A Data-Driven Approach for Detecting and Quantifying Modeling Biases in Geo-Ontologies by Using a Discrepancy Index

Thursday Sep 29   11:10 AM to 11:30 AM (20 minutes)
geo-ontologyontology-engineeringDbpediaLinked DataDiscrepancy Index
Geo-ontologies play an important role in fostering the publication, retrieval, reuse, and integration of geographic data within and across domains. The status quo of geo-ontology engineering often follows a centralized top-down approach, namely a group of domain experts collaboratively formalizing key concepts and their relationships. On the one hand such an approach makes use of the invaluable knowledge and experience of subject matter experts and captures their perception of the world. On the other hand it can introduce biases and ontological commitments that do not well correspond to the data that will be semantically lifted using these ontologies. In this work, we propose a data-driven method to calculate a discrepancy Index in order to identify and quantify the potential modeling biases in current geo-ontologies. In other words, instead of trying to measure quality, we determine how much the ontology differs from what would be expected when looking at the data alone.
University of California, Santa Barbara
University of Tennessee, Knoxville
Assistant Professor

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