Experience in Aligning AnatomicalOntologies

Abstract

An ontology is a formal representation of a domain modeling the entities in the domain and their relations. When a domain is represented by multiple ontologies, there is need for creating mappings among these ontologies in order to facilitate the integration of data annotated with these ontologies and reasoning across ontologies. The objective of this paper is to recapitulate our experience in aligning large anatomical ontologies and to reflect on some of the issues and challenges encountered along the way. The four anatomical ontologies under investigation are the Foundational Model of Anatomy, GALEN, the Adult Mouse Anatomical Dictionary and the NCI Thesaurus. Their underlying representation formalisms are all different. Our approach to aligning concepts (directly) is automatic, rule-based, and operates at the schema level, generating mostly point-to-point mappings. It uses a combination of domain-specific lexical techniques and structural and semantic techniques (to validate the mappings suggested lexically). It also takes advantage of domain-specific knowledge (lexical knowledge from external resources such as the Unified Medical Language System, as well as knowledge augmentation and inference techniques). In addition to point-to-point mapping of concepts, we present the alignment of relationships and the mapping of concepts groupto-group. We have also successfully tested an indirect alignment through a domain-specific reference ontology. We present an evaluation of our techniques, both against a gold standard established manually and against a generic schema matching system. The advantages and limitations of our approach are analyzed and discussed throughout the paper.

Evaluating Thesaurus Alignments for Semantic Interoperability in the Library Domain

“…As most tools are still highly experimental and not used in practical applications, the fi rst evaluation efforts favored mostly "application-independent" methods. 3 Researchers typically created and used manually built reference alignments (or gold standards) that were often biased toward-at best-one single usage scenario (for example, vocabulary merging), with little use for other scenarios. More recent evaluation approaches have adopted more realistic assessments by using, for instance, application-specifi c sampling methods and measures.…”

Using Rules to Investigate the Differences in Partonomy between Biomedical Ontologies

“…Work on ontology mapping related with rules can be found in [11] where mapping heuristics were stated as rules and in [12] where manually established mappings were represented as rules in order to support agent communication. In one of our previous studies [13], we used Jena rules to identify the granularity differences in ISA classification between ontologies, of which the work in this paper is a direct follow-up.…”

Effective Composition of Mappings for Matching Biomedical Ontologies

“…State-of-the art match systems such as COMA++ [2], Falcon [16] or SAMBO [21] combine multiple matchers within a match strategy to achieve better match quality. Results of matching biomedical ontologies showed that linguistic matching methods based on the similarity of concept names and synonyms produce very good results [35,12]. To improve the runtime of matching (especially for large ontologies) some systems try to reduce the search space [17] or perform parallel matching on multiple compute nodes [13].…”