10 citations · 16 across the 7 of their papers we have counts for
7 papers
Hakken: Predicting future discoveries to fill the gaps in today's knowledge
Tarek R. Besold, Uchenna Akujuobi, Pablo Sanchez +21
We present Hakken, a domain-agnostic prediction and explanation system performing knowledge prediction, i.e., growing scientific knowledge by establishing novel relationships, ones…
MEG: Medical Knowledge-Augmented Large Language Models for Question Answering
Laura Cabello, Carmen Martin-Turrero, Uchenna Akujuobi +2
Question answering is a natural language understanding task that involves reasoning over both explicit context, and unstated relevant domain knowledge. Despite the high cost of tra…
It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance
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…
CERM: Context-aware Literature-based Discovery via Sentiment Analysis
Julio Christian Young, Uchenna Akujuobi
Driven by the abundance of biomedical publications, we introduce a sentiment analysis task to understand food-health relationship. Prior attempts to incorporate health into recipe…
Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
Uchenna Akujuobi, Jun Chen, Mohamed Elhoseiny +2
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…
Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification
Uchenna Akujuobi, Han Yufei, Qiannan Zhang +1
In this work, we study semi-supervised multi-label node classification problem in attributed graphs. Classic solutions to multi-label node classification follow two steps, first le…
Recurrent Attention Walk for Semi-supervised Classification
Uchenna Akujuobi, Qiannan Zhang, Han Yufei +1
In this paper, we study the graph-based semi-supervised learning for classifying nodes in attributed networks, where the nodes and edges possess content information. Recent approac…