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Alignment of surface water ontologies: a comparison of manual and automated approaches

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Abstract

Studying the surface water systems of the earth is important for many fields, from biology to agriculture to tourism. Much of the data relevant to surface water systems are stored in isolated repositories that interface with different ontologies, such as the US Geological Survey’s Surface Water Ontology or the Environment Ontology (ENVO). Effectively using these data requires integrating the ontologies so that the data can be seamlessly queried and analyzed. Automated alignment algorithms exist to facilitate this data integration challenge. In this paper we examine the utility of two leading automated alignment systems to integrate four pairs of ontologies from the surface water domain. We show that the performance of such systems in this domain lags behind their results on popular benchmarks, and therefore incorporate the alignment task described here into the set of benchmarks used by the alignment community. We also show that with minor modifications, existing alignment algorithms can be used effectively within a semi-automated system for the surface water domain. In addition, we analyze the unique challenges of this domain with respect to data integration and discuss possible solutions to pursue in order to address these challenges.

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Notes

  1. http://oaei.ontologymatching.org.

  2. http://oaei.ontologymatching.org/2018/complex/index.html.

  3. http://oaei.ontologymatching.org/2018/.

  4. http://oaei.ontologymatching.org/2018/complex/index.html#hydrography.

  5. There was also a fourth data set from the plan taxonomy domain, but we could not include it in our analysis because the reference alignments are not public.

  6. This code is in the string matcher and the neighborhood matcher within AML.

  7. https://github.com/mcheatham/aml-comments.

  8. https://github.com/mcheatham/worldview.

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Acknowledgements

This work was funded by the United States Geological Survey under Agreement Number G16AC00120.

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Authors and Affiliations

  1. Wright State University, 3640 Colonel Glenn Hwy., Dayton, OH, 45435, USA

    Michelle Cheatham, Fatima Arauz & Lu Zhou

  2. U.S. Geological Survey, 1400 Independence Road, Rolla, MO, 65401, USA

    Dalia Varanka

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  1. Michelle Cheatham
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  2. Dalia Varanka
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  3. Fatima Arauz
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  4. Lu Zhou
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Correspondence to Michelle Cheatham.

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Cheatham, M., Varanka, D., Arauz, F. et al. Alignment of surface water ontologies: a comparison of manual and automated approaches. J Geogr Syst 22, 267–289 (2020). https://doi.org/10.1007/s10109-019-00312-3

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