<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Completion Reasoning Emulation for the Description Logic EL+</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Deep Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Description Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">EL+</style></keyword><keyword><style  face="normal" font="default" size="100%">LSTM</style></keyword><keyword><style  face="normal" font="default" size="100%">NeSy</style></keyword><keyword><style  face="normal" font="default" size="100%">Reasoning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-2600/paper5.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Stanford University, Palo Alto, California, USA</style></pub-location><volume><style face="normal" font="default" size="100%">2600</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Ferguson, Holly</style></author><author><style face="normal" font="default" size="100%">Charles, Vardeman</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Modification to the Hazardous Situation ODP to Support Risk Assessment and Mitigation</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop on Ontology Design Patterns (WOP)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">hazard</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontology Design Pattern</style></keyword><keyword><style  face="normal" font="default" size="100%">risk assessment</style></keyword><keyword><style  face="normal" font="default" size="100%">risk mitigation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2016</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Hazardous Situation ontology design pattern models the consequences of exposure of an object to a hazard. In its current form, the ODP is well suited for representing the consequences of exposure after the fact, which is very useful for applications such as damage assessment and recovery planning. In this work, we present a modification to this pattern that enables it to additionally support proactive questions central to risk assessment and mitigation planning.&lt;/p&gt;
</style></abstract></record></records></xml>