ARGUS: Role-Aware Event Knowledge Graphs for U.S. Employment-Discrimination Complaints

Title:ARGUS: Role-Aware Event Knowledge Graphs for U.S. Employment-Discrimination Complaints

Abstract:U.S. employment-discrimination complaints describe complex event sequences that are not explicitly captured by lexical or embedding-based representations alone. We present ARGUS, a source-grounded pipeline that combines a 5W1H-inspired schema, legal-domain models, and LLM-based structured generation to construct document-level Event Knowledge Graphs (EKGs) from CourtListener complaints. ARGUS extracts fact-bearing statements, builds chunk-level event graphs with participant, temporal, and causal structure, and merges them into document-level representations. We evaluate graph quality through human and multi-model assessment and test downstream utility on claim classification and legal QA. The graph-structured classifier outperforms raw and linearized baselines on the held-out set, and EKG-only retrieval improves document-scoped QA, while open-retrieval gains remain limited by low first-stage candidate recall. These results suggest that EKGs are most useful for organizing and reasoning over evidence once relevant material has been retrieved.

Computation and Language (cs.CL)

arXiv:2609.30184 [cs.CL]

(or arXiv:2609.30184v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.30184 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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