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https://ceur-ws.org/Vol-2849/paper-23.pdf
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