QAnswer: Towards Question Answering Search over Websites
원문 초록
Question Answering (QA) is increasingly used by search engines to provide results to their end-users, yet very few websites currently use QA technologies for their search functionality. To illustrate the potential of QA technologies for the website search practitioner, we demonstrate web searches that combine QA over knowledge graphs and QA over free text -- each being usually tackled separately. We also discuss the different benefits and drawbacks of both approaches for web site searches. We use the case studies made of websites hosted by the Wikimedia Foundation (namely Wikipedia and Wikidata). Differently from a search engine (e.g. Google, Bing, etc), the data are indexed integrally, i.e. we do not index only a subset, and they are indexed exclusively, i.e. we index only data available on the corresponding website.
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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
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