Collecting Community-Based Mappings in an Ontology Repository

A number of ontology repositories provide access to the growing collection of ontologies on the Semantic Web. Some repositories collect ontologies automatically by crawling the Web; in other repositories, users submit ontologies themselves. In addition to providing search across multiple ontologies, the added value of ontology repositories lies in the metadata that they may contain. This metadata may include information provided by ontology authors, such as ontologies’ scope and intended use; feedback provided by users such as their experiences in using the ontologies or reviews of the content; and mapping metadata that relates concepts from different ontologies. In this paper, we focus on the ontology-mapping metadata and on community-based method to collect ontology mappings. More specifically, we develop a model for representing mappings collected from the user community and the metadata associated with the mapping. We use the model to bring together more than 30,000 mappings from 7 sources. We also validate the model by extending BioPortal—a repository of biomedical ontologies that we have developed—to enable users to create single concept-toconcept mappings in its graphical user interface, to upload and download mappings created with other tools, to comment on the mappings and to discuss them, and to visualize the mappings and the corresponding metadata. 1 Ontology Mapping and the Wisdom of the Crowds As the number of ontologies available for Semantic Web applications grows, so does the number of ontology repositories that index and organize the ontologies. Some repositories crawl the Web to collect ontologies (e.g., Swoogle [4], Watson [3] and OntoSelect [2]). In other repositories, users submit their ontologies themselves (e.g., the DAML ontology library1 and SchemaWeb2). These repositories provide a gateway for users and application developers who need to find ontologies to use in their work. In our laboratory, we have developed BioPortal3—an open repository of biomedical ontologies. Researchers in biomedical informatics submit their ontologies to BioPortal and others can access the ontologies through the BioPortal user interface or through web services. The BioPortal users can browse and search the ontologies, update the ontologies in the repository by uploading new versions, comment on any ontology (or portion of an ontology) in the repository, evaluate it, describe their experience in using the ontology, 1 http://www.daml.org/ontologies/ 2 http://www.schemaweb.info/ 3 http://alpha.bioontology.org or make suggestions to ontology developers. At the time of this writing, BioPortal has 72 biomedical ontologies with more than 300,000 classes. While the BioPortal content focuses on the biomedical domain, the BioPortal technology is domain-independent. Ontologies in BioPortal, as in almost any ontology repository, overlap in coverage. Thus, mappings among ontologies in a repository constitute a key component that enables the use of the ontologies for data and information integration. For example, researchers can use the mappings to relate their data, which had been annotated with concepts from one ontology, to concepts in another ontology. We view ontology mappings as an essential part of the ontology repository: Mappings between ontology concepts are first-class objects in the BioPortal repository. Users can browse the mappings, create new mappings, upload the mappings created with other tools, download mappings that BioPortal has, or comment on the mappings and discuss them. The mapping repository in BioPortal address two key problems in ontology mapping. First, our implementation enables and encourages community participation in mapping creation. We enable users to add as many or as few mappings as they like or feel qualified to do. Users can use the discussion facilities that we integrated in the repository to reach consensus on controversial mappings or to understand the differences between their points of view. Most researchers agree that, even though there has been steady progress in the performance of the automatic alignment tools [5], experts will need to be involved in the mapping task for the foreseeable future. Enabling community participation in mapping creation, we hope to have more people contributing mappings and, hence, to get closer to the critical mass of users that we need to create and verify the mappings. Second, the integration of an ontology repository with a mapping repository provides users with a one-stop shopping for ontology resources. The BioPortal system integrates ontologies, ontology metadata, peer reviews of ontologies, resources annotated with ontology terms, and ontology mappings, adding value to each of the individual components. The services that use one of the resources can rely on the other resources in the system. For instance, we can use mappings when searching through OBR. Alternatively, we can use the OBR data to suggest new mappings. The BioPortal mapping repository contains not only the mappings created by the BioPortal users, but also (and, at the time of this writing, mostly) mappings created elsewhere and by other tools, and uploaded in bulk to BioPortal. In recent years, Semantic Web researchers explored community-based approaches to creating various ontology-based resources [16]. For example, SOBOLEO [26] uses an approach that is similar to collaborative tagging to have users create a simple ontology. Collaborative Protégé [15] enables users to create OWL ontologies collaboratively, discussing their design decisions, putting forward proposals, and reaching consensus. BioPortal harnesses collective intelligence to provide peer reviews of ontologies and to have users comment on ontologies and ontology components [21]. Researchers have also proposed using community-based approaches to create mappings [14]. For example, McCann and colleagues[12] asked users to identify mappings between database schemas as a “payment” for accessing some services on their web site. The authors then used these mappings to improve the performance of their mapping algorithms. They analyzed different characteristics of the user community and