Uchenna Akujuobi
Profile
Uchenna Akujuobi is currently a research scientist with the SonyAI gastronomy team based in Tokyo. His research interests include network embedding, graph mining, information retrieval,text mining, and deep neural networks. Born in Nigeria, he obtained his BS degree in Saint Petersburg Electrotechnical University and his MS and PhD degree in the MINE Laboratory at the King Abdullah University of Science and Technology. He is motivated by the use of AI to augment human abilities for more creativity and improved performance. He believes in pushing the boundary between human possibilities and impossibilities one AI step at a time.
Publications
Literature-based Hypothesis Generation: Predicting the evolution of scientific literature to support scientists
AI4X, 2025 | Tarek R Besold, Uchenna Akujuobi, Samy Badreddine, Jihun Choi, Hatem ElShazly, Frederick Gifford, Kana Maruyama, Kae Nagano, Pablo Sanchez Martin, Thiviyan Thanapalasingam, Alessandra Toniato, Christoph Wehner
Science is advancing at an increasingly quick pace, as evidenced, for instance, by the exponential growth in the number of published research articles per year [1]. On the one hand, this poses anincreasingly pressing challenge: Effectively navigating this ever-growing body o...
Gastro-Health Project: Revolutionizing Personalized Nutrition and Health Forecasting Through Integrated AI Technologies
AI4X, 2025 | Uchenna Akujuobi, Jiu Yi, Maria Enrique Chung, Tarek Besold
Knowledge graphs are powerful tools for modelling complex, multi-relational data and supporting hypothesis generation, particularly in applications like drug repurposing. However, for predictive methods to gain acceptance as credible scientific tools, they must ensure not on...
Revisiting named entity recognition in food computing: enhancing performance and robustness
AIR, 2024 | Uchenna Akujuobi, Shuhong Liu*, Tarek R Besold
In the ever-evolving domain of food computing, named entity recognition (NER) presents transformative potential that extends far beyond mere word tagging in recipes. Its implications encompass intelligent recipe recommendations, health analysis, and personalization. Neverthe...
Link prediction for hypothesis generation: an active curriculum learning infused temporal graph-based approach
AIR, 2024 | Uchenna Akujuobi, Priyadarshini Kumari, Jihun Choi, Samy Badreddine, Kana Maruyama, Sucheendra K Palaniappan*, Tarek R Besold
Over the last few years Literature-based Discovery (LBD) has regained popularity as a means to enhance the scientific research process. The resurgent interest has spurred the development of supervised and semi-supervised machine learning models aimed at making previously imp...
It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance
ACL, 2024 | Laura Cabello*, Uchenna Akujuobi
Aspect-Based Sentiment Analysis (ABSA) involves extracting opinions from textual data about specific entities and their corresponding aspects through various complementary subtasks. Several prior research has focused on developing ad hoc designs of varying complexities for t...
CERM: Context-aware Literature-based Discovery via Sentiment Analysis
ECAI, 2023 | Julio Christian Young*, Uchenna Akujuobi
Motivated by the abundance of biomedical publications and the need to better understand the relationship between food and health, we study a new sentiment analysis task based on literature- based discovery. Many attempts have been made to introduce health into recipe recomme...
T50: T-PAIR: Temporal Node-pair Embedding for Automatic Biomedical Hypothesis Generation (Extended abstract)
ICDE, 2023 | Uchenna Akujuobi, Michael Spranger, Sucheendra Palaniappan*, Xiangliang Zhang*
In this paper, we study an automatic hypothesis generation (HG) problem, which refers to the discovery of meaningful implicit connections between scientific terms, including but not limited to diseases, chemicals, drugs, and genes extracted from databases of biomedical publi...
Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
NEURIPS, 2020 | Uchenna Akujuobi, Jun Chen*, Mohamed Elhoseiny*, Michael Spranger, Xiangliang Zhang*
Understanding the relationships between biomedical terms like viruses, drugs, and symptoms is essential in the fight against diseases. Many attempts have been made to introduce the use of machine learning to the scientific process of hypothesis generation (HG), which refers ...
T-PAIR: Temporal node-pair embedding for automatic biomedical hypothesis generation
IEEE TKDE, 2020 | Uchenna Akujuobi, Michael Spranger, Sucheendra K Palaniappan*, Xiangliang Zhang*
In this paper, we study an automatic hypothesis generation (HG) problem, which refers to the discovery of meaningfulimplicit connections between scientific terms, including but not limited to diseases, chemicals, drugs, and genes extracted fromdatabases of biomedical publica...
Blog Posts
Revolutionizing Hypothesis Generation
July 9, 2021 | Life at Sony AI, Uchenna Akujuobi
In part, scientific research is led by the hypothesis – the supposition or proposal that forms the basis for further investigation. Traditionally, ...