Recognizing lexical and semantic change patterns in evolving life science ontologies to inform mapping adaptation [* Cross-Reference *]

However, from one ontology version to another, a further understanding of ontology changes relevant for supporting mapping adaptation is typically lacking.

Methods

This research work defines a set of change patterns at the level of concept attributes, and proposes original methods to automatically recognize instances of these patterns based on the similarity between attributes denoting the evolving concepts. This investigation evaluates the benefits of the proposed methods and the influence of the recognized change patterns to select the strategies for mapping adaptation. Results: The summary of the findings is as follows: (1) the Precision (>60%) and Recall (>35%) achieved by comparing manually identified change patterns with the automatic ones; (2) a set of potential impact of recognized change patterns on the way mappings is adapted.

We found that the detected correlations cover ~66% of the mapping adaptation actions with a positive impact; and (3) the influence of the similarity coefficient calculated between concept attributes on the performance of the recognition algorithms. Conclusions: The experimental evaluations conducted with real life science ontologies showed the effectiveness of our approach to accurately characterize ontology evolution at the level of concept attributes.

This investigation confirmed the relevance of the proposed change patterns to support decisions on mapping adaptation. ***This entry has been automatically imported via Infodoc(ASO) CSV by LIST harvest scripts. Please refer to doi.org/10.1016/j.artmed.2014.11.002 for the original and latest version of the dataset and data downloads***