27 citations · 95 across the 53 of their papers we have counts for
63 papers
Adaptive Ensemble Selection for Noisy Labels on Tabular Data
Faizaan Ali, Inwon Kang, Oshani Seneviratne
Incorrect or corrupted labels in tabular datasets can significantly degrade supervised learning performance, particularly when mislabeling is subtle and not easily detectable from…
Cost Characterization of Vertically Partitioned Federated Knowledge Graphs
Md Saikat Islam Khan Bappy, Oshani Seneviratne
Knowledge graphs are increasingly distributed across autonomous organizations that share an entity space but own disjoint subsets of relations, forming a vertical partition. Answer…
FedV-KGQA in Practice: Design Lessons and an Interactive Prototype
Md Saikat Islam Khan Bappy, Oshani Seneviratne
Knowledge graph question answering usually assumes that one system can reach the whole graph. In practice, facts are often held by organizations that share entity identifiers but o…
When Connected Does Not Mean Similar: Charting the Homophily Boundary of SNAP-KG for Streaming Entity Integration
Jui-Chien Lin, Oshani Seneviratne
SNAP-KG is a framework for assigning newly arriving entities to semantic communities in a growing knowledge graph (KG) using only their raw features, with no graph access and no re…
SNAP-KG: Streaming Node Assignment via Projection for Knowledge Graph Entity Integration
Jui-Chien Lin, Mohammad Mohammadi Amiri, Oshani Seneviratne
Knowledge graph (KG) construction pipelines must continuously integrate newly arriving entities into a growing graph. Unlike inserting triples between existing nodes, a newly arriv…
FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphs
Md Saikat Islam Khan Bappy, Oshani Seneviratne
Real-world data for knowledge graph question answering is often distributed across different organizations due to governance and data sovereignty constraints. While centralized sys…
GrOIL: Graph-Grounded Domain Ontology Induction with Constrained LLM Mediation
Maruf Ahmed Mridul, Abid Talukder, Oshani Seneviratne
Constructing formal ontologies from domain documents requires simultaneously enforcing corpus grounding, vocabulary consistency, axiom-level expressivity, and end-to-end provenance…
From "Strings" to "Things" for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systems
Abhirup Dasgupta, Fernando Spadea, Oshani Seneviratne
Personal Knowledge Graphs (PKGs) offer a privacy-preserving framework for modeling user preferences, yet constructing them from unstructured, decentralized conversational data rema…
SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching
Inwon Kang, Kavitha Srinivas, Nandana Mihindukulasooriya +4
Schema matching is a fundamental step in integrating heterogeneous data sources. While Pre-trained Language Models (PLMs) have revolutionized this task by capturing linguistic sema…
Towards Automated Ontology Generation from Unstructured Text: A Multi-Agent LLM Approach
Abid Talukder, Maruf Ahmed Mridul, Oshani Seneviratne
Automatically generating formal ontologies from unstructured natural language remains a central challenge in knowledge engineering. While large language models (LLMs) show promise,…
From Risk to Rescue: An Agentic Survival Analysis Framework for Liquidation Prevention
Fernando Spadea, Oshani Seneviratne
Decentralized Finance (DeFi) lending protocols like Aave v3 rely on over-collateralization to secure loans, yet users frequently face liquidation due to volatile market conditions.…
A Benchmark for Gap and Overlap Analysis as a Test of KG Task Readiness
Maruf Ahmed Mridul, Rohit Kapa, Oshani Seneviratne
Task-oriented evaluation of knowledge graph (KG) quality increasingly asks whether an ontology-based representation can answer the competency questions that users actually care abo…
Personal Health Data Integration and Intelligence through Semantic Web and Blockchain Technologies
Oshani Seneviratne, Manan Shukla, Jianjing Lin
Data integration among various stakeholders in the healthcare space remains a challenge, despite the impressive advances in Health AI in the past decade. There is a lot of ``messy'…
Benchmarking Temporal Web3 Intelligence: Lessons from the FinSurvival 2025 Challenge
Oshani Seneviratne, Fernando Spadea, Adrien Pavao +2
Temporal Web analytics increasingly relies on large-scale, longitudinal data to understand how users, content, and systems evolve over time. A rapidly growing frontier is the \emph…
Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendations
Fernando Spadea, Oshani Seneviratne
Personalized recommendation increasingly relies on private user data, motivating approaches that can adapt to individuals without centralizing their information. We present Federat…
Measuring Privacy Risks and Tradeoffs in Financial Synthetic Data Generation
Michael Zuo, Inwon Kang, Stacy Patterson +1
We explore the privacy-utility tradeoff of synthetic data generation schemes on tabular financial datasets, a domain characterized by high regulatory risk and severe class imbalanc…
Language Model Representations for Efficient Few-Shot Tabular Classification
Inwon Kang, Parikshit Ram, Yi Zhou +2
The Web is a rich source of structured data in the form of tables, from product catalogs and knowledge bases to scientific datasets. However, the heterogeneity of the structure and…
Explainable Federated Learning for U.S. State-Level Financial Distress Modeling
Lorenzo Carta, Fernando Spadea, Oshani Seneviratne
We present the first application of federated learning (FL) to the U.S. National Financial Capability Study, introducing an interpretable framework for predicting consumer financia…
Resource-Efficient LLM Application for Structured Transformation of Unstructured Financial Contracts
Maruf Ahmed Mridul, Oshani Seneviratne
The transformation of unstructured legal contracts into standardized, machine-readable formats is essential for automating financial workflows. The Common Domain Model (CDM) provid…
Aligning Language Models with Investor and Market Behavior for Financial Recommendations
Fernando Spadea, Oshani Seneviratne
Most financial recommendation systems often fail to account for key behavioral and regulatory factors, leading to advice that is misaligned with user preferences, difficult to inte…
Parallel and Multi-Stage Knowledge Graph Retrieval for Behaviorally Aligned Financial Asset Recommendations
Fernando Spadea, Oshani Seneviratne
Large language models (LLMs) show promise for personalized financial recommendations but are hampered by context limits, hallucinations, and a lack of behavioral grounding. Our pri…
Curriculum-Guided Reinforcement Learning for Synthesizing Gas-Efficient Financial Derivatives Contracts
Maruf Ahmed Mridul, Oshani Seneviratne
Smart contract-based automation of financial derivatives offers substantial efficiency gains, but its real-world adoption is constrained by the complexity of translating financial…
MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems
Shruthi Chari, Oshani Seneviratne, Prithwish Chakraborty +2
Explanations are crucial for building trustworthy AI systems, but a gap often exists between the explanations provided by models and those needed by users. To address this gap, we…
Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
Fernando Spadea, Oshani Seneviratne
We present a lightweight neuro-symbolic framework to mitigate over-personalization in LLM-based recommender systems by adapting user-side Knowledge Graphs (KGs) at inference time.…