Ontology mapping has been recognised as an important approach to identifying similar information in heterogeneous ontologies. The Knowledge Organisation System Implicit Mapping (KOSIMap) approach relies on DL reasoning (i) to extract background knowledge about every entity, and (ii) to remove inappropriate correspondences from an alignment. The main assumption is that the use of this background knowledge reduces erroneous mappings, thus increasing coverage. In this paper, we provide an overview of KOSIMap, and present the result of our system for its rst participation to the Ontology Alignment Evaluation Initiative (OAEI).
1.1
KOSIMap is an extensional and asymmetric matching approach implemented
in Java. Given two consistent ontologies, KOSIMap aligns entities in the source
ontology to entities in the target ontology by extracting background knowledge
about entities based on DL reasoning. More speci cally, a DL reasoner (e.g.
FaCT++ [
The current KOSIMap implementation of produces a set of homogeneous correspondences, where classes are mapped to classes. object properties to object properties, and datatype properties to datatype properties. More speci cally, the approach computes the similarity between two entities based on their respective sets of features (e.g. subsumption). Note that KOSIMap only considers the equivalence mapping relation between two entities. 1.2
The KOSIMap system calculates the similarity between entities for a pair of
ontologies by analysing three features; namely lexical description (i.e. label),
hierarchical structure (subsumers for concepts, and super-properties), and internal
structure (inherited properties for classes, domains and ranges for object
properties, and domains for datatype properties). The measures obtained by comparing
these three features are then combined into a single value using a weighted sum
in a similar manner to [
Figure 1 shows the architecture of our approach. KOSIMap consists of three
main steps; namely Pre-Processing, Similarity Generation, and Alignment
Extraction. The pre-processing step includes three sub-tasks. It rst parses the
two ontologies with the OWL API [
De nition 1 (Degree of Commonality Coe cient). Given two sets Ss and St, the degree of commonality coe cient between them, denoted DoCCoe (Ss, St) is de ned as:
DoCCoe (Ss; St) = 1
∑ max sim(ei; ej ) max(jSsj; jStj) ei2Ss ej2St (1) where Ss is the source set, St is the target set, and sim(ei; ej ) computes the similarity between pair of elements in the two sets.
Secondly, the similarity generator computes three kinds of similarities; namely
syntax similarity, property-based similarity, and class-based similarity. The most
basic feature of entities is their labels. Labels are human identi ers (i.e. words)
expressed in a vocabulary shared by experts in the same domain. Therefore, we
assume that equivalent classes are likely to be modelled using similar labels (or
names). KOSIMap relies on string similarity (e.g. Jaro-Winkler [
sim(e1; e2) = n ∑ wksimk(e1; e2) k=0 (2)
After the similarity aggregation, we have a n*m matrix containing pairs of
entities with a similarity value. The problem is to extract a set of relevant
mappings from the matrix. This is normally achieved by discarding all candidate
mappings below a threshold . However, this method may return multiple
mappings for each entity in the source ontology. In KOSIMap, we follow a two-step
approach to extract mappings. First, the approach extracts a pre-alignment from
the matrix, by selecting the maximum similarity score for each row in the matrix
(i.e. for each n). This pre-alignment is then passed through a re nement process,
which eliminates inappropriate mappings. In KOSIMap, we use DL reasoning to
extract the local implication as part of the mapping extraction process. This
approach extends the work by Wang and Xu [
As stated in Section 1.1, KOSIMap is an asymmetric matching approach. The asymmetricity results from Equation 1, which consider the maximum value for each element in the source set. However, the organisers of OAEI campaign requested that we delivered a symmetric set of alignments. As a result, we modify the similarity generation for the property-based and class-based similarity to consider the biggest set as the source set. Moreover, we implemented a Java class to run the di erent tracks in batch mode. Moreover, the parameters taken by the approach (i.e. weights and thresholds) were tuned and set depending on the type of information contained in the ontologies to be mapped. For example, the property-based similarity was not calculated for the directory track as no properties were de ned. 1.4
The results of the 2009 OAEI campaign for the KOSIMap system can be found at http://www.csd.abdn.ac.uk/~qreul/research/OAEI2009.zip. 2
In this section, we present the results of the 2009 OAEI campaign obtained
by the KOSIMap system. KOSIMap was used to generate alignments for four
tracks, namely benchmark, anatomy, conference and directory. Note that the full
results of the Alignment Evaluation Initiative (OAEI) 2009 Campaign can be
found in [
The benchmark data set contains 111 alignment tasks. KOSIMap follows the approach de ned in Section 1.2. In this experiment, we used the Q-Gram similarity measure to compute the syntax similarity and as the similarity function for the degree of commonality coe cient. The FaCT++ reasoner returned an exception ( NonSimpleRoleInNumberRestrictionException) for some tests (i.e. 222, 230, 237, 251, 258, and 304), so we used the Pellet reasoner for this test. The threshold was set to 0.2, while the weights were set as follows: { Weight for syntax similarity: 0.3 { Weight for property-based similarity: 0.2 { Weight for class-based similarity: 0.5
KOSIMap gets near perfect alignment (Precision and Recall is 0.99) for tests 101, 103 and 104 (Table 1). Although KOSIMap performs quite well in the 2xx tests, it yields very low recall ( 0:1) when labels in the target ontology have been scrambled (i.e. tests #202 #248, #249, #25x, and #26x). Note that KOSIMap yields high recall (i.e. 0:9) for tests #221 to #247. For the real ontology data set (i.e. 3xx), KOSIMap yields 0.815 for Precision and .425 for Recall. Finally, KOSIMap achieves a much better harmonic mean precision than edna even though our system yields the same recall. The anatomy data set consists of two large scale anatomy ontologies. On the one hand, the Adult Mouse Anatomical Dictionary5 represents the anatomical structure of the postnatal mouse and contains 2744 classes organised hierarchically
by \is-a" and \part-of" relationships. On the other hand, the NCI Thesaurus6 is a reference terminology and biomedical ontology covering clinical care, translational and basic research, public information, and administrative activities. This ontology contains a subset of the classes de ned in the thesaurus (i.e. 3304 classes). Note that the property-based similarity was discarded for this track as these ontologies only contain a very small number of properties.
KOSIMap produces an alignment for three of the four sub-tasks of this track: 1. Optimal solution: The optimal solution is obtained with a threshold set to 0.6, the syntax similarity set to 0.6 and the class-based similarity set to 0.4.
It took KOSIMap approximately 5 min to generate the alignment. 2. Optimal precision: The optimal solution is obtained with a threshold set to 0.7, the syntax similarity set to 0.6 and the class-based similarity set to 0.4.
It took KOSIMap approximately 5 min to generate the alignment. 3. Optimal recall : The optimal solution is obtained with a threshold set to 0.6, the syntax similarity set to 0.6 and the class-based similarity set to 0.4. It took KOSIMap approximately 5 min to generate the alignment.
The results of the anatomy track are shown in Table 2. KOSIMap takes around 5 minutes to extract mappings between the Adult Mouse Anatomical Dictionary and the NCI Thesaurus and the F-Measure is 0.72. We observer signi cant di erences with regard to the trade-o between precision and recall. For instance, we observe that the recall obtained by KOSIMap falls from 0.62 to 0.45 when generating an alignment for optimal precision sub-task. As KOSIMap favours recall over precision, the score obtained for the optimal recall sub-task is the same as the optimal solution. 2.3
This track contains 15 ontologies covering the conference organization domain. These ontologies di er in terms of DL expressivity and size. For example, ekaw.owl is represented in SHIN , while paperdyne.owl is expressed in ALCHIN (D).
KOSIMap generated 105 non-empty alignments with parameters set as follows: { Weight for syntax similarity: 0.3 { Weight for property-based similarity: 0.2 { Weight for class-based similarity: 0.5
Table 3 shows the precision, recall, and F-measure computed for three different thresholds (0.2, 0.5, and 0.7). The results show that KOSIMap reaches an optimal solution with the threshold set to 0.5 before obtaining lower performances with higher thresholds. Moreover, the precision achieved by our system increases at the same time as the threshold. The directory track consists of 4639 test cases. As no properties (object properties or datatype properties) are found in this track, the property-based similarity is discarded for this track. In this experiment, the threshold is set to .0, while the weights are set to 0.6 (for syntax similarity) and 0.4 (for the class-based similarity). Due of the low expressivity of the ontologies (i.e. AL), we simpli ed the rules to retain the correspondence with the highest score when a class in the source ontology maps to several classes in the target ontology. KOSIMap takes just over 1 minute to generate the 4639 alignments. The preliminary results of this track yielded a score of 0.618 for Precision, 0.453 for Recall, and a F-Measure of 0.523. 3
3.1
From the results we can see that KOSIMap can take advantage of all di erent features associated with entities. The lexical description is especially important to achieve high precision and recall, while the hierarchical and internal structure are used to re ne the nal alignment. For example, tests in the benchmark track with scrambled labels (i.e. tests 248 to 266) tend to yield very low recall.
Based on the anatomy track, we have demonstrated the scalability of our approach. Although the two ontologies are not very expressive (i.e. ALE for AMA and ALE + for the NCI thesaurus), we have shown that the use of a DL reasoner does not impact the scalability of our system. Thus, this result suggests that the use of a reasoner does not greatly increase the runtime of the mapping task. Note that testing on more expressive large-scale ontologies should be carried to further test this observation. KOSIMap uses di erent strategies to extract correspondences between two ontologies. Based on the test library, we have seen that some strategies (e.g. property-based similarity) were not always useful to extract alignments. One possible way to improve the current system would be to include a strategy selection module. With strategy selection, KOSIMap could avoid some some noise produced by some strategies when the information these strategies rely on is not adequate. For example, when no properties are de ned in the ontology.
Another improvement to the system would be to include a module to ne-tune weights when combining the di erent similarity measure. The current approach relies on the user to analyse the information contained in the ontologies. It is important to note that this is process is both time-consuming and error prone. A solution to this problem would be to consider the DL expressivity of both ontologies to analyse the impact of each measure on the global similarity value. The advantage of the OAEI test library is that it provides a wide range of tests covering real word and modied ontologies. For example, the benchmark track allows anyone to clearly identify the strengths and weaknesses of their systems. The library also includes test cases for comparing large scale ontologies. However, the ontologies provided in the anatomy track are not very expressive. As a result, it is di cult to address the impact of using DL reasoners on large scale ontologies. 4
In this paper, we present the KOSIMap system, which aligns entities from two ontologies. This system relies on DL reasoning to (i) extract background knowledge about every entity, and (ii) to remove inappropriate correspondences from an alignment. KOSIMap consists of three main steps; namely Pre-Processing, Similarity Generation, and Alignment Extraction. It rst parses the two ontologies, extracts the implicit structure of both ontologies using an OWL DL reasoner, and applies natural language techniques to lexical descriptions (i.e. labels). Next, it computes three di erent types of similarities for every pair of entities. These similarity values are then combined and stored in a n*m matrix from which a pre-alignment is extracted. This pre-alignment is then passed through a re nement process, which eliminates inconsistent mappings.
Secondly, we report the results obtained by KOSIMap for its rst participation to the Ontology Alignment Evaluation Initiative. From the results of the benchmark test case, we can see that our system can take advantage of all di erent features associated with entities during the ontology mapping task. We have also shown that KOSIMap remains scalable despite using DL reasoning throughout the mapping process. However, testing on more expressive large-scale ontologies should be carried to further test this observation.
The IPAS project is co-funded by the Technology Strategy Board's Collaborative Research and Development programme (www.innovateuk.org) and Rolls-Royce (project No. TP/2/IC/6/I/10292).