arXiv:2308.06374
Large Language Models and Knowledge Graphs: Opportunities and Challenges
원문 초록
Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge. In this position paper, we will discuss some of the common debate points within the community on LLMs (parametric knowledge) and Knowledge Graphs (explicit knowledge) and speculate on opportunities and visions that the renewed focus brings, as well as related research topics and challenges.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Position Paper: Metadata Enrichment Model: Integrating Neural Networks and Semantic Knowledge Graphs for Cultural Heritage Applications
Open-World Social Event Classification
EasySpider: A No-Code Visual System for Crawling the Web
Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs
토론
이 논문과 연결된 포럼 글이 아직 없습니다.