14 citations · 114 across the 30 of their papers we have counts for
32 papers
OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models
Hamed Babaei Giglou, Jennifer D'Souza, Andrei Aioanei +2
Ontology learning (OL) aims to automatically construct structured knowledge models from text, yet progress remains fragmented across methods, domains, and evaluation practices. Des…
Automatic Prompt Engineering with No Task Cues and No Tuning
Faisal Chowdhury, Nandana Mihindukulasooriya, Niharika S D'Souza +4
This paper presents a system for automatic prompt engineering that is much simpler in both design and application and yet as effective as the existing approaches. It requires no tu…
Bridging Business Intent and Data: A Benchmark for Automatic Relational Data Product Generation
Faisal Chowdhury, Sola Shirai, Sarthak Dash +2
A data product is designed to address a specific business need by transforming raw data into a curated, usable asset that delivers actionable insights. Despite practical advances i…
DPDisc: From Factoid Questions to Data Product Requests for Open-World Data Product Discovery over Tables and Text
Liangliang Zhang, Nandana Mihindukulasooriya, Niharika S. D'Souza +4
Data products are reusable, self-contained assets designed for specific business use cases. Automating their discovery is of great industry interest, as it enables efficient data a…
Transduction is All You Need for Structured Data Workflows
Alfio Gliozzo, Naweed Khan, Christodoulos Constantinides +4
This paper introduces Agentics, a functional agentic AI framework for building LLM-based structured data workflow pipelines. Designed for both research and practical applications,…
StructText: A Synthetic Table-to-Text Approach for Benchmark Generation with Multi-Dimensional Evaluation
Satyananda Kashyap, Sola Shirai, Nandana Mihindukulasooriya +1
Extracting structured information from text, such as key-value pairs that could augment tabular data, is quite useful in many enterprise use cases. Although large language models (…
Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study
Nandana Mihindukulasooriya, Niharika S. D'Souza, Faisal Chowdhury +1
A KG represents a network of entities and illustrates relationships between them. KGs are used for various applications, including semantic search and discovery, reasoning, decisio…
Domain specific ontologies from Linked Open Data (LOD)
Rosario Uceda-Sosa, Nandana Mihindukulasooriya, Atul Kumar +2
Logical and probabilistic reasoning tasks that require a deeper knowledge of semantics are increasingly relying on general purpose ontologies such as Wikidata and DBpedia. However,…
Scholarly Wikidata: Population and Exploration of Conference Data in Wikidata using LLMs
Nandana Mihindukulasooriya, Sanju Tiwari, Daniil Dobriy +3
Several initiatives have been undertaken to conceptually model the domain of scholarly data using ontologies and to create respective Knowledge Graphs. Yet, the full potential seem…
Research Trends for the Interplay between Large Language Models and Knowledge Graphs
Hanieh Khorashadizadeh, Fatima Zahra Amara, Morteza Ezzabady +7
This survey investigates the synergistic relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs), which is crucial for advancing AI's capabilities in understan…
Matching Table Metadata with Business Glossaries Using Large Language Models
Elita Lobo, Oktie Hassanzadeh, Nhan Pham +3
Enterprises often own large collections of structured data in the form of large databases or an enterprise data lake. Such data collections come with limited metadata and strict ac…
Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text
Nandana Mihindukulasooriya, Sanju Tiwari, Carlos F. Enguix +1
The recent advances in large language models (LLM) and foundation models with emergent capabilities have been shown to improve the performance of many NLP tasks. LLMs and Knowledge…
Finspector: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models
Bum Chul Kwon, Nandana Mihindukulasooriya
Pre-trained transformer-based language models are becoming increasingly popular due to their exceptional performance on various benchmarks. However, concerns persist regarding the…
Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text
Hanieh Khorashadizadeh, Nandana Mihindukulasooriya, Sanju Tiwari +2
Knowledge graphs can represent information about the real-world using entities and their relations in a structured and semantically rich manner and they enable a variety of downstr…
KnowGL: Knowledge Generation and Linking from Text
Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Nandana Mihindukulasooriya +2
We propose KnowGL, a tool that allows converting text into structured relational data represented as a set of ABox assertions compliant with the TBox of a given Knowledge Graph (KG…
Scaling Knowledge Graphs for Automating AI of Digital Twins
Joern Ploennigs, Konstantinos Semertzidis, Fabio Lorenzi +1
Digital Twins are digital representations of systems in the Internet of Things (IoT) that are often based on AI models that are trained on data from those systems. Semantic models…
Knowledge Graph Induction enabling Recommending and Trend Analysis: A Corporate Research Community Use Case
Nandana Mihindukulasooriya, Mike Sava, Gaetano Rossiello +7
A research division plays an important role of driving innovation in an organization. Drawing insights, following trends, keeping abreast of new research, and formulating strategie…
CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases
Dung Thai, Srinivas Ravishankar, Ibrahim Abdelaziz +7
Knowledge bases (KBs) are often incomplete and constantly changing in practice. Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs…
KGI: An Integrated Framework for Knowledge Intensive Language Tasks
Md Faisal Mahbub Chowdhury, Michael Glass, Gaetano Rossiello +2
In this paper, we present a system to showcase the capabilities of the latest state-of-the-art retrieval augmented generation models trained on knowledge-intensive language tasks,…
A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases
Sumit Neelam, Udit Sharma, Hima Karanam +22
Knowledge Base Question Answering (KBQA) tasks that involve complex reasoning are emerging as an important research direction. However, most existing KBQA datasets focus primarily…
Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation
Md Faisal Mahbub Chowdhury, Gaetano Rossiello, Michael Glass +2
In recent years, a number of keyphrase generation (KPG) approaches were proposed consisting of complex model architectures, dedicated training paradigms and decoding strategies. In…
Semantic Answer Type and Relation Prediction Task (SMART 2021)
Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo +5
Each year the International Semantic Web Conference organizes a set of Semantic Web Challenges to establish competitions that will advance state-of-the-art solutions in some proble…
Learning to Transpile AMR into SPARQL
Mihaela Bornea, Ramon Fernandez Astudillo, Tahira Naseem +5
We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA). This allows us to delegate part of…
SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases
Sumit Neelam, Udit Sharma, Hima Karanam +21
Knowledge Base Question Answering (KBQA) tasks that in-volve complex reasoning are emerging as an important re-search direction. However, most KBQA systems struggle withgeneralizab…