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.
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.
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.
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: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: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.
Retrieval in the SQL setting has largely been studied as the task of finding, within a large collection of SQL statements, the statement that answers a natural-language question. At scale, however, a more fundamental retrieval problem precedes generation: schema retrieval, identifying the tables and columns a question requires in a database that may contain thousands of them, far more than fit in a model's context.
The paper introduces SAFE‑QE, a training‑free, retriever‑agnostic method for query expansion that mitigates hallucinated terms from large language models. It uses a subsequence‑association filter to score candidate terms based on their reproducibility across randomised query contexts, down‑weighting terms that lack corpus support. SAFE‑QE then selects a broad yet consistent set of expansions via constrained coverage maximisation, and defines a new metric, the Hallucinated Expansion Term Rate, to quantify faithfulness. The approach matches state‑of‑the‑art ranking performance while injecting far fewer unsupported terms on TREC and BEIR benchmarks.
PRIME introduces an index-side optimization framework for structured document retrieval, comprising a Semantic Restructuring track that converts long documents into dense, structured sections and a Metadata Synthesis & Hierarchical Retrieval track that extracts semantic anchors and heading structures for efficient coarse-to-fine retrieval. The approach reduces embedding computation by 45.8× and index vector count by 22.4× while maintaining retrieval quality comparable to structure-aware chunking. In full deployment, Semantic Restructuring improves Answer‑F1 by 13.7%, and the study identifies qualitative regimes that influence when index-side optimization is beneficial or detrimental, noting that strong rerankers neutralize these effects.
BioELX is a retrieve‑rerank framework for cross‑lingual biomedical entity linking that tackles two key problems: the English‑biased UMLS alias training data and the degradation caused by naïvely adding context. It fine‑tunes SapBERT_multi with Wikidata‑derived cross‑lingual alias supervision to create shared concept neighborhoods, and then reranks candidates using pretrained LLMs with mention‑anchored prompting to focus on the target mention. Experiments demonstrate state‑of‑the‑art performance on four benchmarks, improving Recall@1 by 4.8–18.2 percentage points without task‑specific annotations.
ConvergeWriter introduces a bottom‑up, data‑driven framework for long‑form document generation that first retrieves exhaustive knowledge from a source corpus and clusters it into distinct knowledge groups. These clusters then guide the creation of a hierarchical outline and the final text, ensuring the output is strictly grounded in the retrieved material and traceable to its sources. Experiments on 14B and 32B LLMs show that this approach matches or surpasses state‑of‑the‑art baselines, especially in scenarios requiring high factual fidelity and structural coherence.
arXiv:2607. 02909v1 Announce Type: cross Abstract: Taxonomies provide key information about the semantic relationships between concepts and the inherent organization of vision and language.