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arXiv · 2609.30184

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

Sriram Kannan·Swetha Saseendran·Vishnu Vardhan Reddy Kandi·Leslie Barrett·Madhavan Seshadri·Enrico Santus
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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.

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Sriram Kannan, Swetha Saseendran, Vishnu Vardhan Reddy Kandi, Leslie Barrett, Madhavan Seshadri, Enrico Santus. 2026-09-24. ARGUS: Role-Aware Event Knowledge Graphs for U.S. Employment-Discrimination Complaints. https://arxiv.org/abs/2609.30184

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