Beamqa: Multi-hop knowledge graph question answering with sequence-to-sequence prediction and beam search
F Atif, O El Khatib, D Difallah - Proceedings of the 46th International ACM …, 2023 - dl.acm.org
Knowledge Graph Question Answering (KGQA) is a task that aims to answer natural
language queries by extracting facts from a knowledge graph. Current state-of-the-art …
language queries by extracting facts from a knowledge graph. Current state-of-the-art …
Joint reasoning with knowledge subgraphs for Multiple Choice Question Answering
Humans are able to reason from multiple sources to arrive at the correct answer. In the
context of Multiple Choice Question Answering (MCQA), knowledge graphs can provide …
context of Multiple Choice Question Answering (MCQA), knowledge graphs can provide …
Infusing lattice symmetry priors in attention mechanisms for sample-efficient abstract geometric reasoning
Abstract The Abstraction and Reasoning Corpus (ARC)(Chollet, 2019) and its most recent
language-complete instantiation (LARC) has been postulated as an important step towards …
language-complete instantiation (LARC) has been postulated as an important step towards …
A* net: A scalable path-based reasoning approach for knowledge graphs
Abstract Reasoning on large-scale knowledge graphs has been long dominated by
embedding methods. While path-based methods possess the inductive capacity that …
embedding methods. While path-based methods possess the inductive capacity that …
Rearev: Adaptive reasoning for question answering over knowledge graphs
C Mavromatis, G Karypis - arXiv preprint arXiv:2210.13650, 2022 - arxiv.org
Knowledge Graph Question Answering (KGQA) involves retrieving entities as answers from
a Knowledge Graph (KG) using natural language queries. The challenge is to learn to …
a Knowledge Graph (KG) using natural language queries. The challenge is to learn to …
NuTrea: neural tree search for context-guided multi-hop KGQA
Abstract Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves
retrieving nodes from a knowledge graph (KG) to answer natural language questions …
retrieving nodes from a knowledge graph (KG) to answer natural language questions …
Multi-hop community question answering based on multi-aspect heterogeneous graph
Y Wu, H Yin, Q Zhou, D Liu, D Wei, J Dong - Information Processing & …, 2024 - Elsevier
Community question answering aims to connect queries and answers based on users'
community behaviors, find the most relevant solutions for newly raised questions, and …
community behaviors, find the most relevant solutions for newly raised questions, and …
Exploiting hybrid semantics of relation paths for multi-hop question answering over knowledge graphs
Answering natural language questions on knowledge graphs (KGQA) remains a great
challenge in terms of understanding complex questions via multi-hop reasoning. Previous …
challenge in terms of understanding complex questions via multi-hop reasoning. Previous …
Case-based reasoning for better generalization in textual reinforcement learning
Text-based games (TBG) have emerged as promising environments for driving research in
grounded language understanding and studying problems like generalization and sample …
grounded language understanding and studying problems like generalization and sample …
Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings
Entity linking methods based on dense retrieval are an efficient and widely used solution in
large-scale applications, but they fall short of the performance of generative models, as they …
large-scale applications, but they fall short of the performance of generative models, as they …