Search Results for author: Kuldeep Singh

Found 26 papers, 15 papers with code

Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions

no code implementations22 Nov 2022 Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Toyotaro Suzumura, Manish Singh

Since static methods to learn the graph spectrum would not consider the history of the evolution of the spectrum as the graph evolves with time, we propose a novel approach to learn the graph wavelets to capture this evolving spectra.

Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs

1 code implementation9 Oct 2022 Endri Kacupaj, Kuldeep Singh, Maria Maleshkova, Jens Lehmann

The majority of existing ConvQA methods rely on full supervision signals with a strict assumption of the availability of gold logical forms of queries to extract answers from the KG.

Conversational Question Answering Information Retrieval +3

Plumber: A Modular Framework to Create Information Extraction Pipelines

1 code implementation3 Jun 2022 Mohamad Yaser Jaradeh, Kuldeep Singh, Markus Stocker, Sören Auer

Information Extraction (IE) tasks are commonly studied topics in various domains of research.

How Expressive are Transformers in Spectral Domain for Graphs?

1 code implementation23 Jan 2022 Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Hiroki Kanezashi, Toyotaro Suzumura, Isaiah Onando Mulang'

We further provide a theoretical analysis and prove that the spatial attention mechanism in the transformer cannot effectively capture the desired frequency response, thus, inherently limiting its expressiveness in spectral space.

Graph Representation Learning

Triple Classification for Scholarly Knowledge Graph Completion

no code implementations23 Nov 2021 Mohamad Yaser Jaradeh, Kuldeep Singh, Markus Stocker, Sören Auer

Scholarly Knowledge Graphs (KGs) provide a rich source of structured information representing knowledge encoded in scientific publications.

Classification Link Prediction +1

HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations

1 code implementation12 Aug 2021 Anson Bastos, Kuldeep Singh, Abhishek Nadgeri, Saeedeh Shekarpour, Isaiah Onando Mulang, Johannes Hoffart

A few KGE techniques address interpretability, i. e., mapping the connectivity patterns of the relations (i. e., symmetric/asymmetric, inverse, and composition) to a geometric interpretation such as rotations.

Knowledge Graph Embedding Link Prediction

VOGUE: Answer Verbalization through Multi-Task Learning

2 code implementations24 Jun 2021 Endri Kacupaj, Shyamnath Premnadh, Kuldeep Singh, Jens Lehmann, Maria Maleshkova

The VOGUE framework attempts to generate a verbalized answer using a hybrid approach through a multi-task learning paradigm.

Answer Generation Knowledge Graphs +2

Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs

1 code implementation13 Mar 2021 Joan Plepi, Endri Kacupaj, Kuldeep Singh, Harsh Thakkar, Jens Lehmann

In this work, we propose a novel framework named CARTON, which performs multi-task semantic parsing for handling the problem of conversational question answering over a large-scale knowledge graph.

Conversational Question Answering Knowledge Graphs +2

ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation

1 code implementation13 Mar 2021 Endri Kacupaj, Barshana Banerjee, Kuldeep Singh, Jens Lehmann

This paper presents ParaQA, a question answering (QA) dataset with multiple paraphrased responses for single-turn conversation over knowledge graphs (KG).

Conversational Question Answering Knowledge Graphs +1

Uncovering the Corona Virus Map Using Deep Entities and Relationship Models

no code implementations7 Sep 2020 Kuldeep Singh, Puneet Singla, Ketan Sarode, Anurag Chandrakar, Chetan Nichkawde

We extract entities and relationships related to COVID-19 from a corpus of articles related to Corona virus by employing a novel entities and relationship model.

Inductive Bias Multi-Task Learning

Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models

1 code implementation12 Aug 2020 Isaiah Onando Mulang', Kuldeep Singh, Chaitali Prabhu, Abhishek Nadgeri, Johannes Hoffart, Jens Lehmann

We further hypothesize that our proposed KG context can be standardized for Wikipedia, and we evaluate the impact of KG context on state-of-the-art NED model for the Wikipedia knowledge base.

Entity Disambiguation

Falcon 2.0: An Entity and Relation Linking Tool over Wikidata

1 code implementation24 Dec 2019 Ahmad Sakor, Kuldeep Singh, Anery Patel, Maria-Esther Vidal

The Natural Language Processing (NLP) community has significantly contributed to the solutions for entity and relation recognition from the text, and possibly linking them to proper matches in Knowledge Graphs (KGs).

Knowledge Graphs Language Modelling +1

Towards Optimisation of Collaborative Question Answering over Knowledge Graphs

no code implementations14 Aug 2019 Kuldeep Singh, Mohamad Yaser Jaradeh, Saeedeh Shekarpour, Akash Kulkarni, Arun Sethupat Radhakrishna, Ioanna Lytra, Maria-Esther Vidal, Jens Lehmann

Collaborative Question Answering (CQA) frameworks for knowledge graphs aim at integrating existing question answering (QA) components for implementing sequences of QA tasks (i. e. QA pipelines).

Knowledge Graphs Question Answering

Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text

1 code implementation NAACL 2019 Ahmad Sakor, on, Isaiah o Mulang{'}, Kuldeep Singh, Saeedeh Shekarpour, Maria Esther Vidal, Jens Lehmann, S{\"o}ren Auer

Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e. g. wrt.

Entity Linking Implicit Relations +3

No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph

3 code implementations26 Sep 2018 Kuldeep Singh, Ioanna Lytra, Arun Sethupat Radhakrishna, Saeedeh Shekarpour, Maria-Esther Vidal, Jens Lehmann

Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of question answering for user interaction.

Knowledge Graphs Question Answering

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