The Pistoia Alliance was established ten years ago to promote innovation by industry through pre-competitive collaboration to reduce the barriers to innovation. The Ontologies Mapping Project started in 2016 to enable better tools and services for ontology mapping and to define best practices for ontology management in the Life Sciences [1]. The interest in ontologies is growing within the pharmaceutical domain. Data is a very valuable corporate asset to enable digital transformation and lead to innovative biological insight. However, data integration is fundamental piece in the puzzle where ontologies and ontology matching may play an important role. The Pistoia Alliance Ontologies Mapping Project has covered two domains of interest: (i) phenotype and disease [2], and (ii) laboratory analytics domain. In this paper we focus on the later, for which alignment sets are not that common, we introduce the system Paxo, and we compare its results against participants of the Ontology Alignment Evaluation Initiative (OAEI, http://oaei.ontologymatching.org/). Datasets. We selected, in conjunction with (pharmaceutical) industry partners of the Pistoia Alliance, 9 relevant ontologies to the laboratory analytics domain and 13 ontology pairs to compute their alignment. Table 1 shows the ontologies that were selected for their relevance to the laboratory analytics domain. Note that there is not a public hand-curated gold standard alignment among the selected ontology pairs. Paxo system. Paxo is a lightweight ontology mapping approach. Unlike other algorithms, Paxo does not need to store, load or index ontologies. Instead Paxo accesses the API of the Ontology Lookup Service (OLS, https://www.ebi.ac.uk/ols/index) and the Ontology Mapping Repository (OxO, https://www.ebi.ac.uk/spot/oxo/) at EMBL-EBI to explore ontologies. Through OLS, Paxo can perform search via preferred label and synonyms, while OxO offers access to a wide range of known ontology mappings, that were defined, for example, as cross references within the ontologies themselves or in the UMLS Metathesaurus.
Introduction ? Copyright c 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). 1 0:6
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1 Fig. 1: Two-dimensional representation of the Jaccard distances among EFOMESH mappings. Plots computed with the MELT framework (https: //github.com/dwslab/melt).