Jiho Noh

My specializations are in


Dr. Jiho Noh's research interests include the following topics: ♣ Generative AI for Knowledge Representation, ♣ Neural representation learning for knowledge discovery, ♣ Text processing including data mining, information extraction, entity recognition and knowledge database construction, and ♣ Conversation techniques in information retrieval. If you are interested in discussing research topics with him, please contact me via email.

Affiliated Positions

Assistant Professor, Department of Computer Science, Kennesaw State University

  • Office: Atrium Building #J-341
  • Email: jnoh3@kennesaw.edu
  • Phone: 470-578-6355


Sep 26, 2023 – I am attending "Task Focused IR in the Era of Generative AI" at Microsoft Research, Redmond, WA, USA.


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Survey on Conversational Search and Applications in Biomedicine

A literature review on conversational search techniques for information retrieval and applications in the biomedical domains.

Graph Representation Learning for IR and Ontology Construction

Graph representation learning using various meta-paths and the applications in information retrieval and ontology construction tasks.


Joint Model for Entity Recognition and Normalization

Design a joint model utilizing neural network architecture for mention detection and entity linking within a biomedical ontology (i.e., UMLS concepts).

Misleading Information on Twitter Regarding Nicotine as Potential Prophylactic or Therapeutic for COVID-19

Predictive model using CBA algorithm (apriori association rules) for identifying tweets with misleading information, especially on the use of smoking (vaping) for COVID-19.

Identifying Special Interest Groups on Twitter and the Effects of Twitter's interventions

Data analytics on the perspectives of a special interest group (i.e., QAnon) towards the COVID related events.

Document Retrieval using Neural Text Summarization

Scholarly document retrieval for precision medicine using deep learning methods for document classification and text summarization.

BMET (BioMedical Entity Tagged) Embeddings

Learning joint word-concept embeddings from the biomedical entity annotated corpus. We use the MeSH (Medical Subject Headings) codes for representing biomedical entities.


Kratylos: Unified linguistic corpora from diverse data sources

developed a document management system that serves the (computational) linguistic researchers for managing and accessing the lexical and corpus datasets

(Fundedby Endangered Language Alliance (ELA) and University of Kentucky)


  • Tong, C., Jiho, N., Luke C., John M., & Junggab S. "Improving Fashion Attribute Clasification Accuracy with Limited Labeled Data using Transfer Learning." 2022 20th IEEE International Conference on Machine Learning and Applications.
  • Naga, K., Gowtham, G. & Jiho N. "A Survey on Conversational Search and Applications in Biomedicine." pre-print.
  • Noh, Jiho, and Ramakanth Kavuluru, "Joint Learning for Biomedical NER and Entity Normalization: Encoding Schemes, Counterfactual Examples, and Zero-Shot Evaluation." BCB '21: Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine. July 2021 pdf
  • Noh, Jiho, "Neural Representations of Concepts and Texts for Biomedical Information Retrieval" (2021). Theses and Dissertations--Computer Science. 102. pdf
  • Noh, Jiho, and Ramakanth Kavuluru, "Improving Static Biomedical Word Embeddings using Concept Relationship in the Corpus and Ontology." Journal of Biomedical Informatics. pdf
  • Noh, Jiho, and Ramakanth Kavuluru, Literature Retrieval for Precision Medicine using Neural Matching and Faceted Summarization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) 2020 Nov. pdf
  • Ramakanth Kavuluru, Jiho Noh, and Shyanika W. Rose, Twitter Discourse on Nicotine as Potential Prophylactic or Therapeutic for COVID-19. pdf
  • Noh, Jiho, and Ramakanth Kavuluru. "Document Retrieval for Biomedical Question Answering with Neural Sentence Matching." 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018.
  • Noh, Jiho, and Ramakanth Kavuluru. "Team UKNLP at TREC 2017 Precision Medicine Track: A Knowledge-Based IR System with Tuned Query-Time Boosting." TREC. 2017.


"One of the essential tenets of my teaching philosophy is the belief that education should be an ongoing process of inspiring students to recognize how the subjects they are learning are conntected to their abilities." -- from Jiho's statement of teaching philosophy

Information Retrieval

Information Retrieval is a course that focuses on the techniques and algorithms used to search and retrieve information from large collections of data. It covers topics such as indexing, query processing, relevance feedback, and text mining. The course also covers the use of search engines and other information retrieval systems.

[Fall21, Fall22, Fall23]

Machine Learning

Machine Learning is a course that focuses on the development of algorithms and techniques used to make predictions and decisions from data. It covers topics such as supervised and unsupervised learning, neural networks, deep learning, and reinforcement learning.

[Sp22, Sp23, Fall23]

Database Systems

Database Systems is a course that focuses on the design, implementation, and management of databases. It covers topics such as relational databases, query languages, data modeling, and database security.

[Sp22, Sm22]