Steps to Knowledge Graphs Quality Assessment
원문 초록
Knowledge Graphs (KGs) have been popularized during the last decade, for instance, they are used widely in the context of the web. In 2012 Google has presented the Google's Knowledge Graph that is used to improve their web search services. The web also hosts different KGs, such as DBpedia and Wikidata, which are used in various applications like personal assistants and question-answering systems. Various web applications rely on KGs to provide concise, complete, accurate, and fresh answer to users. However, what is the quality of those KGs? In which cases should a Knowledge Graph (KG) be used? How might they be evaluated? We reviewed the literature on quality assessment of data, information, linked data, and KGs. We extended the current state-of-the-art frameworks by adding various quality dimensions (QDs) and quality metrics (QMs) that are specific to KGs. Furthermore, we propose a general-purpose, customizable to a domain or task, and practical quality assessment framework for assessing the quality of KGs.
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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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