In this paper, we introduce a novel approach toward automatic data wrangling in an attempt to alleviate the effort of end-users, e. g. data analysts, in structuring dynamic views from data lakes in the form of tabular data.
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.
As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger.
Ranked #1 on Open-Domain Question Answering on KILT: TriviaQA
no code implementations • 11 Jul 2022 • Nandana Mihindukulasooriya, Mike Sava, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Irene Yachbes, Aditya Gidh, Jillian Duckwitz, Kovit Nisar, Michael Santos, Alfio Gliozzo
A research division plays an important role of driving innovation in an organization.
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.
Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question answering.
In recent years, a number of keyphrase generation (KPG) approaches were proposed consisting of complex model architectures, dedicated training paradigms and decoding strategies.
The Semantic Answer Type and Relation Prediction Task (SMART) task is one of the ISWC 2021 Semantic Web challenges.
Most existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes.
Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI.
Ranked #1 on Zero-shot Slot Filling on T-REx
Relation linking is essential to enable question answering over knowledge bases.
Ranked #1 on Relation Linking on QALD-9
Recently, there has been a promising direction in evaluating language models in the same way we would evaluate knowledge bases, and the task of slot filling is the most suitable to this intent.
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.
1 code implementation • • Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramon Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
Knowledgebase question answering systems are heavily dependent on relation extraction and linking modules.
Ranked #1 on Relation Linking on QALD-7
no code implementations • 22 Jun 2020 • Luca Buratti, Saurabh Pujar, Mihaela Bornea, Scott McCarley, Yunhui Zheng, Gaetano Rossiello, Alessandro Morari, Jim Laredo, Veronika Thost, Yufan Zhuang, Giacomo Domeniconi
We explore this hypothesis through the use of a pre-trained transformer-based language model to perform code analysis tasks.
Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces.
We address relation extraction as an analogy problem by proposing a novel approach to learn representations of relations expressed by their textual mentions.
The textual similarity is a crucial aspect for many extractive text summarization methods.
People have information needs of varying complexity, which can be solved by an intelligent agent able to answer questions formulated in a proper way, eventually considering user context and preferences.