Pith. sign in

REVIEW 1 cited by

Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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

arxiv 2312.03022 v3 pith:MF6EV7XZ submitted 2023-12-05 cs.AI cs.CLcs.LG

Hongbin Ye , Honghao Gui , Aijia Zhang , Tong Liu , Weiqiang Jia This is my paper · ORCID
classification cs.AIcs.CLcs.LG
keywords cooperkgcknowledgeconstructiongraphacrossaddressingaggregationapproach
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper introduces CooperKGC, a novel framework challenging the conventional solitary approach of large language models (LLMs) in knowledge graph construction (KGC). CooperKGC establishes a collaborative processing network, assembling a team capable of concurrently addressing entity, relation, and event extraction tasks. Experimentation demonstrates that fostering collaboration within CooperKGC enhances knowledge selection, correction, and aggregation capabilities across multiple rounds of interactions.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From "Strings" to "Things" for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systems

    cs.IR 2026-04 conditional novelty 6.0 of 10

    Open-weight LLMs extract usable user-preference triples from recommendation dialogues for Personal Knowledge Graphs, with balanced small models often best for downstream recommendations.

Pith tools