Leveraging Ontologies to Improve Model Generalization Automatically with Online Data Sources


  • Sasin Janpuangtong Texas A&M University
  • Dylan A. Shell Texas A&M University




This paper describes an end-to-end learning framework that allows a novice to create a model from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. Prudence must be exercised to avoid these hazards: certain conclusions may be supported by extra knowledge if, for example, there are reasons to trust a particular narrower set of hypotheses. This paper adopts the solution of using higher-level knowledge in order to allow this sort of domain knowledge to be inferred automatically, thereby selecting only relevant input attributes and thence constraining the hypothesis space. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. To validate our approach, models of four different problem domains were built using our implementation of the framework. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.




How to Cite

Janpuangtong, S., & Shell, D. (2015). Leveraging Ontologies to Improve Model Generalization Automatically with Online Data Sources. Proceedings of the AAAI Conference on Artificial Intelligence, 29(2), 3981-3986. https://doi.org/10.1609/aaai.v29i2.19058