=Paper=
{{Paper
|id=Vol-431/paper-19
|storemode=property
|title=TaxoMap in the OAEI 2008 alignment contest
|pdfUrl=https://ceur-ws.org/Vol-431/oaei08_paper12.pdf
|volume=Vol-431
|dblpUrl=https://dblp.org/rec/conf/semweb/HamdiZSR08
}}
==TaxoMap in the OAEI 2008 alignment contest==
https://ceur-ws.org/Vol-431/oaei08_paper12.pdf
TaxoMap in the OAEI 2008 alignment contest
Fayçal Hamdi1 , Haı̈fa Zargayouna2 , Brigitte Safar1 , and Chantal Reynaud1
1
LRI, Universit Paris-Sud, Bt. G, INRIA Futurs
2-4 rue Jacques Monod, F-91893 Orsay, France
firstname.lastname@lri.fr
2
LIPN, Université Paris 13 - CNRS UMR 7030,
99 av. J.B. Clément, 93440 Villetaneuse, France.
haifa.zargayouna@lipn.univ-paris13.fr
Abstract. TaxoMap is an alignment tool which aim is to discover rich corre-
spondences between concepts. It performs an oriented alignment (from a source
to a target ontology) and takes into account labels and sub-class descriptions.
Our participation in last year edition of the competition have put the emphasis on
certain limits. TaxoMap 2 is a new implementation of TaxoMap that reduces sig-
nificantly runtime and enables parameterization by specifying the ontology lan-
guage and different thresholds used to extract different mapping relations. The
new implementation stresses on terminological techniques, it takes into account
synonymy, and multi-label description of concepts. Special effort was made to
handle large-scale ontologies by partitioning input ontologies into modules to
align. We conclude the paper by pointing out the necessary improvements that
need to be made.
1 Introduction
TaxoMap was designed to retrieve useful alignments for information integration be-
tween different sources. The alignment process is then oriented from ontologies that
describe external ressources (named source ontology) to the ontology (named target on-
tology) of a web portal. The target ontology is supposed to be well-structured whereas
source ontology can be a flat list of concepts.
TaxoMap makes the assumption that most semantic resources are based essentially on
classification structures. This assumption is confirmed by large scale ontologies which
contain rich lexical information and hierarchical specification without describing spe-
cific properties or instances.
To find mappings in this context, we can only use the following available elements:
labels of concepts and hierarchical structures.
Previous participation of TaxoMap in the alignment contest [2], despite positive
outcome, have put the emphasis on certain limits:
– Multi-label concepts: previous version of TaxoMap assumed that a concept has only
one label. This leads to loose interesting relations between multi-label concepts.
– Large ontologies: TaxoMap were unable to run on real ontologies, such as Agrovoc3 .
3
http://www4.fao.org/agrovoc/
TaxoMap 2 is a new implementation of TaxoMap which aims to overcome these
limits and provides modular code (easily extensible). It introduces a morphosyntactic
analysis and new heuristics. Moreover, we propose new methods to partition large on-
tologies into modules which TaxoMap can handles easily.
We take part to four tests. Results on benchmarks are almost the same as last year
as the philosophy behind TaxoMap reminds the same (oriented alignment, between
concepts only). We perform better -in terms of number of mappings generated and
runtime- on Anatomy. Library test allows us to perform a new algorithm for ontology
partitioning and to experiments our system with a new language (Dutch). Directory test
enables to test our alignment tool in real world taxonomy integration scenario.
2 Presentation of the System
2.1 State, Purpose and General Statement
We consider an ontology as a pair (C, HC ) consisting of a set of concepts C arranged in
a subsumption hierarchy HC . A concept c is defined by two elements: a set of labels and
subclass relationships. The labels are terms that describe entities in natural language
and which can be an expression composed of several words. A subclass relationship
establishes links with other concepts.
Our alignment process is oriented; from a source (OSource ) to a target (OT arget )
ontology. It aims at finding one-to-many mappings between single concepts and es-
tablishing three types of relationships, equivalence, subclass and semantically related
relationships defined as follows.
Equivalence relationships An equivalence relationship, isEq, is a link between a con-
cept in OSource and a concept in OT arget with labels assumed to be similar.
Subclass relationships Subclass relationships are usual isA class links. When a concept
cS of OSource is linked to a concept cT of OT arget with such a relationship, cT is
considered as a super concept of cS .
Semantically related relationships A semantically related relationship, isClose, is a
link between concepts that are considered as related but without a specific typing of the
relation.
2.2 Techniques Used
TaxoMap 2 improves terminology alignment techniques. The use of TreeTagger [3], a
tool for tagging text with part-of-speech and lemma information, enables to take into
account the language, lemma and an use word categories in an efficient way. TaxoMap
performs a linguistic similarity measure between labels of concepts. The measure takes
into consideration categories of words which compose a label. The words are classi-
fied as functional (verbs, adverbs or adjectives) and stop words (articles, pronouns).
Stop words category enables to ignore these words in similarity computation. Func-
tional words has less power than all the other (noun, etc.). The position of a word in
the label is also of importance, a common word between two labels is less important
after a preposition than a word that is a head. TreeTagger, however, is error-prone, due
essentially to short labels.
The main methods used to extract mappings between a concept cs in OSource and a
concept ct in OT arget are:
– Label equivalence: An equivalence relationship, isEq, is generated if the similarity
between one label of cs and one label of ct is greater than a threshold (Equiv.threshold).
– Label inclusion (and its inverse) and hidden inclusion: Then, we consider inclusion
of label words: let ct be the concept in OT arget with the highest similarity mea-
sure with cs . If one of the labels of ct is included in one of the labels of cs , we
propose a subclass relationship < cs isA ct >. Inversely, if one of the labels of
cs is included in one of the labels of ct , we propose a semantically related rela-
tionships < cs isGeneral ct >. If ct is not the concept with the highest similarity
measure, its measure must be greater than a threshold (HiddenInc.thresholdSim)
and the highest similarity measure must be greater than another threshold (Hidden-
Inc.thresholdMax). The intuition behind this strategy is to extract hidden inclusion.
– Reasoning on similarity values : Let ctM ax and ct2 be the two concepts in OT arget
with the highest similarity measure with cs , the relative similarity is the ratio of ct2
similarity on ctM ax similarity. If the relative similarity is lower than a threshold
(isA.threshold), one of the three following techniques can be used:
• the relationship < cs isClose ctM ax > is generated if the similarity of ctM ax is
greater than a threshold (isCloseBefore.thresholdMax) and if one of the labels
of cs is included in one of the labels of ctM ax .
• the relationship < cs isClose ctM ax > is generated if the similarity of ctM ax
is greater than a threshold (isClose.thresholdMax).
• an isA relationship is generated between cs and the father of ctM ax if the
similarity of ctM ax is greater than a second threshold (isA.thresholdMax).
– Reasoning on structure: an isA relationship is generated if the three concepts in
OT arget with the highest similarity measure with cs have similarity greater than a
threshold (Struct.threshold), and has a common father.
2.3 Partitioning of large scale ontologies
We propose two methods of ontology partitioning. The aim of our methods is to have
minimum blocs to align with maximal number of concepts (that TaxoMap is able to
handle). The originality of our methods is that they are alignment oriented, that means
that the partitioning process is influenced by the mapping process.
The two methods relies on the implementation of PBM[4] algorithm. PBM parti-
tions large ontologies into small blocks (or modules) and construct mappings between
the blocks, using predefined matched class pairs, called anchors to identify related
blocks. We only reuse the partitioning part and the idea of anchors, but adapt them in
order to take into account the alignment process in the partitionning. We identify the set
of anchors as the set of concepts which have the same label in the two ontologies. Even
on very large ontologies, this set is computable with a fast and strict equality measure.
We also used the possible dissymmetry between ontologies to order the partitionning:
if one ontology is well-structured, it will be easier to split it up into cohesive modules,
and its partitionning can be used as guideline to partition the other ontology.
The methods proposed are as follows:
– Method1 (see figure 1):
1. Use PBM algorithm to partition the target ontology OT into some blocs BT i .
2. Identify the set of anchors included in each module BT i . This set will be the
kernel or center CBSi of the future module BSi which will be generated from
the source ontology OS .
3. Use PBM algorithm to partition the source ontology around the identified cen-
ters CBSi .
4. Align each module BSi with the corresponding module BT i .
– Method2 (see figure 2):
1. Partition the target ontology OT by modifying PBM algorithm in order to take
into account anchors. Generated modules contain coherent set of concepts that
maximize the number of anchors.
2. Partition the source ontology OS the same way then step 1. The interesting
anchors that influence partitioning are those that goes in the same module gen-
erated from OT .
3. Align modules that share maximal number of anchors.
Fig. 1. Method1 for partitioning Fig. 2. Method2 for partitioning
2.4 Adaptations made for the Evaluation
We do not make any specific adaptation in the OAEI 2008 campaign. All the alignments
outputted by TaxoMap are uniformly based on the same parameters. For library test, the
language was set to nl (for Dutch). We had, however, fixed confidence values depending
on relation types.
2.5 Link to the system and parameters file
TaxoMap requires :
– Mysql
– Java (from 1.5)
– TreeTagger 4 with its language parameter files.
The version of TaxoMap (with parameter files) used in 2008 contest can be down-
loaded from:
– http://www.lri.fr/˜haifa/TaxoMap.jar: a parameter lg has to be specified it denotes
the language of the ontology. For example TaxoMap.jar fr to perform alignment on
ontologies in French. If no language is specified, it is supposed to be English.
– http://www.lri.fr/˜haifa/TaxoMap.properties: a parameter file which specifies:
• The command to launch tree-tagger.
• Treetagger word categories that has to be considered as functional, stop words
and prepositions.
• The RDF output file.
• Different thresholds of similarity, depending on the method used.
– http://www.lri.fr/˜haifa/dbproperties.properties: a parameter file which contains the
user and password to access to MySql.
2.6 Link to the Set of Provided Alignments
The alignments produced by TaxoMap are available at the following URLs:
http://www.lri.fr/˜haifa/benchmarks/
http://www.lri.fr/˜haifa/anatomy/
http://www.lri.fr/˜haifa/directory/
http://www.lri.fr/˜haifa/library/
3 Results
3.1 Benchmark Tests
Since our algorithm only considers labels and hierarchical relations and only provides
mapping for concepts, the recall is low even for the reference alignment. The overall
results are almost similar -with no surprise- to those of last year.
The whole process of alignment costs less than 2 minutes.
4
http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger/
3.2 Anatomy Test
The anatomy real world case is to match the Adult Mouse Anatomy (denoted by Mouse)
and the NCI Thesaurus describing the human anatomy (tagged as Human). Mouse has
2,744 classes, while Human has 3,044 classes. As last year, we considered Human as
the target ontology as is it well structured and larger than Mouse.
TaxoMap gains considerably on runtime, it performs the alignment (with no need to
partition) in about 25 minutes which is better than last year where TaxoMap took about
5 hours to align the two ontologies.
TaxoMap generates much more mappings than last year. Only about 200 concepts
were left unmapped, whereas last year it was nearly 900.
As only equivalence relationships will be evaluated, we change different mapping types
to equivalence with these confidence values:
– (type1) For isEq and isClose relations, confidence value was set to 1.
– (type2) For isA relations generated by label inclusion, confidence value was set to
0.8.
– (type3) For isA relations generated by structural technique or by relative similarity
method, confidence value was set to 0.5.
TaxoMap discovers 2 533 mappings: 1 208 type1 relations, 1 190 type2 relations
and 135 type3 relations. The improvement in comparaison with last year results relies
on the use of TreeTagger and on taking into account synonymy.
3.3 Directory Test
The directory task consists of Web sites directories like Google, Yahoo! or Looksmart.
To date, it includes 4,639 tests represented by pairs of OWL ontologies. TaxoMap takes
about 40 minutes to complete all the tests.
3.4 Library Test
The library task includes two SKOS thesauri GTT and Brinkman thesauri. Since Tax-
oMap focuses on Web ontologies expressed in RDFS and OWL, we have to adopt two
OWL version ontologies transformed by campaign organizers in this task. GTT owns
35,000 classes, while Brinkman thesauri owns 5,000 classes. The main drawback of
using OWL ontologies is that there is no distinction in OWL descriptions (rdfs:label
statements) between skos:prefLabel, skos:altLabel and skos:hiddenLabel statements,
which removes the subtle distinctions that exist between these different properties.
We applied the first method of partitioning, this is due to the fact that only 3535
anchors were discovered and that the two ontologies were poorly structured. As the
method2 relies on these two informations simultaneously, the partitioning results were
not judged relevant.
The partitioning of Brinkman thesauri (considered as target ontology) leads to 227
modules, the largest module contains 703 concepts. GTT (source ontology) is parti-
tionned into 18 306 modules, 16 265 modules contain only one concept, the largest
module contains 517 concepts. We performed 212 combinations that leads to 3 217
mappings.
The fact that the total number of mappings is less than the number of found anchors
is due to the fact that anchors are computed between labels (a concept described by three
labels can have three anchors, which is not the case for mappings, where a concept is
matched to only one concept). As alignments are performed between modules, this can
lead to loose some potential mappings. This is particularly the case of all modules that
contain only one concept, as they are ignored by the alignment process.
As skos relations will be evaluated, we change different mapping types to skos ones
with these confidence values:
– (type1) isEq relations become skos:exactMatch with a confidence value set to 1.
– (type2) isA relations become skos:narrowMatch with a confidence value set to 1
for label inclusion, 0.5 for relations generated by structural technique or by relative
similarity method.
– (type3) isGeneral relations become skos:broadMatch with a confidence value set
to 1.
– (type4) isClose relations become skos:relatedMatch with a confidence value set to
1.
Generated mappings are as follows: 1 872 type1 relations, 1 031 type2 relations, 274
type3 relations and 40 type4 relations. The whole process of alignment costs about 40
minutes. The partitioning process costs nearly 2 hours.
The language of both thesauri is Dutch, we launched tree-tagger with Dutch pa-
rameter file. The main difficulty is that there were no Tagset description given for this
language and it was difficult to specify word categories needed for the linguistic simi-
larity method.
4 General Comments
4.1 Results
TaxoMap 2 significantly improves the results on the previous version of TaxoMap in
terms of runtime and number of generated mappings. The new implementation offers
extensibility and modularity of code. TaxoMap can be parameterized by the language
used in ontologies and different thresholds. We put the emphasis on terminological
alignment by taking into account synonymy and multi-label concepts. Our partitioning
algorithms allows us to participate to tests with large ontologies.
4.2 Future Improvements
The following improvements can be made to obtain better results:
– Use of WordNet as a dictionary of synonymy. The synsets can enrich the termino-
logical alignment process if an a priori disambiguation is made.
– To develop the remaining structural techniques which proved to be efficient in last
experiments [5] [6].
5 Conclusion
This paper reports our participation to OAEI campaign with a new implementation of
TaxoMap. Our algorithm proposes an oriented mapping between concepts. TaxoMap 2
is better now than last year. Due to partitioning, it is able to perform alignment on real-
world ontologies. Our participation in the campaign allows us to test the robustness of
TaxoMap, our partitioning algorithms and new terminological heuristics.
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