
rect, which then implies the incorrect match CSP:Dental pulp i MSH:Bronchus is
found.
Case 3: Matches found by direct but not by indirect matching. An example
of such a match is CSP:Mesenteric artery ≡d MSH:Mesenteric Arteries. The re-
lation was not found indirectly because a concept Mesenteric artery does not exist
in FMA, instead there are two more specific FMA:Inferior Mesenteric Artery and
FMA:Superior Mesenteric Artery and one broader concept FMA:Artery. Using FMA
the following indirect relations were discovered for CSP:Mesenteric artery:
– CSP:Mesenteric artery i MSH:Arteries
– CSP:Mesenteric artery i MSH:Mesenteric Artery, Inferior
– CSP:Mesenteric artery i MSH:Mesenteric Artery, Superior
Combining the results of the direct and indirect matching will improve the result of the
both. We showed that in an earlier study in [4].
7 Related work
The research topic of semantic integration is a very active one, yet we encountered two
major difficulties when comparing our approach with the others. First is the objective
in matching: some target at finding pairs of most corresponding concepts, others aim at
1-1 mappings only, etc.; and second is the different way we use background knowledge.
Existing approaches mostly use background knowledge in the form of lexicons for
discovering synonyms, see [6, 2, 7]. S-Match, [8], is example where background knowl-
edge is exploited in doing the mapping, which is very similar to the approach we used
in this work. In the current state S-Match uses a predefined set of background knowl-
edge sources, such as Wordnet and UMLS. Moreover, it uses the class hierarchy of the
background knowledge ontologies.
Related case-study was performed in [9], where the authors investigated enriching
user search queries for image retrieval, by using relations from Wordnet. This follows
closely our own scheme: the user search query is a source ontology of one single con-
cept with one single label, Wordnet is the background knowledge, and the classification
of images is the target ontology. They used isa and part-of relations, and they arrived to
conclusion comparable with ours about using and combining these relations.
The work we present here is a sequel of another case study that followed similar
scheme of using background knowledge in ontology integration, see [4]. In contrast to
this work, in the previous studies we used source and target ontologies without struc-
ture, the relations we were chasing for were semantic closeness rather than relations
with exact semantics such as broader-than and narrower-than. The background knowl-
edge contained only broader-than and narrower-than relations, which prohibited us in
investigating the impact of combining different relations.
8 Conclusions
Based on the results produced in the experiments, we draw the following conclusions
from our study:
(i) Using comprehensive background knowledge in form of ontology can boost the
ontology matching process as compared to a direct matching of the two ontologies.
(ii) Most of the value in using background knowledge comes from combining dif-
ferent pieces of knowledge within the background knowledge.
(iii) Different pieces of knowledge within the background knowledge need careful
combination in order to gain maximal benefit.
A crucial requirement in using background knowledge is the existence of extensive
reference ontologies in different domains at hand. Therefore, the development of such
ontologies and subsequent publication on the Semantic Web will make the problem of
integration easier.
Currently we are expanding the reported experiments further, we are looking for ap-
proximation schemes when deriving the relations within FMA, and we are investigating
the usefulness of other relations in FMA like: X is-attached-to Y , X sends-output-to Y ,
etc. These relations will produce more matches different from those we present here.
Our findings were concluded from experiments conducted on medical test data.
Therefore, we are conducting similar experiments in music domain on matching styles
and genres from different music providers. In contrast to the medical domain, the
knowledge in music is much weakly structured as different music content providers
largely disagree on the meaning of music terms. In this direction, we took the effort to
extract relatively extensive music ontology from Wikipedia which will serve as back-
ground knowledge in our experiments.
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