arXiv:2307.11206
Towards Ontologically Grounded and Language-Agnostic Knowledge Graphs
원문 초록
Knowledge graphs (KGs) have become the standard technology for the representation of factual information in applications such as recommendation engines, search, and question-answering systems. However, the continual updating of KGs, as well as the integration of KGs from different domains and KGs in different languages, remains to be a major challenge. What we suggest here is that by a reification of abstract objects and by acknowledging the ontological distinction between concepts and types, we arrive at an ontologically grounded and language-agnostic representation that can alleviate the difficulties in KG integration.
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Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs
iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems
DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation
토론
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