Tootfinder

This https://arxiv.org/abs/2312.17269 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Conversational Question Answering with Reformulations over Knowledge Graph
Conversational question answering (convQA) over knowledge graphs (KGs) involves answering multi-turn natural language questions about information contained in a KG. State-of-the-art methods of ConvQA often struggle with inexplicit question-answer pairs. These inputs are easy for human beings to understand given a conversation history, but hard for a machine to interpret, which can degrade ConvQA performance. To address this problem, we propose a reinforcement learning (RL) based model, CornNet,…

Knowledge Graph Completion using Structural and Textual Embeddings
Sakher Khalil Alqaaidi, Krzysztof Kochut
https://arxiv.org/abs/2404.16206 https://

Knowledge Graph Completion using Structural and Textual Embeddings
Knowledge Graphs (KGs) are widely employed in artificial intelligence applications, such as question-answering and recommendation systems. However, KGs are frequently found to be incomplete. While much of the existing literature focuses on predicting missing nodes for given incomplete KG triples, there remains an opportunity to complete KGs by exploring relations between existing nodes, a task known as relation prediction. In this study, we propose a relations prediction model that harnesses bo…

Will have to read this and find out what is a pan-cancer knowledge graph!
RT @biorxiv_bioinfo: Knowledge Graph-based Thought: a knowledge graph enhanced LLMs framework for pan-cancer question answering https://www.biorxiv.org/content/10.1101/2024.04.17.589873v…

Federated Complex Qeury Answering
Qi Hu, Weifeng Jiang, Haoran Li, Zihao Wang, Jiaxin Bai, Qianren Mao, Yangqiu Song, Lixin Fan, Jianxin Li
https://arxiv.org/abs/2402.14609

Federated Complex Qeury Answering
Complex logical query answering is a challenging task in knowledge graphs (KGs) that has been widely studied. The ability to perform complex logical reasoning is essential and supports various graph reasoning-based downstream tasks, such as search engines. Recent approaches are proposed to represent KG entities and logical queries into embedding vectors and find answers to logical queries from the KGs. However, existing proposed methods mainly focus on querying a single KG and cannot be applied…

This https://arxiv.org/abs/2404.04264 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs
Despite the superb performance in many tasks, large language models (LLMs) bear the risk of generating hallucination or even wrong answers when confronted with tasks that demand the accuracy of knowledge. The issue becomes even more noticeable when addressing logic queries that require multiple logic reasoning steps. On the other hand, knowledge graph (KG) based question answering methods are capable of accurately identifying the correct answers with the help of knowledge graph, yet its accurac…

Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs
Lihui Liu, Zihao Wang, Ruizhong Qiu, Yikun Ban, Hanghang Tong
https://arxiv.org/abs/2404.04264

Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs
Despite the superb performance in many tasks, large language models (LLMs) bear the risk of generating hallucination or even wrong answers when confronted with tasks that demand the accuracy of knowledge. The issue becomes even more noticeable when addressing logic queries that require multiple logic reasoning steps. On the other hand, knowledge graph (KG) based question answering methods are capable of accurately identifying the correct answers with the help of knowledge graph, yet its accurac…

This https://arxiv.org/abs/2310.04910 has been replaced.
link: https://scholar.google.com/scholar?q=a

Faithful Knowledge Graph Explanations for Commonsense Reasoning
While fusing language models (LMs) and knowledge graphs (KGs) has become common in commonsense question answering research, enabling faithful chain-of-thought explanations in these models remains an open problem. One major weakness of current KG-based explanation techniques is that they overlook the faithfulness of generated explanations during evaluation. To address this gap, we make two main contributions: (1) We propose and validate two quantitative metrics - graph consistency and graph fide…

This https://arxiv.org/abs/2403.05881 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities. However, their application in real clinical settings is challenging due to potential deviations from medical facts and inherent biases. In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) with ranking and re-ranking techniques, aiming to improve free-text question-answering (QA) in the medical domain. Specifically, upon receiving a q…