Applied Knowledge Representation and Natural Language Understanding Lab

AKRaNLU is a research team of students and faculty interested in working on Natural Language Understanding problems. The group enhances technologies that benefit from full language understanding by combining the meaning extracted from Natural Language texts with the knowledge of the world, represented in conceptual forms (knowledge respresentation and reasoning).


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Dr. Julia Rayz


Dr. Victor Raskin

Faculty Member

Dr. C. F. Hempelmann

Faculty Member


Xiaonan Jing

Ph.D Candidate

Shih Feng Yang

Ph.D Candidate

Tatiana Ringenberg

Ph.D Candidate

Saltanat Tazhibayeva

Ph.D Candidate

Kanishka Misra

Ph.D Student

Geetanjali Bihani

Ph.D Student

Yifei Hu

Ph.D Student

Rey A. Gonzalez

Ph.D Student

And More...

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Gilchan Park

M.S., Ph.D

Chien-Yi Hsiang


Hemanth Devarapalli


Qiaofei Ye


Parag Guruji


And More...

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Recent News
2021.07 Congratulations to Tatiana on a successful PhD defense!
2021.05 Congratulations to Tatiana on an acceptence to Findings of ACL!
2021.04 Congratulations to Tatiana on NAFIPS Outstanding Student Paper award (2nd place) and to Kanishka on the honorable mention (3rd place)
2021.04 Congratulations to Geetanjali on a DeepLIO workshop paper acceptance at NAACL!
2021.04 Congratulations to Kanishka on CogSci Full Paper with Talk acceptance!
2021.04 Congratulations to Julia on being promoted to Full Professor! -"Faculty promotions at Purdue approved by board"
2021.03 Congratulations to Shannon, Carol, Yi, Tatiana, Kanishka, and Geetanjali on their papers being accepted to NAFIPS'21
2021.03 Congratulations to Yifei, Shannon, Carol, Yi, and BK on their papers being accepted to FLAIRS'34
2021.01 Congratulations to Soumya on her new job at Siemens!
2020.12 Congratulations to Shih-Feng on his PhD proposal defense
2020.11 Congratulations to Yifei on a successful MS defense!
2020.11 Congratulations to Shannon on a paper acceptance to Data Analytics on Social Media workshop of WI'20
2020.11 Congratulations to Kanishka on a successful MS defense!
2020.10 Congratulations to Geetanjali on a paper acceptance to WI'20!
2020.10 Congratulations to Kanishka on an accepted presentation to BlackboxNLP 2020!
2020.08 Congratulations to Kanishka on an acceptance of his paper to Findings of EMNLP!
2020.08 Congrats to Carol, Yi, Kanishka, Yifei, and Shannon on their papers being accepted to ICCI*CC’20
2020.07 Congrats to Tatiana on the best student paper award at NAFIPS'20
2020.06 Congrats to Soumya on her paper acceptance to ICCM2020
2020.05 Congrats to Kanishka and Tatiana on acceptance of their papers to NAFIPS’2020!
2020.04 Congrats to Carol, Yi, and their mentor Kanishka on second place at the PURC 2020! Here's the video
2020.04 Congrats to Geetanjali on a successsfull MS defense!
2020.04 Congrats to Soumya on a successsfull MS defense!
2019.12 Congrats to Tatiana on passing her thesis proposal defense!
2019.08 Congrats to Xiaonan "Shannon" Jing on passing her prelims!
2019.06 Julia and Victor recieved Outstanding Paper Award at IFSA-NAFIPS 2019
2019.06 Julia is elected to the Board of NAFIPS
2019.06 Congratulations to Qiaofei, Kanishka, Tatiana, and Hemanth on their papers being accepted to IEEE-SMC 2019
2019.05 Congratulations to Kanishka and Hemanth on their work being accepted to ICCM'2019 and CogSci 2019
2019.04 Congratulations to Shih-Feng on passing his prelims!
2019.04 Congratulations to Tatiana on passing her prelims!
2018.12 Congratulations to Saltanat on passing her prelims
2018.10 Congrats to John on being selected 1 of 4 Purdue scholars for NSF Center for Science of Information Channels Undergraduate Research Program!
2018.06 Congratulations to Cheryl and Gilchan on successful dissertation deposits!
2018.04 Congratulations to Kanishka on receiving a PRF fellowship!
Recent Publications
Ontological Detection of Phishing Emails

Gilchan Park, Julia Rayz

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Authorship Analysis of Online Predatory Conversations using Character Level Convolution Neural Networks.

Kanishka Misra, Hemanth Devarapalli, Tatiana Ringenberg, Julia Taylor Rayz

2019 IEEE International Conference on Systems, Man, and Cybernetics

Not So Cute but Fuzzy: Estimating Risk of Sexual Predation in Online Conversations.

Tatiana Ringenberg, Kanishka Misra, Julia Taylor Rayz

2019 IEEE International Conference on Systems, Man, and Cybernetics

Exploring Automatic Identification of Fantasy-Driven and Contact-Driven Sexual Solicitors

Tatiana Ringenberg, Kanishka Misra, Kathryn C. Seigfried-Spellar, Julia Taylor Rayz

2019 Third IEEE International Conference on Robotic Computing

A Sentiment Based Non-Factoid Question-Answering Framework

Qiaofei Ye, Kanishka Misra, Hemanth Devarapalli, Julia Taylor Rayz

2019 IEEE International Conference on Systems, Man, and Cybernetics

Measuring the Influence of L1 on Learner English Errors in Content Words within Word Embedding Models.

Kanishka Misra, Hemanth Devarapalli, Julia Taylor Rayz

17th International Conference on Cognitive Modelling, 2019

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Ongoing Projects
EAGER: SaTC-EDU: AI-based Humor-Integrated Social Engineering Training

Advances in artificial intelligence (AI) have introduced new opportunities and challenges in cybersecurity. Social engineering, while contributing to the majority of cyberattacks, poses a uniquely difficult problem in cybersecurity because of a combination of factors. First, social engineering is low cost and involves multiple increasingly complex and subtle attack models. Second, the majority of computer users are not cybersecurity-literate, with less than 30% judged competent on basic knowledge. Third, social engineering takes advantage of human vulnerabilities such as habit formation and susceptibility to persuasive techniques. This all results in a significant gap in security because individuals are unprepared to counteract social engineering. To address the need to educate casual computer users against social engineering attacks, this project proposes a novel approach that will take advantage of human psychology, just like the attacks themselves do. The project team proposes to create an accessible and engaging learning experience that will promote changes in attitude and behavior in computer users by teaching them about social engineering techniques and how to detect them. This project fills an important gap by focusing on users normally marginalized by current cybersecurity education efforts, including casual computer users or those with computer anxiety, such as the elderly and low-income families.
To address the dual problems of a lack of cybersecurity literacy and increasing social engineering attacks, the multidisciplinary project team proposes to integrate AI techniques to create a customized social engineering education experience that utilizes the principles of entertainment education. This effort will target non-security professionals and will use pretext design maps to train AI systems to generate social engineering scenarios. Transformer-based natural language processing models and humor theory knowledge will be used to generate explainable humorous training schemas based on these social engineering scenarios. The scenarios will then be applied in a classroom setting, where learning patterns and specific psychological markers will be used to refine the AI-generated scenarios. The combination of these approaches will result in an effective cybersecurity pedagogical tool, powered by AI, for casual computer users.
This project is supported by a special initiative of the Secure and Trustworthy Cyberspace (SaTC) program to foster new, previously unexplored, collaborations between the fields of cybersecurity, artificial intelligence, and education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Ontological Semantic Technology

Ontological Semantic Technology (OST) is a set of resources that enable computational understand of natural language. The resources consist of language independent ontology (representing general knowledge of the world), a number of language-dependent lexica (one per supported natural language, such as English, Russian, Korean, etc.) and a set of tools that interpret text into text-meaning representation (TMR), results of which are stored in an information base. The ultimate goal of OST is to understand explicit and implicit information, knowledge of which is compatible with the ontology.

Communication about Energy Usage in Smart Homes
Part of Sociotechnical systems to enable smart and connected energy-aware residential communities

The overall goal of the project is to discover new knowledge on how individuals, groups, and residential communities make decisions related to their home energy consumption through some feedback mechanism. This feedback mechanism can visual or verbal. The goal of the verbal communication in this project is to give users information about their energy usage informally, in a way that is most acceptable to them. The grain size of information (both in terms of numbers, energy devices, and user behavior) to be delivered to the user as well as the acceptability of verbal cues, based on the density level of text, different voices, effects, and grammatical constructions is part of the experiments performed. The communication of individual cues as well as prolonged dialogs is currently accomplished through Amazon Alexa.

Chat Analysis for Law Enforcement
In collaboration with High Tech Crime Unit

The overall goal of the project is to provide law enforcement with a faster and reliable way to triage suspicious conversations between minors and adults in order to prioritize existing cases based on their risk level. AKRaNLU’s component of the data consists of anonymized chats, mostly from social media platforms, each of which includes two or more participants. In order to detect risk of conversations, themes of various parts of chats are considered as well as style of the conversations that may point out to the groups of individuals working together or the same individual using various aliases and multiple social media platforms

Detection of Information Inconsistency

The aim of the project is to develop framework and preliminary results to show feasibility of inconsistency detection. The goal is to taxonomize various inconsistency types that can be seen in text, and identify existing methods that are suitable for identifying each type, as well as outline and develop new methods that would improve the performance of the overall system.

Computational Humor

Advancements in Artificial Intelligence allow computational agents to become more ‘natural’ in various communication aspects. Humor is one of the components of human interaction that should be accounted for, if computational agents are to communicate in a way that is similar to humans. The goal of the project is to be able to detect (and be able to explain) when a component of a text contains humor, when an utterance contains humor potential that can be highlighted with a computer-generated punchline, and to understand humor preferences of a particular individual based on his/her interaction with a computational system, and respond appropriately to a user. Existing research from multiple disciplines that contribute to humor studies is considered, as well as natural language processing techniques.