<?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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amir Hossein Yazdavar</style></author><author><style face="normal" font="default" size="100%">Mohammad Saeid Mahdavinejad</style></author><author><style face="normal" font="default" size="100%">Goonmeet Baja</style></author><author><style face="normal" font="default" size="100%">William Romine</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Amir Hassan Monadjemi</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author><author><style face="normal" font="default" size="100%">John M. Meddar</style></author><author><style face="normal" font="default" size="100%">Annie Myers</style></author><author><style face="normal" font="default" size="100%">Jyotishman Pathak</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%">Multimodal mental health analysis in social media</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS ONE</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Explainable Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Hypothesis Testing</style></keyword><keyword><style  face="normal" font="default" size="100%">National Language Processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">Regression</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248&amp;type=printable</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.5px Helvetica}&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;Depression is a major public health concern in the U.S. and globally. While successful early&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;identification and treatment can lead to many positive health and behavioral outcomes,&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;depression, remains undiagnosed, untreated or undertreated due to several reasons,&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;including denial of the illness as well as cultural and social stigma. With the ubiquity of social&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;media platforms, millions of people are now sharing their online persona by expressing their&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;thoughts, moods, emotions, and even their daily struggles with mental health on social&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;media. Unlike traditional observational cohort studies conducted through questionnaires&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;and self-reported surveys, we explore the reliable detection of depressive symptoms from&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social)&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;data to discern depressive behaviors using a wide variety of features including individuallevel&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;demographics. By developing a multimodal framework and employing statistical techniques&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;to fuse heterogeneous sets of features obtained through the processing of visual,&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;textual, and user interaction data, we significantly enhance the current state-of-the-art&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;approaches for identifying depressed individuals on Twitter (improving the average F1-&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;Score by 5 percent) as well as facilitate demographic inferences from social media. Besides&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;providing insights into the relationship between demographics and mental health, our&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;research assists in the design of a new breed of demographic-aware health interventions.&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%">Maryam Labaf</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></secondary-authors><tertiary-authors><author><style face="normal" font="default" size="100%">Anthony B. Evans</style></author></tertiary-authors></contributors><titles><title><style face="normal" font="default" size="100%"> Propositional rule extraction from neural networks under background knowledge</style></title><secondary-title><style face="normal" font="default" size="100%">Twelfth International Workshop on Neural-Symbolic Learning and Reasoning</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Background knowledge</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural Network</style></keyword><keyword><style  face="normal" font="default" size="100%">Propositional Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">Rule Extraction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2017</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shasha Huang</style></author><author><style face="normal" font="default" size="100%">Qingguo Li</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%">Reasoning with Inconsistencies in Hybrid MKNF Knowledge Bases</style></title><secondary-title><style face="normal" font="default" size="100%">Logic Journal of the IGPL</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Data complexity</style></keyword><keyword><style  face="normal" font="default" size="100%">Description logics and rules</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge representation</style></keyword><keyword><style  face="normal" font="default" size="100%">Non-monotonic reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Paraconsistent reasoning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1093/jigpal/jzs043</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">263–290</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper is concerned with the handling of inconsistencies occurring in the combination of description logics and rules, especially in hybrid MKNF knowledge bases. More precisely, we present a paraconsistent semantics for hybrid MKNF knowledge bases (called para-MKNF knowledge bases) based on four-valued logic as proposed by Belnap. We also reduce this paraconsistent semantics to the stable model semantics via a linear transformation operator, which shows the relationship between the two semantics and indicates that the data complexity in our paradigm is not higher than that of classical reasoning. Moreover, we provide fixpoint operators to compute paraconsistent MKNF models, each suitable to different kinds of rules. At last we present the data complexity of instance checking in different paraMKNF knowledge bases.</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%">David Carral</style></author><author><style face="normal" font="default" size="100%">Cong Wang</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%">Towards an Efficient Algorithm to Reason over Description Logics Extended with Nominal Schemas</style></title><secondary-title><style face="normal" font="default" size="100%">Web Reasoning and Rule Systems - 7th International Conference, {RR} 2013, Mannheim, Germany, July 27-29, 2013. Proceedings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">EL++</style></keyword><keyword><style  face="normal" font="default" size="100%">Nominal Schemas</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-39666-3_6</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">65–79</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Extending description logics with so-called nominal schemas has been shown to be a major step towards integrating description logics with rules paradigms. However, establishing efficient algorithms for reasoning with nominal schemas has so far been a challenge. In this paper, we present an algorithm to reason with the description logic fragment ELROVn, a fragment that extends EL++ with nominal schemas. We also report on an implementation and experimental evaluation of the algorithm, which shows that our approach is indeed rather efficient.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Matthias Knorr</style></author><author><style face="normal" font="default" size="100%">José Júlio Alferes</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%">Local Closed World Reasoning with Description Logics under the Well-Founded Semantics</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Description Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge representation</style></keyword><keyword><style  face="normal" font="default" size="100%">Logic Programming</style></keyword><keyword><style  face="normal" font="default" size="100%">Non-monotonic reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontologies</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1016/j.artint.2011.01.007</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">175</style></volume><pages><style face="normal" font="default" size="100%">1528–1554</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases [52], and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">9-10</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Barbara Hammer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Wolfgang Maass</style></author><author><style face="normal" font="default" size="100%">Marc Toussaint</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">10302 Abstracts Collection - Learning paradigms in dynamic environments</style></title><secondary-title><style face="normal" font="default" size="100%">Learning paradigms in dynamic environments</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Autonomous learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural-symbolic integration</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurobiology</style></keyword><keyword><style  face="normal" font="default" size="100%">Recurrent neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Speech processing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://drops.dagstuhl.de/opus/volltexte/2010/2804</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany</style></publisher><pub-location><style face="normal" font="default" size="100%">Dagstuhl, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>