This tutorial offers a high-level view of KG data quality at scale, organized along KG lifecycle stages, covering efficient quality estimation, as well as the role of Large Language Models as validators and auxiliary signals.
This work proposes Explore-Construct-Verify (ECV), a three-stage framework for API KG construction using large language models (LLMs), which preserves LLMs’ ability to discover domain-specific knowledge while enabling efficient post-hoc validation.
Yan-Bang Sun, Qing Huang, Zhen-Chang Xing et al.· ACM Transactions on Software...· 0 citations
The five-layer approach to using this information asset gives practitioners and researchers a reference architecture for designing, evaluating, and governing enterprise knowledge graph deployments built on proprietary organizational data.
The results show that structured RDF pipelines currently provide the most stable integration behavior, whereas JSON and text pipelines remain more sensitive to errors in mapping, extraction, and linking.
Knowledge graph question answering (KGQA) focuses on answering questions with data retrieved from knowledge graphs (KGs). Although Large Language Models (LLMs) have enabled the development of innovative KGQA systems, serious challenges remain. Approaches that utilize supervised finetuning are accurate and responsive, b...
Sergios-Anestis Kefalidis, Konstantinos Plas, Manolis Koubarakis· Proceedings of the 32nd ACM...· 0 citations
KGVoyager is presented, a KG-agnostic agentic architecture that generates SPARQL queries from natural language questions by dynamically discovering graph structure and semantics, requiring only a query endpoint of the underlying graph.
This position paper analyzes the state of the art in lifecycle-oriented information extraction and knowledge management for MICE and identifies key gaps, including the lack of integrated, lifecycle-oriented pipelines for multi-source data collection and preparation, insufficient alignment between extraction outputs, ta...
Gladys Kouakam, Nico Lindemann, Philippe Tamla· TRI-IVIS@AVI· 1 citation
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