@article {987, title = {Neuro-Symbolic AI and the Semantic Web}, journal = {Semantic Web Journal}, year = {In Press}, author = {Pascal Hitzler and Monireh Ebrahimi and Md Kamruzzaman Sarker and Daria Stepanova} } @article {965, title = {Secure Architecture for Biometric Authentication}, year = {2023}, author = {George T. Amariucai and Pascal Hitzler and Abhishek Jana and Monireh Ebrahimi and Md Kamruzzaman Sarker} } @proceedings {960, title = {Memory Networks for RDFS Reasoning: Experiments}, journal = {SemREC-SMART 2022, Joint Proceedings of SemREC 2022 and SMART 2022 co-located with 21st International SemanticWeb Conference (ISWC 2022)}, year = {2022}, month = {10/2022}, pages = {pp. 28-32}, publisher = {CEUR Workshop Proceedings 3337, CEUR-WS.org 2023}, address = {Hangzhou, China}, author = {Sulogna Chowdhury and Monireh Ebrahimi and Pascal Hitzler and Aaron Eberhart} } @article {946, title = {Neural Fuzzy Extractors: A Secure Way to Use Artificial Neural Networks for Biometric User Authentication}, journal = {Proceedings of Privacy Enhancing Technologies }, volume = {2022 (4)}, year = {2022}, pages = {86-104}, chapter = {86}, author = {Abhishek Jana and Bipin Paudel and Md Kamruzzaman Sarker and Monireh Ebrahimi and Pascal Hitzler and George T Amariucai} } @article {897, title = {Neuro-symbolic approaches in artificial intelligence}, journal = {National Science Review}, volume = {9}, year = {2022}, month = {06/2022}, doi = {https://doi.org/10.1093/nsr/nwac035}, url = {https://academic.oup.com/nsr/article/9/6/nwac035/6542460}, author = {Pascal Hitzler and Aaron Eberhart and Monireh Ebrahimi and Md Kamruzzaman Sarker and Lu Zhou} } @article {865, title = {On the Capabilities of Pointer Networks for Deep Deductive Reasoning}, journal = {arXiv}, year = {2021}, author = {Monireh Ebrahimi and Aaron Eberhart and Pascal Hitzler} } @conference {856, title = {Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment}, booktitle = {AAAI-MAKE 2021}, year = {2021}, publisher = {AAAI}, organization = {AAAI}, author = {Monireh Ebrahimi and Md Kamruzzaman Sarker and Federico Bianchi and Ning Xie and Aaron Eberhart and Derek Doran and HyeongSik Kim and Pascal Hitzler} } @article {855, title = {Towards Bridging the Neuro-Symbolic Gap: Deep Deductive Reasoners}, journal = {Applied Intelligence}, year = {2021}, author = {Monireh Ebrahimi and Aaron Eberhart and Federico Bianchi and Pascal Hitzler} } @mastersthesis {878, title = {Towards generalizable neuro-symbolic reasoners}, volume = {PhD}, year = {2021}, month = {08/2021}, school = {Kansas State University}, type = {Dissertation}, address = {Manhattan, KS}, url = {https://krex.k-state.edu/dspace/handle/2097/41621}, author = {Monireh Ebrahimi} } @proceedings {770, title = {Completion Reasoning Emulation for the Description Logic EL+}, journal = {Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice}, volume = {2600}, year = {2020}, month = {03/2020}, publisher = {CEUR-WS.org}, address = {Stanford University, Palo Alto, California, USA}, abstract = {

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{\textquoteright}s and LSTM{\textquoteright}s predictions on corrupt data with correct answers.

}, keywords = {Deep Learning, Description Logic, EL+, LSTM, NeSy, Reasoning}, url = {http://ceur-ws.org/Vol-2600/paper5.pdf}, author = {Aaron Eberhart and Monireh Ebrahimi and Lu Zhou and Cogan Shimizu and Pascal Hitzler} } @article {790, title = {Neural-Symbolic Integration and the Semantic Web}, journal = {Semantic Web}, volume = {11}, year = {2020}, url = {http://www.semantic-web-journal.net/content/neural-symbolic-integration-and-semantic-web-0}, author = {Pascal Hitzler and Federico Bianchi and Monireh Ebrahimi and Md Kamruzzaman Sarker} } @article {779, title = {Challenges of Sentiment Analysis for Dynamic Events}, year = {2017}, abstract = {

Efforts to assess people{\textquoteright}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{\textquoteright}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{\textquoteright}s Twitris system.

}, author = {Monireh Ebrahimi and Amir Hossein Yazdavar and Amit Sheth} } @conference {776, title = {Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media}, booktitle = {ASONAM}, year = {2017}, abstract = {

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\%.

}, url = {https://dl.acm.org/doi/abs/10.1145/3110025.3123028}, author = {Amir Hossein Yazdavar}, editor = {Hussein S. Al-Olimat and Monireh Ebrahimi and Goonmeet Bajaj and Tanvi Banerjee and Krishnaprasad Thirunarayan and Jyotishman Pathak and Amit Sheth} } @article {780, title = {Recognition of side effects as implicit-opinion words in drug reviews}, year = {2016}, abstract = {

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{\textquoteright}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.

}, author = {Monireh Ebrahimi and Amir HosseinYazdavar and Naomie Salim and Safaa Eltyeb} }