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Automatic ontology generation from patents using a pre-built library, WordNet and a class-based n-gram model

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  • Zhen Li
  • Derrick Tate

Abstract

An ontology is defined as a structured, hierarchical way for describing domain knowledge. Research work regarding ontological engineering has yielded fruitful results, but these methods share a common drawback: they require significant manual work to generate an ontology, which limits the usefulness of these approaches in practice. In this paper, we propose a computational model that combines data mining, Natural Language Processing (NLP), WordNet and a novel class-based n-gram model for automatic ontology discovery and recognition from existing patent documents. A pre-built ontology library was constructed by gathering knowledge from engineering textbooks and dictionaries. Then a data set of engineering patent claims was split into training (80%) and validation (20%) subsets. The pre-built library and WordNet were used to generate class labels for constructing class-based n-gram models in a training process. The holdout validation showed that the average accuracy was 87.26% for all validation samples.

Suggested Citation

  • Zhen Li & Derrick Tate, 2015. "Automatic ontology generation from patents using a pre-built library, WordNet and a class-based n-gram model," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 20(2), pages 142-172.
    • Handle: RePEc:ids:ijpdev:v:20:y:2015:i:2:p:142-172
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