<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Daria Stepanova</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neuro-Symbolic AI and the Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></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>25</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">George T. Amariucai</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Abhishek Jana</style></author><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Secure Architecture for Biometric Authentication</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></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>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sulogna Chowdhury</style></author><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Memory Networks for RDFS Reasoning: Experiments</style></title><secondary-title><style face="normal" font="default" size="100%">SemREC-SMART 2022, Joint Proceedings of SemREC 2022 and SMART 2022 co-located with 21st International SemanticWeb Conference (ISWC 2022)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2022</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">CEUR Workshop Proceedings 3337, CEUR-WS.org 2023</style></publisher><pub-location><style face="normal" font="default" size="100%">Hangzhou, China</style></pub-location><pages><style face="normal" font="default" size="100%">pp. 28-32</style></pages><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%">Abhishek Jana</style></author><author><style face="normal" font="default" size="100%">Bipin Paudel</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">George T Amariucai</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neural Fuzzy Extractors: A Secure Way to Use Artificial Neural Networks for Biometric User Authentication</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of Privacy Enhancing Technologies </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><volume><style face="normal" font="default" size="100%">2022 (4)</style></volume><pages><style face="normal" font="default" size="100%">86-104</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">86</style></section></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%">Pascal Hitzler</style></author><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%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neuro-symbolic approaches in artificial intelligence</style></title><secondary-title><style face="normal" font="default" size="100%">National Science Review</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2022</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://academic.oup.com/nsr/article/9/6/nwac035/6542460</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">6</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>36</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</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%">On the Capabilities of Pointer Networks for Deep Deductive Reasoning</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><num-vols><style face="normal" font="default" size="100%">arXiv:2106.09225</style></num-vols></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%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Federico Bianchi</style></author><author><style face="normal" font="default" size="100%">Ning Xie</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Derek Doran</style></author><author><style face="normal" font="default" size="100%">HyeongSik Kim</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%">Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment</style></title><secondary-title><style face="normal" font="default" size="100%">AAAI-MAKE 2021</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><publisher><style face="normal" font="default" size="100%">AAAI</style></publisher><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%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Federico Bianchi</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 Bridging the Neuro-Symbolic Gap: Deep Deductive Reasoners</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></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>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards generalizable neuro-symbolic reasoners</style></title><secondary-title><style face="normal" font="default" size="100%">Department of Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://krex.k-state.edu/dspace/handle/2097/41621</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Kansas State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Manhattan, KS</style></pub-location><volume><style face="normal" font="default" size="100%">PhD</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Dissertation</style></work-type></record><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%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Federico Bianchi</style></author><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neural-Symbolic Integration and the Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.semantic-web-journal.net/content/neural-symbolic-integration-and-semantic-web-0</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Amir Hossein Yazdavar</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Challenges of Sentiment Analysis for Dynamic Events</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: sans-serif; font-size: 15px;&quot;&gt;Efforts to assess people's sentiments on Twitter have suggested that Twitter could be a valuable resource for studying political sentiment and that it reflects the offline political landscape. Many opinion mining systems and tools provide users with people's attitudes toward products, people, or topics and their attributes/aspects. However, although it may appear simple, using sentiment analysis to predict election results is difficult, since it is empirically challenging to train a successful model to conduct sentiment analysis on tweet streams for a dynamic event such as an election. This article highlights some of the challenges related to sentiment analysis encountered during monitoring of the presidential election using Kno.e.sis's Twitris system.&lt;/span&gt;&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%">Amir Hossein Yazdavar</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hussein S. Al-Olimat</style></author><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Goonmeet Bajaj</style></author><author><style face="normal" font="default" size="100%">Tanvi Banerjee</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author><author><style face="normal" font="default" size="100%">Jyotishman Pathak</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media</style></title><secondary-title><style face="normal" font="default" size="100%">ASONAM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://dl.acm.org/doi/abs/10.1145/3110025.3123028</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;&lt;span style=&quot;font-family: Merriweather, serif; font-size: 17px; background-color: rgb(250, 250, 250);&quot;&gt;With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.&lt;/span&gt;&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Amir HosseinYazdavar</style></author><author><style face="normal" font="default" size="100%">Naomie Salim</style></author><author><style face="normal" font="default" size="100%">Safaa Eltyeb</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Recognition of side effects as implicit-opinion words in drug reviews</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span&gt;Many opinion-mining systems and tools have been developed to provide users with the attitudes of people toward entities and their attributes or the overall polarities of documents. In addition, side effects are one of the critical measures used to evaluate a patient’s opinion for a particular drug. However, side effect recognition is a challenging task, since side effects coincide with disease symptoms lexically and syntactically. The purpose of this paper is to extract drug side effects from drug reviews as an integral implicit-opinion words.&lt;/span&gt;&lt;/p&gt;
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