Matches in Nanopublications for { ?s ?p ?o <https://w3id.org/np/RAkEC3g1S3-AUa4oS1tpjvWWtUUz8VEbzlSRYczzMRW3A/assertion>. }
Showing items 1 to 26 of
26
with 100 items per page.
- EmbedKGQA type Workflow assertion.
- PullNet type Workflow assertion.
- GraphNet type Workflow assertion.
- HGNet type Workflow assertion.
- arXiv.2502.03992 type Entity assertion.
- OntoSCPrompt type Workflow assertion.
- STaGQA type Workflow assertion.
- TERP type Workflow assertion.
- EmbedKGQA label "EmbedKGQA" assertion.
- PullNet label "PullNet" assertion.
- GraphNet label "GraphNet" assertion.
- HGNet label "HGNet" assertion.
- OntoSCPrompt label "OntoSCPrompt" assertion.
- STaGQA label "STaG-QA" assertion.
- TERP label "TERP" assertion.
- OntoSCPrompt comment "This method introduces a novel two-stage LLM-based KGQA system to generalize across heterogeneous KGs. It utilizes an ontology-guided hybrid prompt learning strategy, integrating KG ontology into prompts for semantic parsing and KG content population, and employs task-specific decoding strategies to ensure SPARQL query validity. The primary goal is to improve KGQA performance and generalization using LLMs." assertion.
- OntoSCPrompt subject LLMAugmentedKGQuestionAnswering assertion.
- arXiv.2502.03992 title "Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answering" assertion.
- arXiv.2502.03992 describes OntoSCPrompt assertion.
- arXiv.2502.03992 discusses EmbedKGQA assertion.
- arXiv.2502.03992 discusses PullNet assertion.
- arXiv.2502.03992 discusses GraphNet assertion.
- arXiv.2502.03992 discusses HGNet assertion.
- arXiv.2502.03992 discusses STaGQA assertion.
- arXiv.2502.03992 discusses TERP assertion.
- OntoSCPrompt hasTopCategory LLMAugmentedKG assertion.