arXiv Machine Learning
Peeling the Matryoshka: Extracting Ontological Structure from Retrieval Embeddings
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arXiv AI
Jul 3
OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models
arXiv:2607. 01977v1 Announce Type: new Abstract: Ontology learning (OL) aims to automatically construct structured knowledge models from text, yet progress remains fragmented across methods, domains, and evaluation practices.
arXiv Computation and Language
Aug 27
Corpus2Skill: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG
Corpus2Skill is a retrieval architecture that transforms an enterprise knowledge base into a hierarchical skill directory, enabling an LLM agent to navigate from high-level summaries to specific documents and backtrack when necessary. On an enterprise customer‑support benchmark, it outperforms single‑shot dense, hybrid, hierarchical‑retrieval, and agentic RAG baselines in answer quality and grounding, with a moderate cost tradeoff. An eleven‑dataset study shows that corpus navigation excels on single‑domain corpora with a recoverable topical taxonomy but is less effective on open‑domain factoid pools or homogeneous‑tabular corpora, providing a design guideline for knowledge‑grounded systems.
arXiv AI
6d ago
SpectRAG: Weighted Chunk Graphs for Token-Budgeted Context Selection in Multi-Hop Retrieval-Augmented Generation
SpectRAG is a retrieval‑augmented generation framework that uses weighted chunk graphs and spectral relevance propagation to select token‑budgeted context for multi‑hop reasoning. It builds a high‑recall seed set, expands it over a graph that encodes semantic similarity, document structure, and entity links, and re‑scores candidates with graph proximity and spectral information. Experiments on MultiHopRAG, UltraDomain, and long‑document QA show improved evidence recovery and competitive generation quality compared to dense, hybrid, and other RAG baselines.
arXiv Computation and Language
Sep 18
CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph
CORTEX is a novel framework that transforms web‑scale corpus construction from flat document filtering into structured knowledge organization using an Ontological Corpus Graph (OCG). The OCG comprises a quality‑refined content layer, a lightweight ontology layer that evolves via LLMs, and a cross‑domain alignment layer that supports arbitrary taxonomic resolution. Experiments demonstrate CORTEX’s effectiveness, and the authors release a 24.14 B‑token refined corpus, its OCG, and a cross‑domain benchmark called CortexBench for evaluating large language models.
arXiv Machine Learning
Jun 11
GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs
arXiv:2606. 11898v1 Announce Type: cross Abstract: Research on Text-Attributed Graphs (TAGs) has gained significant attention recently due to its broad applications across various real-world data scenarios, such as citation networks, e-commerce platforms, social media, and web pages.
arXiv AI
Jun 19
IHUBERT: Vector-Based Semantic Deduplication and Domain-Balanced Pretraining for Persian Resources
arXiv:2606. 20089v1 Announce Type: cross Abstract: Persian pretrained language models (PLMs) are still limited by the scarcity of large-scale, high-quality pretraining corpora and by insufficient evaluation beyond standard classification and NER tasks.