Knowledge Graph
Knowledge graphs (KGs) are structured representations of information, aiming to organize data into interconnected entities and relationships to facilitate knowledge discovery and reasoning. Current research heavily focuses on integrating KGs with large language models (LLMs) to enhance question answering, knowledge graph completion, and other knowledge-intensive tasks, often employing retrieval-augmented generation (RAG) and graph neural network architectures. This integration improves the accuracy and efficiency of various applications, ranging from legal article recommendation and medical diagnosis to supporting legislative processes and scholarly research. The resulting advancements have significant implications for diverse fields requiring complex information processing and reasoning.
Papers - Page 9
A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings
Jeffrey Sardina, John D. Kelleher, Declan O'SullivanLost in the Middle, and In-Between: Enhancing Language Models' Ability to Reason Over Long Contexts in Multi-Hop QA
George Arthur Baker, Ankush Raut, Sagi Shaier, Lawrence E Hunter, Katharina von der WenseText2Cypher: Bridging Natural Language and Graph Databases
Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga, Gianandrea Minneci
In-Context Learning with Topological Information for Knowledge Graph Completion
Udari Madhushani Sehwag, Kassiani Papasotiriou, Jared Vann, Sumitra GaneshHyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation
Mengfan Li, Xuanhua Shi, Chenqi Qiao, Teng Zhang, Hai Jin
Combining knowledge graphs and LLMs for hazardous chemical information management and reuse
Marcos Da Silveira, Louis Deladiennee, Kheira Acem, Oona FreudenthalAdapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs
Xiaqiang Tang, Jian Li, Nan Du, Sihong XieRAG-based Question Answering over Heterogeneous Data and Text
Philipp Christmann, Gerhard WeikumGenerating Knowledge Graphs from Large Language Models: A Comparative Study of GPT-4, LLaMA 2, and BERT
Ahan Bhatt, Nandan Vaghela, Kush DudhiaFine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning
Shengheng Liu, Tianqi Zhang, Ningning Fu, Yongming Huang
Knowledge Graphs are all you need: Leveraging KGs in Physics Question Answering
Krishnasai Addala, Kabir Dev Paul Baghel, Dhruv Jain, Chhavi Kirtani, Avinash Anand, Rajiv Ratn ShahA*Net and NBFNet Learn Negative Patterns on Knowledge Graphs
Patrick Betz, Nathanael Stelzner, Christian Meilicke, Heiner Stuckenschmidt, Christian BarteltKaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning
Peng Yu, Cheng Deng, Beiya Dai, Xinbing Wang, Ying WeneXpath: Explaining Knowledge Graph Link Prediction with Ontological Closed Path Rules
Ye Sun, Lei Shi, Yongxin Tong