Ontology Matching

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The API provides a format for expressing alignments in a uniform way. The goal of this format is to be able to share on the web the available alignments. The format is expressed in RDF, so it is freely extensible. It defines four main interfaces Alignment, Cell, Relation and Evaluator. Blooms is a tool for ontology matching. It utilizes information from Wikipedia category hierarchy and from the web to identify subclass relationship between entities. See also its Wiki page. The current implementation produces mappings between concepts, properties, and individuals.

CODI is based on the syntax and semantics of Markov logic and transforms the alignment problem to a maximum-a-posteriori optimization problem. Its graphical interface supports a variety of interaction. In addition, it integrates a partitioner PBM to cope with large-scale ontologies. GOMMA is a generic infrastructure for managing and analyzing life science ontologies and their evolution. The component-based infrastructure utilizes a generic repository to uniformly and efficiently manage many versions of ontologies and different kinds of mappings.

Different functional components focus on matching life science ontologies, detecting and analyzing evolutionary changes and patterns in these ontologies. HerTUDA is a simple, fast ontology matching tool, based on syntactic string comparison and filtering of irrelevant mappings. Despite its simplicity, it outperforms many state-of-the-art ontology matching tools. Users integrate information according to an ontology of their choice using a graphical user interface that automates much of the process. Karma learns to recognize the mapping of data to ontology classes and then uses the ontology to propose a model that ties together these classes.

KitAMO is a tool for evaluating ontology alignment strategies and their combinations. It supports the study, evaluation and comparison of alignment strategies and their combinations based on their performance and the quality of their alignments on test cases. The linked open data enhancer LODE framework is a set of integrated tools that allow digital humanists, librarians, and information scientists to connect their data collections to the linked open data cloud.

It can be applied to any domain with RDF datasets.

Computer Science > Artificial Intelligence

They are viewed as the silver bullet for many applications, such as information integration, peer-to-peer systems, electronic commerce, semantic web services, social networks, and so on. They, indeed, are a practical means to conceptualize what is expressed in a computer format. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies.

Thus, just using ontologies, like just using XML, does not reduce heterogeneity: it raises heterogeneity problems at a higher level. Journal List J Biomed Semantics v. J Biomed Semantics. Published online Dec 2. Author information Article notes Copyright and License information Disclaimer. Corresponding author. Received Apr 13; Accepted Oct This article has been cited by other articles in PMC.

Comparing Ontologies in Protege 4.2

Abstract Background The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Results Eleven systems out of 21 OAEI participating systems were able to cope with at least one of the tasks in the Disease and Phenotype track.

Conclusions Four systems gave the highest performance for matching disease and phenotype ontologies. Background The Pistoia Alliance Ontologies Mapping project 1 was set up to find or create better tools and services for mapping between ontologies including controlled vocabularies in the same domain and to establish best practices for ontology management in the Life Sciences.

Open in a separate window. Table 1 Metrics of the track ontologies. Ontology Number of axioms Number of classes Maximum depth Avg. Preparation phase As specified by the OAEI the ontologies and public reference alignments were made available in advance during the first week of June Execution phase System developers had to implement a simple interface and to wrap their tools including all required libraries and resources in order to use the SEALS infrastructure.

Evaluation phase Participants were required to submit their wrapped tools by August 31st, Participating systems AML [ 21 , 22 ] is an ontology matching system originally developed to tackle the challenges of matching biomedical ontologies. Use of specialised background knowledge The use of specialised background knowledge is allowed in the OAEI, but participants are required to specify which sources their systems rely on to enhance the matching process.

Table 2 Recall against BioPortal baseline mappings. Creation of consensus alignments In the MP-HP matching task 11 systems were able to produce mappings. Votes 1 2 3 4 5 6 7 Mappings 0. Votes 1 2 3 4 5 6 Mappings 50, Table 5 Example mappings in the Disease and Phenotype track. Entity 1 Entity 2 Rel.

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Results against consensus alignments The union of the input ontologies together with the consensus alignments or the mappings computed by each of the systems was coherent and thus, we did not require to repair any of the mapping sets to calculate the semantic precision and recall. Table 8 Results against curated alignments. Table 9 Manual assessment of unique mappings and estimated positive and negative contribution in the HP-MP task.

System-mappings Unique mappings Precision Positive contrib. Negative contrib. BioPortal baseline 0 - AML 0. BioPortal baseline 5 0. Results in the OAEI interactive matching track The OAEI interactive track 20 aims at offering 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.

Discussion The OAEI has been proven to be an effective campaign to improve ontology matching systems. Notes Ethics approval and consent to participate Not applicable.

Ontology alignment

Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Contributor Information Ian Harrow, Email: gro. References 1. Results of the Ontology Alignment Evaluation Initiative The human phenotype ontology in Nucleic Acids Res. The mammalian phenotype ontology as a tool for annotating, analyzing and comparing phenotypic information. Genome Biol. Kibbe WA, et al.

Bibliographic Information

Disease Ontology update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. J Web Semantics. The alignment API 4. Semantic Web.

Technical documentation

Logic-based assessment of the compatibility of UMLS ontology sources. Euzenat J. Semantic precision and recall for ontology alignment evaluation. Fleischhacker D, Stuckenschmidt H. David J, Euzenat J. On fixing semantic alignment evaluation measures.

:: Ontology Matching ::

Meilicke C. Alignment incoherence in ontology matching.

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  • Ontology Matching Tools.
  • PhD thesis, University of Mannheim. Evaluating mapping repair systems with large biomedical ontologies. Ontology alignment repair through modularization and confidence-based heuristics. Results of the ontology alignment evaluation initiative Shvaiko P, Euzenat J. BioPortal: ontologies and integrated data resources at the click of a mouse.

    Berlin: Springer; Is my ontology matching system similar to yours? OTM Conferences. Dismatch results for OAEI Gabrilovich E, Markovitch S. Computing semantic relatedness using wikipedia-based explicit semantic analysis. Zhao M, Zhang S. Identifying and validating ontology mappings by formal concept analysis. Web Conf. Amsterdam: IOS Press; Extending an ontology alignment system with bioportal: a preliminary analysis. Integrating phenotype ontologies with PhenomeNET. PhenomeNET: a whole-phenome approach to disease gene discovery.

    Submission history

    Bodenreider O. The unified medical language system UMLS : integrating biomedical terminology. Uberon, an integrative multi-species anatomy ontology. Maryland: AMIA; Creating mappings for ontologies in biomedicine: Simple methods work. Towards evaluating interactive ontology matching tools. Requirements for and evaluation of user support for large-scale ontology alignment.