Ontology Mapping for the Laboratory Analytics Domain Ian Harrow1, Thomas Liener1, and Ernesto Jimenez-Ruiz2,3. 1 Ontologies Mapping Project, Pistoia Alliance, USA 2City, University of London, UK and 3 SIRIUS, University of Oslo, Norway. 1. Introduction The Pistoia Alliance was established ten years ago to promote innovation by industry Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). through pre-competitive collaboration to reduce the barriers to innovation. The Ontologies Mapping Project [1] was established in 2016 to enable better tools and services for mapping between ontologies and to establish best practices for ontology management in the Life Sciences. 2. Extendibility of the Ontology Mapping algorithm We have reported already on the development of the algorithm, Paxo for mapping between public ontologies hosted by the Ontology Lookup Service (OLS) and the Ontology Mapping Repository (OxO) at EMBL-EBI [2, 3]. Paxo was used previously to map between public ontologies in the phenotype and disease domain, while here we report on mapping in the laboratory analytics domain. 3. Selected public Ontologies for Mapping Eleven public ontologies were selected from the laboratory analytics domain for mapping with Paxo as listed below: 4. Perceived value of Ontology Mappings Each ontology was scored for perceived value (PV) by the 9 members of the project team, from numerous pharmaceutical and biotechnology companies. Each ontology was assigned a score of 3 for high PV, 2 for medium PV and 1 for low PV and 0 for no PV by each of the 9 team members. This gave the total PV score (a simple summation of scores) for each of the 54 mappings predicted by Paxo, which informed our priorities for evaluation: 5. Evaluation of selected Ontology Mapping sets Thirteen mappings with high total PV scores and unique matches were selected for evaluation of recall and precision: The parameters of Paxo were selected to balance recall (matches missing from the LOOM baseline standard) and precision (correct matches from random sampling from unique matches where n=60). Recall ranged from 66% to 97% while precision for unique matches ranged from 45% to 95% for each mapping. These predicted mapping sets will be made accessible openly via the project web page [4]. 6. Summary and Future Plans Fifty-four ontology mappings were predicted using the Paxo algorithm which demonstrates how it can be applied to any pair of ontologies hosted by OLS and OxO at EMBL-EBI, within a single domain where overlap of class concepts is likely to be found. As no hand-curated gold standard mappings exist to measure recall, in the near future we will use a panel of numerous algorithms to generate a set of silver standard mappings from a minimum of three consensus votes as we have published previously [6]. The panel of algorithms are participants in the annual challenge for Ontology Alignment Evaluation Initiative (OAEI) [5, 6] which included the top performing LogMap [7] and AML [8], in addition to the purely lexical algorithm, LOOM [9] which served as a baseline standard [6]. Future work may include crowd validation of predicted mappings and further mapping between ontologies in the clinical domain. Acknowledgements We would like to express gratitude to all the Pistoia Alliance Ontology Mapping project team members and their parent organisations who contributed expertise, time and funding. EJR was supported by the AIDA project, funded by the Alan Turing Institute, and the SIRIUS Centre for Scalable Data Access (Research Council of Norway, project no.: 237889). References 1. http://www.pistoiaalliance.org/projects/ontologies-mapping 2. https://doi.org/10.6084/m9.figshare.7346057.v1 3. https://www.ebi.ac.uk/spot/ontology 4. https://www.pistoiaalliance.org/projects/current-projects/ontologies-mapping 5. http://oaei.ontologymatching.org 6. Harrow I et al (2017) Matching disease and phenotype ontologies in the ontology alignment evaluation initiative. J Biomed Semantics. 8(1), 55 7. Jimenez-Ruiz E and Cuenca Grau B (2011) LogMap: Logic-based and Scalable Ontology Matching. International Semantic Web Conference (1) 2011, 273-288 8. Faria D et al (2018) Tackling the challenges of matching biomedical ontologies. J Biomed Semantics 9(1), 4 9. Ghazvinian A et al (2009). Creating mappings for ontologies in biomedicine: simple methods work. AMIA. Annual Symposium (AMIA 2009) San Francisco, CA