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.
Yanbang Sun, Qing Huang, Zhenchang Xing et al.· ACM Transactions on Software...· 0 citations
Organizations produce significant amounts of first-party data as part of their day-to-day operations in manufacturing, clinical, and customer experience domains, which are not accessible to generic large language models. The paper discusses the five-layer approach to using this information asset: (1) data ingestion and quality assurance, (2) domain ontology engineering, (3) knowledge graph engineering and population, (4) GraphRAG-enabled AI augmentation, and (5) downstream application enablement. The paper focuses on the design considerations, implementation tactics, and lessons learned from real-world applications in manufacturing, healthcare, and professional networks rather than presenting original research results. The implementation results show up to 70–80% query time reduction and close to 85% fewer hallucinations on average for GraphRAG over the standard RAG for most of the use cases analyzed. The main barriers to adoption are the substantial manual effort involved in ontology engineering, 73–94% entity resolution accuracy across different industries, and 3–5× higher computational costs for GraphRAG compared to RAG. The framework gives practitioners and researchers a reference architecture for designing, evaluating, and governing enterprise knowledge graph deployments built on proprietary organizational data.
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, but also require large training datasets that provide good coverage of both the target knowledge graph and expected user questions. Likewise, integrating KG-specific optimizations improves accuracy and responsiveness but limits versatility and is susceptible to updates to the KG's ontology. LLMs have facilitated the development of training-free and KG-agnostic solutions that utilize reasoning and real-time decision making to explore the target KG, but the semi-structured nature of KGs makes this a complex and unpredictable task, leading to performance degradation and longer response times. In this paper we present PYTHIA, a training-free, plug-and-play solution for KGQA over any RDF KG. At its core, PYTHIA consists of an LLM agent guided by a relation-centric conceptual model of the KGQA task which is acted upon through a suite of tools for entity linking, graph exploration and query execution. The agent is responsible for interpreting user intent, orchestrating tool calls, and evaluating tool output, while the tools themselves present simple interfaces that abstract complex processes, reducing cognitive load on the agent. To assess its viability, we perform a comprehensive evaluation of PYTHIA and prior systems across four knowledge graphs and six datasets. The results show PYTHIA achieving a median performance improvement of 15.31% while improving cost and time efficiency at the performance frontier.
Sergios-Anestis Kefalidis, Konstantinos Plas, Manolis Koubarakis· Proceedings of the 32nd ACM...· 0 citations
Knowledge Graph Question Answering (KGQA) over RDF graphs remains challenging in domain-specific settings, where formal ontologies and curated text-SPARQL pairs are often unavailable. We present KGVoyager, 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. Using a think-act-observe loop with search, exploration, and execution tools, KGVoyager maps terms to graph IRIs, uncovers structure, and refines queries through execution feedback - all without pre-existing ontologies or examples. Unlike the prior state of the art, KGVoyager requires only a lightweight class index which renders it applicable for far more real-world endpoints. Across four benchmarks, KGVoyager improves F1 by ~8 points while cutting cost and runtime by ~22% each.
