Search Results for author: Nandana Mihindukulasooriya

Found 25 papers, 8 papers with code

Permutation Invariant Strategy Using Transformer Encoders for Table Understanding

no code implementations Findings (NAACL) 2022 Sarthak Dash, Sugato Bagchi, Nandana Mihindukulasooriya, Alfio Gliozzo

Existing methods that leverage pretrained Transformer encoders range from a simple construction of pseudo-sentences by concatenating text across rows or columns to complex parameter-intensive models that encode table structure and require additional pretraining.

Column Type Annotation Entity Linking +4

Matching Table Metadata with Business Glossaries Using Large Language Models

no code implementations8 Sep 2023 Elita Lobo, Oktie Hassanzadeh, Nhan Pham, Nandana Mihindukulasooriya, Dharmashankar Subramanian, Horst Samulowitz

The resulting matching enables the use of an available or curated business glossary for retrieval and analysis without or before requesting access to the data contents.


Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text

1 code implementation4 Aug 2023 Nandana Mihindukulasooriya, Sanju Tiwari, Carlos F. Enguix, Kusum Lata

In this paper, we present Text2KGBench, a benchmark to evaluate the capabilities of language models to generate KGs from natural language text guided by an ontology.

Fact Checking Graph Generation +1

Finspector: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models

1 code implementation26 May 2023 Bum Chul Kwon, Nandana Mihindukulasooriya

Pre-trained transformer-based language models are becoming increasingly popular due to their exceptional performance on various benchmarks.

Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text

no code implementations15 May 2023 Hanieh Khorashadizadeh, Nandana Mihindukulasooriya, Sanju Tiwari, Jinghua Groppe, Sven Groppe

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 downstream applications such as question-answering, recommendation systems, semantic search, and advanced analytics.

graph construction Knowledge Graphs +5

Scaling Knowledge Graphs for Automating AI of Digital Twins

1 code implementation26 Oct 2022 Joern Ploennigs, Konstantinos Semertzidis, Fabio Lorenzi, Nandana Mihindukulasooriya

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.

Knowledge Graphs

KnowGL: Knowledge Generation and Linking from Text

no code implementations25 Oct 2022 Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Nandana Mihindukulasooriya, Owen Cornec, Alfio Massimiliano Gliozzo

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), such as Wikidata.


KGI: An Integrated Framework for Knowledge Intensive Language Tasks

no code implementations8 Apr 2022 Md Faisal Mahbub Chowdhury, Michael Glass, Gaetano Rossiello, Alfio Gliozzo, Nandana Mihindukulasooriya

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, such as slot filling, open domain question answering, dialogue, and fact-checking.

Fact Checking Open-Domain Question Answering +4

A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases

no code implementations15 Jan 2022 Sumit Neelam, Udit Sharma, Hima Karanam, Shajith Ikbal, Pavan Kapanipathi, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Young-suk Lee, Santosh Srivastava, Cezar Pendus, Saswati Dana, Dinesh Garg, Achille Fokoue, G P Shrivatsa Bhargav, Dinesh Khandelwal, Srinivas Ravishankar, Sairam Gurajada, Maria Chang, Rosario Uceda-Sosa, Salim Roukos, Alexander Gray, Guilherme Lima, Ryan Riegel, Francois Luus, L Venkata Subramaniam

Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata.

Knowledge Base Question Answering Semantic Parsing

Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation

no code implementations14 Jan 2022 Md Faisal Mahbub Chowdhury, Gaetano Rossiello, Michael Glass, Nandana Mihindukulasooriya, Alfio Gliozzo

In recent years, a number of keyphrase generation (KPG) approaches were proposed consisting of complex model architectures, dedicated training paradigms and decoding strategies.

Keyphrase Generation Language Modelling

Learning to Transpile AMR into SPARQL

no code implementations15 Dec 2021 Mihaela Bornea, Ramon Fernandez Astudillo, Tahira Naseem, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Pavan Kapanipathi, Radu Florian, Salim Roukos

We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA).

Knowledge Base Question Answering Semantic Parsing

Type Prediction Systems

no code implementations2 Apr 2021 Sarthak Dash, Nandana Mihindukulasooriya, Alfio Gliozzo, Mustafa Canim

Inferring semantic types for entity mentions within text documents is an important asset for many downstream NLP tasks, such as Semantic Role Labelling, Entity Disambiguation, Knowledge Base Question Answering, etc.

Entity Disambiguation Knowledge Base Question Answering +2

Open Knowledge Graphs Canonicalization using Variational Autoencoders

1 code implementation EMNLP 2021 Sarthak Dash, Gaetano Rossiello, Nandana Mihindukulasooriya, Sugato Bagchi, Alfio Gliozzo

In this work, we propose Canonicalizing Using Variational Autoencoders (CUVA), a joint model to learn both embeddings and cluster assignments in an end-to-end approach, which leads to a better vector representation for the noun and relation phrases.

Clustering Knowledge Graphs

SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge

1 code implementation1 Dec 2020 Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck

Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given problem domain.

Knowledge Base Question Answering Type prediction +1

Hypernym Detection Using Strict Partial Order Networks

no code implementations23 Sep 2019 Sarthak Dash, Md. Faisal Mahbub Chowdhury, Alfio Gliozzo, Nandana Mihindukulasooriya, Nicolas Rodolfo Fauceglia

This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints.

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