研究人员开发了OPI，一个用于多跳知识图谱问答（KGQA）的新型框架。该方法解决了现有方法中的挑战，例如搜索空间的快速增长以及满足复杂问题约束的困难。OPI利用以关系为中心的本体图来管理关系类型约束，并采用双向检索机制以实现更有效的扩展。迭代细化策略通过过滤无关证据进一步提高了答案预测的可靠性。 AI
影响 这项研究可能带来更高效、更准确的复杂知识图谱问答系统。
排序理由 该集群包含一篇详细介绍知识图谱问答新框架的学术论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2502.11491v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in natural language processing. However, in knowledge graph question answering tasks (KGQA), there remains the issue of answering questions that require multi…
arXiv:2606.28076v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) aims to answer natural-language questions by reasoning over structured facts. Existing multi-hop KGQA methods mainly rely on topic-centered expansion, which faces two key challenges: the sea…
Knowledge graph question answering (KGQA) aims to answer natural-language questions by reasoning over structured facts. Existing multi-hop KGQA methods mainly rely on topic-centered expansion, which faces two key challenges: the search space rapidly grows with noisy mixed-type pa…