32 citations · 39 across the 13 of their papers we have counts for
13 papers
Position: Avoid Overstretching LLMs for every Enterprise Task
Kuldeep Singh, Anson Bastos, Isaiah Onando Mulang'
Enterprise workloads are dominated by deterministic, structured, and knowledge-dependent tasks operating under strict cost, latency, and reliability constraints. While these are of…
SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables
Isaiah Onando Mulang, Felix Sasaki, Tassilo Klein +3
Building upon the SALT benchmark for relational prediction (Klein et al., 2024), we introduce SALT-KG, a benchmark for semantics-aware learning on enterprise tables. SALT-KG extend…
Sparsity-based Feature Selection for Anomalous Subgroup Discovery
Girmaw Abebe Tadesse, William Ogallo, Catherine Wanjiru +5
Anomalous pattern detection aims to identify instances where deviation from normalcy is evident, and is widely applicable across domains. Multiple anomalous detection techniques ha…
How Expressive are Transformers in Spectral Domain for Graphs?
Anson Bastos, Abhishek Nadgeri, Kuldeep Singh +3
The recent works proposing transformer-based models for graphs have proven the inadequacy of Vanilla Transformer for graph representation learning. To understand this inadequacy, t…
Post-discovery Analysis of Anomalous Subsets
Isaiah Onando Mulang', William Ogallo, Girmaw Abebe Tadesse +1
Analyzing the behaviour of a population in response to disease and interventions is critical to unearth variability in healthcare as well as understand sub-populations that require…
Automated Supervised Feature Selection for Differentiated Patterns of Care
Catherine Wanjiru, William Ogallo, Girmaw Abebe Tadesse +3
An automated feature selection pipeline was developed using several state-of-the-art feature selection techniques to select optimal features for Differentiating Patterns of Care (D…
Ranking Facts for Explaining Answers to Elementary Science Questions
Jennifer D'Souza, Isaiah Onando Mulang', Soeren Auer
In multiple-choice exams, students select one answer from among typically four choices and can explain why they made that particular choice. Students are good at understanding natu…
HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations
Anson Bastos, Kuldeep Singh, Abhishek Nadgeri +3
Recently, several Knowledge Graph Embedding (KGE) approaches have been devised to represent entities and relations in dense vector space and employed in downstream tasks such as li…
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction
Abhishek Nadgeri, Anson Bastos, Kuldeep Singh +4
We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG). Especially…
CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata
Manoj Prabhakar Kannan Ravi, Kuldeep Singh, Isaiah Onando Mulang' +3
In this paper, we propose CHOLAN, a modular approach to target end-to-end entity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two transformer-based models in…
RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network
Anson Bastos, Abhishek Nadgeri, Kuldeep Singh +4
In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG). R…
Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models
Isaiah Onando Mulang', Kuldeep Singh, Chaitali Prabhu +3
Pretrained Transformer models have emerged as state-of-the-art approaches that learn contextual information from text to improve the performance of several NLP tasks. These models,…
Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking
Isaiah Onando Mulang, Kuldeep Singh, Akhilesh Vyas +4
The collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information. Since the crowd is not bound to a standard protocol for assigning entit…