=Paper= {{Paper |id=Vol-1111/om2013_poster4 |storemode=property |title=An ontology mapping method based on support vector machine |pdfUrl=https://ceur-ws.org/Vol-1111/om2013_poster4.pdf |volume=Vol-1111 |dblpUrl=https://dblp.org/rec/conf/semweb/LiuQW13 }} ==An ontology mapping method based on support vector machine== https://ceur-ws.org/Vol-1111/om2013_poster4.pdf
An Ontology Mapping Method Based on Support Vector
                    Machine
                               Jie Liu,Linlin Qin, Hanshi Wang

    College of Information and Engineering, Capital Normal University, Beijing 100048,
                                        P.R.China

Correspondence should be addressed to Jie Liu, liujxxxy@126.com



       Abstract. Ontology mapping has been applied widely in the field of semantic
       web. In this paper a new algorithm of ontology mapping were achieved. First,
       the new algorithms of calculating four individual similarities (concept name,
       property, instance and structure) between two concepts were mentioned. Se-
       condly, the similarity vectors consisting of four weighted individual similarities
       were built, and the weights are the linear function of harmony and reliability,
       and the linear function can measure the importance of individual similarities.
       Here, each of ontology concept pairs was represented by a similarity vector.
       Lastly, Support Vector Machine (SVM) was used to accomplish mapping dis-
       covery by training the similarity vectors. Experimental results showed that, in
       our method, precision, recall and f-measure of ontology mapping discovery
       reached 95%, 93.5% and 94.24%, respectively. Our method outperformed other
       existing methods.

Introduction: In this paper, our study mainly is to discover the mapping[1] between
concepts belonging to the different ontologies respectively. The proposed algorithm
about ontology mapping in this paper mainly focuses on the following two points:
1. Using new methods of calculating individual similarities (concept name, property,
instance and structure).
2. Proposing the methods of similarity aggregation using SVM to classify the similari-
ty vectors which reflect the similarities of concept pairs. Here, the elements of a simi-
larity vector consist of the weighted individual similarities, and the weight of an indi-
vidual similarity is the linear function of harmony[2] and reliability[3].
    To evaluate the method proposed in this paper, we used the benchmark tests in
OAEI ontology matching campaign 2012 as data sets, and got precision, recall and f-
measure of the different ontology mapping algorithms by experiment.
The algorithms of ontology mapping: The process from calculating similarities to
discovering ontology mapping is shown as Fig.1. In Fig.1, O1, O2 are two ontologies.
Firstly, four individual similarities were computed; secondly, the similarity vectors
consisting of four weighted individual similarities were built, and the weights were
decided by both of harmony and reliability. Here, each of concept pairs between two
ontologies was represented by a similarity vector; lastly, SVM was used to accom-
plish mapping discovery by classifying the similarity vectors.
                                                   O1                O2
                Class name similarity   Property similarity    Instance similarity   Structure similarity

                Class name similarity    Property similarity   Instance similarity   Structure similarity
                based on harmony &      based on harmony&      based on harmony&     based on harmony&
                      reliability            reliability            reliability           reliability

                                                    Similarity vectors



                                            Mapping discovery based on SVM




                                                  O1                 O2

                                  Fig.1. Process of ontology mapping
Experiment Design: Ontology mapping methods related to similarity calculation
have been discussed in many studies, and precision, recall and f-measure are usually
used to evaluate mapping results. Experimental steps are as follows:
  (1) For all ontological concept pairs, the four individual similarities would be calcu-
lated; (2)These similarities would be aggregated and mappings between ontologies
would be extracted by using 11 methods such as “Neural network”, “Sigmoid”, “Harmo-
ny”, “Reliability” and so on;(3) For our approach, after four individual similarities and
their respective harmony and reliability were worked out, similarity vectors consisting
of our weighted individual similarities would be built, and the weights are the linear
function of harmony and reliability, and ontology mappings would be extracted by
SVM;(4)For all ontology pairs, precision, recall and f-measure of ontology mapping
discovery would be calculated in every methods.
Result: Precision, recall and f-measure in our approach reach 0.95,0.935 and 0.9424,
respectively, and are the highest, which can validate that the results of mapping dis-
covery are more accurate after harmony and reliability is joined into SVM, and also
can show that our approach outperforms than others dramatically.
Conclusions: This study is an effective approach to resolve the problem about ontol-
ogy mapping in the Semantic Web. Future work will focus on studying the mapping
algorithms between uncertain ontologies.

ACKNOWLEDGMENTS

This paper is supported by the National Nature Science Foundation (No.61371194,
61303105).

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