01414nas a2200241 4500008004100000245006500041210006400106260007400170490000900244520070200253653001800955653002200973653000800995653000901003653000901012653001401021100002001035700002201055700001301077700001901090700002001109856004301129 2020 eng d00aCompletion Reasoning Emulation for the Description Logic EL+0 aCompletion Reasoning Emulation for the Description Logic EL aStanford University, Palo Alto, California, USAbCEUR-WS.orgc03/20200 v26003 a
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.
10aDeep Learning10aDescription Logic10aEL+10aLSTM10aNeSy10aReasoning1 aEberhart, Aaron1 aEbrahimi, Monireh1 aZhou, Lu1 aShimizu, Cogan1 aHitzler, Pascal uhttp://ceur-ws.org/Vol-2600/paper5.pdf00883nas a2200229 4500008004100000020002200041245004000063210003900103260003900142300001200181490001000193520026000203653000800463653002200471653000700493653002200500653001900522100002000541700002300561700002000584856004900604 2018 eng d a978-3-319-98191-800aPseudo-Random ALC Syntax Generation0 aPseudoRandom ALC Syntax Generation aHeraklion, Crete, GreecebSpringer a19–220 v111553 aWe discuss a tool capable of rapidly generating pseudo-random syntactically valid ALC expression trees. The program is meant to allow a researcher to create large sets of independently valid expressions with a minimum of personal bias for experimentation.10aALC10aDescription Logic10aDL10arandom generation10asynthetic data1 aEberhart, Aaron1 aCheatham, Michelle1 aHitzler, Pascal uhttps://doi.org/10.1007/978-3-319-98192-5\_4