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Combining Neural Networks and Pattern Matching for Ontology Mining - a Meta Learning Inspired Approach | IEEE Conference Publication | IEEE Xplore

Combining Neural Networks and Pattern Matching for Ontology Mining - a Meta Learning Inspired Approach


Abstract:

Several applications dealing with natural language text involve automated validation of the membership in a given category (e.g. France is a country, Gladiator is a movie...Show More

Abstract:

Several applications dealing with natural language text involve automated validation of the membership in a given category (e.g. France is a country, Gladiator is a movie, but not a country). Meta-learning is a recent and powerful machine learning approach, which goal is to train a model (or a family of models) on a variety of learning tasks, such that it can solve new learning tasks in a more efficient way, e.g. using smaller number of training samples or in less time. We present an original approach inspired by meta-learning and consisting of two tiers of models: for any arbitrary category, our general model supplies high confidence training instances (seeds) for our category-specific models. Our general model is based on pattern matching and optimized for the precision at top N, while its recall is not important. Our category-specific models are based on recurrent neural networks (RNN -s), which recently showed themselves extremely effective in several natural language applications, such as machine translation, sentiment analysis, parsing, and chatbots. By following the meta-learning principles, we are training our highest level (general) model in such a way that our second - tier category -specific models (which are dependent on it) are optimized for the best possible performance in a specific application. This work is important because our approach is capable of verifying membership in an arbitrary category defined by a sequence of words including longer and more complex categories such as Ridley Scott movie or City in southern Germany that are currently not supported by existing manually created ontologies (such as Freebase, Wordnet or Wikidata). Also, our approach uses only raw text, and thus can be useful when there are no such ontologies available, which is a common situation with languages other than English. Even the largest English ontologies are known to have low coverage, insufficient for many practical applications such as automated question answering...
Date of Conference: 30 January 2019 - 01 February 2019
Date Added to IEEE Xplore: 14 March 2019
ISBN Information:
Print on Demand(PoD) ISSN: 2325-6516
Conference Location: Newport Beach, CA, USA

References

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