PulseAugur
EN
LIVE 10:32:27

New framework OPI improves multi-hop knowledge graph question answering

By PulseAugur Editorial · [3 sources] ·

Researchers have developed OPI, a novel framework for multi-hop knowledge graph question answering (KGQA). This approach addresses challenges in existing methods, such as the rapid growth of search spaces and the difficulty in satisfying complex question constraints. OPI utilizes a relation-centric ontology graph to manage relation type constraints and employs a bidirectional retrieval mechanism for more efficient expansion. An iterative refinement strategy further enhances answer prediction reliability by filtering irrelevant evidence. AI

IMPACT This research could lead to more efficient and accurate question-answering systems for complex knowledge graphs.

RANK_REASON The cluster contains an academic paper detailing a new framework for knowledge graph question answering.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New framework OPI improves multi-hop knowledge graph question answering

COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Runxuan Liu, Bei Luo, Jiaqi Li, Baoxin Wang, Ming Liu, Dayong Wu, Shijin Wang, Bing Qin ·

    Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering

    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…

  2. arXiv cs.AI TIER_1 English(EN) · Yongxue Shan, Meihan Wu, Cundi Fang, Jie Peng, Xiaodong Wang ·

    Ontology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering

    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…

  3. arXiv cs.AI TIER_1 English(EN) · Xiaodong Wang ·

    Ontology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering

    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…

RELATED ENTITIES

RELATED TOPICS