%0 Conference Proceedings %B Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice %D 2020 %T Completion Reasoning Emulation for the Description Logic EL+ %A Aaron Eberhart %A Monireh Ebrahimi %A Lu Zhou %A Cogan Shimizu %A Pascal Hitzler %K Deep Learning %K Description Logic %K EL+ %K LSTM %K NeSy %K Reasoning %X

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.

%B Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice %I CEUR-WS.org %C Stanford University, Palo Alto, California, USA %V 2600 %8 03/2020 %G eng %U http://ceur-ws.org/Vol-2600/paper5.pdf %0 Thesis %B Computer Science and Engineering %D 2017 %T Efficient Reasoning Algorithms for Fragments of Horn Description Logics %A David Carral %K Description Logic %K Knowledge representation %K Reasoning %X We characterize two fragments of Horn Description Logics and we define two specialized reasoning algorithms that effectively solve the standard reasoning tasks over each of such fragments. We believe our work to be of general interest since (1) a rather large proportion of real-world Horn ontologies belong to some of these two fragments and (2) the implementations based on our reasoning approach significantly outperform state-of-the-art reasoners. Claims (1) and (2) are extensively proven via empirically evaluation. %B Computer Science and Engineering %I Wright State University %C Dayton %V Doctor of Philosophy (PhD) %P 70 %G eng %U http://rave.ohiolink.edu/etdc/view?acc_num=wright1491317096530938