REVIEW 6 cited by
KGValidator: A Framework for Automatic Validation of Knowledge Graph Construction
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
This study explores the use of Large Language Models (LLMs) for automatic evaluation of knowledge graph (KG) completion models. Historically, validating information in KGs has been a challenging task, requiring large-scale human annotation at prohibitive cost. With the emergence of general-purpose generative AI and LLMs, it is now plausible that human-in-the-loop validation could be replaced by a generative agent. We introduce a framework for consistency and validation when using generative models to validate knowledge graphs. Our framework is based upon recent open-source developments for structural and semantic validation of LLM outputs, and upon flexible approaches to fact checking and verification, supported by the capacity to reference external knowledge sources of any kind. The design is easy to adapt and extend, and can be used to verify any kind of graph-structured data through a combination of model-intrinsic knowledge, user-supplied context, and agents capable of external knowledge retrieval.
Forward citations
Cited by 6 Pith papers
-
Knowledge Conceptualization Impacts RAG Efficacy
An empirical study showing that both schema complexity and representation format affect how well GPT-4o generates SPARQL queries from competency questions, with mixed results across two knowledge graph families.
-
A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data
A metric-oriented survey that classifies intrinsic quality and trustworthiness metrics for LLM-generated data across six modalities and documents systematic evaluation gaps in the current literature.
-
MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph
A submission whose abstract describes a large temporal medical knowledge graph built by LLM agents, but whose full text is an unrelated paper on histogram regression, leaving the announced claims unsupported.
-
AI4Research: A Survey of Artificial Intelligence for Scientific Research
A survey that organizes AI-for-research work into five tasks, comprehension, survey, discovery, writing, and peer review, and compiles associated tools and benchmarks.
-
Content Moderation in TV Search: Balancing Policy Compliance, Relevance, and User Experience
The paper's hybrid lexicon-plus-LLM moderation layer flags unwanted TV search results, but all reported accuracy numbers come from the same LLM that powers the system, making the evaluation circular.
-
MultiCNKG: Integrating Cognitive Neuroscience, Gene, and Disease Knowledge Graphs Using Large Language Models
An LLM merges three biomedical ontologies into a small knowledge graph, but its validation metrics are self-contradictory and the resource is not released.
Discussion (0). Sign in to comment.