The paper proposes a low-cost and interpretable fine-tuning extraction strategy for automatic term extraction, demonstrating consistent and balanced performance on the ATE Shared Task.

This paper introduces a new benchmark dataset and evaluation framework for 'data snapshot extraction,' focusing on identifying and localizing semantically meaningful analytical artifacts within operat…

The paper proposes a neuro-symbolic framework to construct highly consistent knowledge graphs for complex question answering by performing ontology-grounded corrections in a post-extraction stage.

This paper proposes a joint BERT-GNN architecture to systematically extract entities and relationships from diverse historical texts, achieving superior performance over conventional methods.

EviLink addresses the ambiguity of schema linking in Text-to-SQL by treating it as an uncertainty-aware inference over multiple plausible SQL paths, significantly improving recall and efficiency.

The paper introduces FOSSIL, a new multilingual dataset and specialized workflow designed to significantly improve the extraction of citations embedded within complex footnotes common in law and human…

OmniRetrieval introduces a unified framework that handles natural language queries across diverse, heterogeneous knowledge sources (text, relational, graphs) by dispatching source-native queries witho…

The paper introduces a robust four-stage NLP framework that uses schema-constrained LLMs and ESCO vocabulary to accurately extract and align educational competencies with labor market demands, quantif…

The paper introduces ANCHOR, a schema-agnostic system that constructs knowledge graphs from Cyber Threat Intelligence by dynamically discovering and validating against large ontologies, overcoming lim…

The authors introduce Structured PubMed, a comprehensive corpus of section-labeled biomedical abstracts compiled from the complete PubMed database.

This paper introduces GraphSteal, an attack framework demonstrating that Graph RAG systems can leak substantial portions of a hidden knowledge graph by treating them as structural oracles.

This paper proposes a lightweight encoder-based MEL solution called FAST-MEL that meets three objectives: high linking accuracy, computational efficiency, and storage efficiency.

The paper introduces IPO-Mine, a comprehensive toolkit and large-scale dataset designed to enable standardized, multimodal analysis of extremely long and structurally complex Initial Public Offering (…

The paper proposes an aggressive, parameter-efficient method to prune non-essential experts from Mixture-of-Experts (MoE) LLMs, significantly compressing the model while maintaining high machine trans…

The paper introduces Sieve, a system that uses a large language model (LLM) to generate executable query code from natural language security questions, significantly improving the ability to perform c…

The paper introduces 'infilling extraction' to accurately model training data memorization in Diffusion Language Models (DLMs), finding that bidirectional masking significantly increases the extractab…

The paper introduces GRID, an end-to-end framework that significantly improves the construction of security knowledge graphs from cyber threat intelligence by replacing unstable LLM-based supervision…

The paper proposes an embarrassingly simple detector that monitors model extraction attacks by testing whether the aggregate distribution of incoming LLM queries deviates from the historical distribut…

The paper introduces Semantic Triplet Restoration (STR), a novel protocol that converts complex table structures into atomic semantic triplets, improving table question answering by providing explicit…

mcp-proto-okn is a Python server that facilitates natural language access to complex scientific knowledge graphs, simplifying cross-domain knowledge analysis for biomedical research.