Abstract
Identifying relationships between hitherto unrelated entities in different ontologies is the key task of ontology alignment. An alignment is either manually created by domain experts or automatically by an alignment system. In recent years, several alignment systems have been made available, each using its own set of methods for relation detection. To evaluate and compare these systems, typically a manually created alignment is used, the so-called reference alignment. Based on our experience with several of these reference alignments we derived requirements and translated them into simple quality checks to ensure the alignments’ validity and also their reusability. In this article, these quality checks are applied to a standard reference alignment in the biomedical domain, the Ontology Alignment Evaluation Initiative Anatomy track reference alignment, and two more recent data sets covering multiple domains, including but not restricted to anatomy and biology.
Highlights
In knowledge-intensive domains such as the life sciences, there is an ever-increasing need for concept systems and ontologies to organize and classify the large amounts of clinical and lab data and to describe such data collections with value-adding meta data
Results of applying the quality checks We ran the ten basic quality checks on the data sets introduced above and achieved the following results: Check 1 In the ANATOMY data set, the reference alignment is used together with a version of the NCI Thesaurus anatomy branch as from 2006-02-13, and a version of the MA as from 2007-01-18, while the alignment itself was created based on the NCI Thesaurus release version 04.09a and the MA version as from 2004-11-22 [12]
Check 8 After applying a simple term normalization procedure to all class labels, for the ANATOMY data set we found 13 class pairs and for the Linked Open Data (LOD) alignments a total of 37 class pairs with identical labels for which no equivalentClass-based correspondence existed in the respective manual alignment
Summary
In knowledge-intensive domains such as the life sciences, there is an ever-increasing need for concept systems and ontologies to organize and classify the large amounts of clinical and lab data and to describe such data collections with value-adding meta data. For this purpose, numerous ontologies with different levels of coverage, expressiveness and formal rigor have evolved that, from a content point of view, complement each other and in some cases even overlap. To facilitate the interoperability between information systems using different ontologies and to detect overlaps between them, ontology alignment has become a crucial need. A correspondence consists of a pair of entities (e.g., a class from the first input ontology, O1, and a class from the second one, O2) and a relation that, according to the creator of the alignment, holds between these entities (e.g., an equivalentClass or subClassOf relation between two classes)
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