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Haohan Xue

3 accepted papers

2026

Information-Needs-Guided Virtual Knowledge Graph Enrichment via Large Language Models

IJCAI 2026

Virtual Knowledge Graphs (VKGs) provide an effective solution for data integration by mapping heterogeneous data sources to a unified ontology. However, existing VKG construction frameworks primarily focus on one-shot construction, which often results in partial data coverage and support for only in

Cited by 0Scholar
2026

NaVQA: Mitigating Silent Failures in Question Answering over Virtual Knowledge Graph

IJCAI 2026

Virtual Knowledge Graphs (VKGs) provide unified access to legacy relational data sources through a high-level ontology modeling a domain of interest. The content of the ontology elements (classes and properties) is virtually mapped to underlying data sources through declarative mappings. The standar

Cited by 0Scholar
2025

LLM4VKG: Leveraging Large Language Models for Virtual Knowledge Graph Construction

IJCAI 2025

Virtual Knowledge Graphs (VKGs) provide an effective solution for data integration but typically require significant expertise for their construction. This process, involving ontology development, schema analysis, and mapping creation, is often hindered by naming ambiguities and matching issues, whi