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- W4297790369 abstract "We present a new approach to integrating deep learning with knowledge-based systems that we believe shows promise. Our approach seeks to emulate reasoning structure, which can be inspected part-way through, rather than simply learning reasoner answers, which is typical in many of the black-box systems currently in use. We demonstrate that this idea is feasible by training a long short-term memory (LSTM) artificial neural network to learn EL+ reasoning patterns with two different data sets. We also show that this trained system is resistant to noise by corrupting a percentage of the test data and comparing the reasoner's and LSTM's predictions on corrupt data with correct answers." @default.
- W4297790369 created "2022-10-01" @default.
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- W4297790369 date "2019-12-10" @default.
- W4297790369 modified "2023-09-24" @default.
- W4297790369 title "Completion Reasoning Emulation for the Description Logic EL+" @default.
- W4297790369 doi "https://doi.org/10.48550/arxiv.1912.05063" @default.
- W4297790369 hasPublicationYear "2019" @default.
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