Ontology matching produces correspondences between entities of two ontologies. The OMReasoner is unique in that it creates an extensible framework for combination of multiple individual matchers, and reasons about ontology matching by using description logic reasoner. It handles ontology matching in semantic level and makes full use of the semantic part of OWL-DL instead of structure. This paper describes the result of OMReasoner in the OAEI 2012 competition in two tracks: benchmark and conference.
Ontology matching finds correspondences between semantically related entities of the ontologies. It plays a key role in many application domains.
Many approaches to ontology matching have been proposed: the implementation of
match may use multiple match algorithms or matchers, and the following
largelyorthogonal classification criteria are considered [
Most approaches focus on syntactic aspects instead of semantic ones. OMReasoner achieves the matching by means of reasoning techniques. Still, this approach includes strategy of combination of (mainly syntactical) multi-matchers (e.g., EditDistance matcher, Prefix/Suffix matcher, WordNet matcher) before match reasoning. 1.1
The matching process can be viewed as a function f.
A’=f(O1, O2, A, p, r)
Where O1 and O2 are a pair of ontologies as input to match, A is the input alignment between these ontologies and A’ is new alignment returned, p is a set of parameters (e.g., weight w and threshold τ) and r is a set of oracles and resources.
OMReasoner C1’,C2’, R1’,R2’
C1,C2, R1,R2… p ( w,τ) r
dictionary 2 multi-matchers matcher1 . . . matchern
Combination
3 reasoning
C1≡C1’ R1⊑R1’
C2≡C2’ C2⊒C3’
R2⊓R2’ evaluation results C1,C2, R1,R2… C1’,C2’, R1’,R2’ … multi-matchers
EditDistance A
A1 +
Similarity
WordNet A2 +
A3
A=A1+A2+A3
Fig.2. Instances of multi-matchers in OMReasoner
The OMReasoner achieved ontology alignment as following three steps (see Fig.1): 1. Parsing: we can achieve the classes and properties of ontologies by using ontology
API: Jena. 2. Combination of multiple individual matchers: the literal correspondences (e.g. equivalence) can be produced by using multiple match algorithms or matchers, for example, string similarity measure (prefix, suffix, edit distance) by string-based, constrained-based techniques. Also, some semantic correspondences can be achieved by using some external dictionary: WordNet. Then the multiple match results can be combined by weighted summarizing method. The framework of multi-matchers combination is supported, which facilitates inclusion of new individual matchers. 3. Reasoning: the further semantic correspondences can be deduced by using DL reasoner, which uses literal correspondences produced in step 2 as input.
Finally, we evaluate the results against the reference alignments, and compute two measures: precision and recall.
In OMReasoner, the framework for multi-matchers is flexible, and any new individual matcher can be included. Now, the instances of multi-matchers include EditDistance, Similarity and WordNet (see Fig.2). 1.2
OMReasoner includes summarizing algorithm to combine the multiple match results. The combination can be summarized over the n weighted similarity methods (see formula 1), where wk is the weight for a specific method, and simk(e1,e2) is the similarity evaluation by the method.
sim(e1, e2) = ∑kn-1 wk simk (e1, e2) (1) OMReasoner uses semantic matching methods like WordNet matcher and description logic (DL) reasoning.
WordNet1 is an electronic lexical database for English, where various senses (possible meanings of a word or expression) of words are put together into sets of synonyms. Relations between ontology entities can be computed in terms of bindings between WordNet senses. This individual matcher uses an external dictionary: WordNet to achieve semantic correspondences.
Another important matcher uses edit distance, which is a measure of the similarity between two words. Based on this value, we calculate the morphology analogous degree by using some math formula.
All the results of each individual matcher will be normalized before combination. OMReasoner employs DL reasoner provided by Jena. OMReasoner includes external rules to reason about the ontology matching. 2
Results：a comment for each dataset performed There are 46 alignment tasks in benchmark data set and 21 alignment tasks in conference data set. We test the data sets with OMReasoner and present the results in Table 1, Table 2, Fig 3 and Fig 4. The average measures (precision, recall and FMeasure) of Benchmark are 0516, 0.379 and 0.419 respectively. The average measures of Conference are 0.159, 0.506 and 0.266 respectively. In conclusion, the precision, recall and F-Measure are not satisfying. However, we will improve it in the future. 2.1
We evaluated the results against reference alignments, and obtained precision varies from 0 to 0.949, and recall varies from 0 to 1.000, F-Measure varies from 0 to 0.990. Some measures are zero, because the reference alignments are a little bit strange. For example, aqdsq in dataset 248 is equivalent to some class in dataset 101.
O1-O2 101-101 101-103 101-104 101-202 101-204 101-204 101-205 101-206 101-207 101-208 101-210 101-221 101-222 101-223 101-224 101-225 101-228 101-230 101-232 101-233 101-236 101-237 101-238 101-239 101-240 101-241 101-246 101-247 101-248 101-249 101-250 101-251 101-252 101-253 101-254 101-257 101-258 101-259 101-260 101-261 101-262 101-265 101-266 101-301 101-302 101-304
Prec. 0.919 0.919 0.919 0 0 0.917 0.133 0.540 0.551 0.917 0.600 0.919 0.914 0.919 0.919 0.919 0.868 0.949 0.919 0.868 0.868 0.914 0.919 0.853 0.868 0.868 0.794 0.868 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.800 0.833 0 confOf-edas confOf-ekaw confOf-iasted confOf-sigkdd The precision of results is not good enough, because only a few individual matchers are included.
The measures in Benchmark are better than those in Conference. The major reason is that the structure similarity of ontology is not considered in our tool.
The performance of inference relies on the literal correspondences heavily, so more accurate results which are exported from multi-matchers will greatly enhance the results of our tool.
Some probable approaches to improving our tool are listed as follow: 1. Adopt more flexible strategies in multi-matchers combination instead of just weighed sum. 2. Add some pre-processes, such as separating compound words, before words are imported into matchers. 3. Take comments and label information of ontology into account, especially when the name of concept is meaningless. 4. Improve the algorithm of some matchers. 5. More different matchers can be included.
Another problem in our tool is that we ignore structure information among ontology at the present stage. And we will improve it in the future. 3.3
OAEI procedure arranged everything in good order, furthermore SEALS platform provides a uniform and convenient way to standardize and evaluate our tool. 4
In this paper, we presented the results of the OMReasoner system for aligning onltologies in the OAEI 2012 competition in two tracks: benchmark and conference. The combination strategy of multiple individual matchers and DL reasoner are included in our approach. This is the second time we participate the OAEI, the results is still not satisfying and we will improve it in the future. 6. Do, H. and Rahm, E.: COMA- a system for flexible combination of schema matching approaches. In: Proceedings of the International Conference on Very Large Databases, 610--621.( 2002) 7. Giunchiglia, F., Shvaiko, P., and Yatskevich, M.: S-Match: an algorithm and an implementation of semantic matching. In: Proceedings of the European Semantic Web Symposium, 61--75.( 2004) 8. Kalfoglou, Y. and Schorlemmert, M.: If-map: an ontology mapping method based on information flow theory. In: Proceedings of ISWC’03, Workshop on Semantic Integration, (2003) 9. Bouquet, P., Serafini, L., and Zanobini, S.: Semantic coordination: A new approach and an application. In: Proceedings of the International Semantic Web Conference, 130-145.( 2003) 10. Baader, F., Calvanese, D., McGuinness, D., et al.: The description logic handbook: theory, implementations and applications. Cambridge University Press, (2003) 11. Ehrig, M., Sure, Y.: Ontology mapping - an integrated approach. In Proceedings of the
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