ServOMBI at OAEI 2015

Abstract

We describe in this paper the ServOMBI system and the results achieved during the 2015 edition of the Ontology Alignment Evaluation Initiative. ServOMBI reuse components from the ServOMap ontology matching system, which uses to participate in the OAEI campaign, and implements new features. This is the rst participation of the ServOMBI in the OAEI challenge.

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In the 2015 edition, ServOMBI participated in the entity level matching tasks apart from the Multifarm task. In the following sections, we described the main characteristics of the system and the overall results obtained during this year edition of OAEI. 1.1

State, Purpose, General Statement

ServOMBI has been built on the basis of the ServOMap system. ServOMap is designed with the purpose of facilitating interoperability between di erent systems which are based on heterogeneous knowledge organization systems (KOS). The heterogeneity of these KOS may have several causes ranging from the language format they use to the level of formalism of the terminology which describe the entities they involve. Our system relies on Information Retrieval (IR) techniques [ 4 ] and a dynamic description of entities of di erent KOS for computing the similarity between them.

ServOMBI implements new features and strategies and reuse some components of the ServOMap system. 1.2

Speci c techniques used

The overall process followed by the ServOMBI system is depicted on Figure 1. The initialization phase is modi ed by introducing the Binary Ontology Indexing (BOI). 1.2.1

Initialization phase 1. Ontology Loading: ServOMBI following the ServOMap approach relies on IR techniques for ontology matching. Each ontology to process is seen as a corpus of semantic documents to process. Each entity of the ontology is a document in the sense of IR. It is therefore necessary to identify the useful descriptors for indexing entities. The loading step perform the task of generating documents from entities. ServOMBI uses di erent reasoners (ELK [ 9 ], Hermit[ 11 ]) according to the size of the ontology to process. 2. Metadata and Metrics Generation: This step reuse the component implement in the ServOMap system and and identify 4 categories of matching tasks that are used to classify the input ontologies that are being processed : entity matching task (small, medium, big) and instances matching task. 3. Terminological Indexing: Following a generic metamodel provided by the ServO OR, a terminological based inverted index is built from the documents generated during the loading step. ServOMBI introduces a tasks parallelization using multithreading as the input ontologies terminology indexing could be done separately. 4. Binary Ontology Indexing: To complement the terminological indexing and to optimize the performance in terms of processing times due to the contextual similarity computation (surrounding concepts lookups), we have introduced a binary indexing strategy for the input ontologies. This technique of taxonomical representation consists of representing each concept of the ontology by a binary code, is inspired by the CEDAR system [ 5 ]. A binary code is a number of n bits, with n the number of concepts within the processed ontology. Thus, each concept has a code (a bit vector) carrying a \1" in the position corresponding to his index and the index of any other elements that it subsumes. These bit vectors must be encoded as the re exive transitive closure of the \is-a" relation obtained from subsort declarations. Concepts are represented by a graph. Figure 2 gives an example of a binary representation of the extract of an ontology in the academic domain. The concept Professor is the ancestor of Full Professor and Associate Professor. Therefore, if Full Professor is coded as the binary code of 1 and Associate Professor as the binary code of 2, Full Professor is coded as the binary code of 3. 1.2.2

Candidate Retrieving phase Three main steps are used during the candidate mappings retrieving phase: terminological, extended (general purpose knowledge background) and contextual based candidate retrieving. The terminological based candidate retrieving uses indexes previously built and the IR common vectorial model. The extended candidate retrieving uses WordNet [ 7 ] while the contextual based candidate retrieving exploits the structure of each input Ontology, and the set of candidates provided by the terminological based candidate retrieving, in a ML strategy for acquiring more candidates. The ML strategy is based on the Logistic Model Trees (LMT) [ 10 ] algorithm.

Task

Anatomy

Precision 0.963 Recall 0.617

In this phase two main steps are performed: the selction of the nal mappings and consistency checking. The selection of nal mappings implement an algorithm of the stable marriage problem [ 8 ]. ServOMBI use the Lucene Apache IR library. Lucene provides functionalities for indexing and searching textual documents. The actual version of the matching system is based on the version 4 while the uploaded version for OAEI is based on the version 3.6. Their index format is slightly di erent. We have implemented the initial interactive matching [ 12 ] in ServOMBI using an oracle by modifying the validation process of the candidate mappings . This is performed after each round of candidate retrieving. The wrapped SEALS client for ServOMBI version used for the OAEI 2015 edition is available at http://lesim.isped.u-bordeaux2.fr/servo/ServOMBI. The instructions for testing the tool is described in the tutorial dedicated to the SEALS client1.

Link to the set of provided alignments

The results obtained by ServOMap during OAEI 2015 are available at http://lesim.isped.ubordeaux2.fr/servo/ServOMBI/oaei2015.zip/. 2

Results

2.1

Anatomy

We summarize in this section the results obtained by ServOMBI during the 2015 edition of OAEI.

The Anatomy track consists of nding an alignment between the Adult Mouse Anatomy and a part of the NCI Thesaurus (describing the human anatomy). The results achieved by ServOMBI are summarized by Table 1. 2.2

Conference

The Conference track contains 16 ontologies from the same domain (conference organization). They have been developed within the OntoFarm project2. This 1http://oaei.ontologymatching.org/2015/tutorial/tutorialv4.pdf 2http://owl.vse.cz:8080/ontofarm/

Precision 0.64 0.29 0.61 0.6 0.29 0.57 0.59 0.29 0.56 year the di erent tools are evaluated using i) crisp reference alignments where the con dence values for all matches are 1.0, ii) the uncertain version of the reference alignment where con dence values re ect the degree of agreement of a group of twenty people on the validity of the match [ 6 ] and iii) logical reasoning using violations of consistency and conservativity principles [ 15 ] [ 16 ]. Various reference alignments and evaluation modalities (R.A.M.) are used to assess the performance of the tooms. Thus, ra1 is the original reference alignment of the Conference track, ra2 is entailed reference alignment generated as a transitive closure computed on the original reference alignment (ra1) and rar2 is violation free version of reference alignment. Three di erent modalities are provided for these reference alignments, M1, M2 and M3 which contain respectively only classes, only properties and classes and properties.

The results obtained by ServOMBI according to these di erent modalities on the crisp reference alignments where the con dence value is 1.0 are summarized on table 2. The value of is respectively set to 0.5, 1 (harmonic measure) and 2. 2.3

Largebio

The Large BioMed track consists of nding alignments between the Foundational Model of Anatomy (FMA), SNOMED CT, and the National Cancer Institute Thesaurus (NCI). The results obtained by ServOMBI for the small fragments of FMA-NCI task and FMA-SNOMED ontologies are summarize in Table 3 2.4

Interactive track

This track aims at o ering a systematic and automated evaluation of matching systems with user interaction to compare the quality of interactive matching approaches in terms of F-measure and number of required interactions. For the 2015 edition, the Conference, Anatomy and Largebio tracks dataset are used

Error rate

0.0 0.1 0.2 0.3

Precision

1.00 1.00 1.00 1.00

Recall

0.650 0.637 0.622 0.627

F-Measure

0.788 0.778 0.767 0.770 for the evaluation. Moreover, this year a domain experts with variable error rates, respectively 0.1, 0.2 and 0.3 are considered in addition to the perferct emmulated user (oracle) with error rate 0.0. ServOMBI participated for the rst year to this track. The interaction implemented currently in the system is mainly to allow the user validating the provided candidate mappings. Tables 4, 5 and 6 give respectively the results obtained by the system on the Anatomy, Conference and Largebio dataset for the Interactive track. We note that for the Largebio interactive track, the ServOMBI was only able to match the FMA-NCI small fragments and FMA-SNOMED small fragments.

Overall ServOMBI improved its performance when compared to the results obtained with the normal Anatomy, Conference and Largebio track. However, the system make a greater number of requests compared to the other participating systems in the Interactive track. 2.5

Ontology Alignment for Query Answering

This track does not follow the usual OAEI tasks for evaluating the performance of participating systems [ 14 ]. Precision and Recall are calculated with respect to the ability of the generated alignments to answer a set of queries in a ontologybased data access scenario where several ontologies exist. This track uses the Conference dataset for the evaluation with two reference alignments, the publicly available Conference track alignment (RA1) and the repaired one (RAR1). Table 7 summarizes the results of ServOMBI which succeed with 6 out of 18 queries.

Task OAQA RA1 OAQA RAR1

Precision

0.222 0.222 We have participated in the 2012 and 2013 edition with the ServOMap system which achieved overall good results. The performance of this system is very good in particular for the tasks involving large ontologies. The new features implemented within ServOMBI did not lead to overall improved performances according to the results of the ServOMap system as expected. The contextual similarity computation, which is performed iteratively, is very time consuming and did not improved the overall recall of the system. In addition, while there is a gain in terms of computation times with concepts lookups, the BOI does not impact the overall performance of the system in terms of times taken to perform the matching tasks. 4

Conclusion

We have described in this paper the main functionalities of the ServOMBI ontology matching system and the overall results obtained during the 2015 OAEI edition. ServOMBI introduces a binary indexing strategy to complement the usual terminological indexing strategy used by the ServOMap system. The system achieved performance lower than expected according to the introduced features for the contextual similarity coputation. However it succed improving the F-Measure whith the interaction strategy. ServOMap continues to be developed in parralel and now include graph-based visualization.

As of future work, we envision to investigate an improved integration of the binary indexing and the contextual similarity computing. In addition, we plan to use combine multiple learning algorithms to improve the candidate selection during the contextual similarity computing. 5

Acknowledgments

This work has been partly supported by the project DRUG-SAFE funded ANSM (Agence Nationale de la Securite du Medicament). We also thank the organizers of OAEI with providing test dataset and the evaluation infrastructure.

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