arXiv AI

Automated Construction of FAIR Digital Object Knowledge Graphs from Flat Cultural Heritage Records

arXiv AI
Sep 21

From Code Archival to Knowledge Graph: Bridging Software Heritage, COAR Notify and Wikidata

The paper introduces an end‑to‑end pipeline that harvests, validates, and models links between scholarly articles and their source code from journals such as JOSS, SoftwareX, and IPOL, as well as SIGMOD ARI reproducibility reports. It produces a curated set of 4,397 DOI‑repository pairs and defines two Wikidata‑based application profiles—one for articles and one for software—aligned with schema.org and CodeMeta. Using these profiles, the authors created 4,182 new Wikidata software items linked to their papers, while only 82 repositories were previously represented, and they demonstrate compatibility with the COAR Notify protocol for future live enrichment.

By Camillo Carlo Pellizzari di San Girolamo, Francesco Tosoni
arXiv AI
Oct 31

Rebuilding a Field's Open-Problem Catalog from the Literature: A Two-Layer Knowledge Graph, and Why Status Must Stay Proposed

The paper presents a system that transforms open‑problem listings in research fields into a structured, queryable knowledge graph. It combines a hand‑curated problem layer with an evidence layer derived from bibliographic data, linked through typed, provenance‑carrying connections. The authors evaluate the approach on database and AI problem catalogs, showing that the new graph can answer all previously answerable queries and additional ones about recent work, but also reveal limitations in automated status judgments, leading to a design recommendation that status changes must be proposed with evidence and reviewed by humans.

By Madhulatha Mandarapu, Sandeep Kunkunuru
arXiv AI
Aug 3

An Ontology-Guided, Deduplication-Aware Extraction Layer for Knowledge Graph Construction from Heterogeneous Documents

arXiv:2607. 28662v1 Announce Type: new Abstract: Large language models extract entities and relationships from unstructured documents fluently but inconsistently: type vocabularies fracture across documents, the same person surfaces under several name variants, relationships duplicate, and distinct individuals who share a name risk silent conflation.

By Vaibhav Dangaich, Kevin Lewis, Kundeshwar Pundalik
arXiv AI
Oct 31

Beyond Similarity Search: Knowledge Graph Metadata Enrichment for Enterprise Retrieval-Augmented Generation

The paper investigates whether a knowledge graph can enhance retrieval for enterprise retrieval‑augmented generation (RAG) systems compared to traditional similarity search. Using a snapshot of SEC EDGAR filings, the authors built a graph with 430,257 nodes and 561,144 edges, then evaluated three architectures—text‑to‑SQL, dense‑vector RAG, and a two‑stage GraphRAG. GraphRAG outperformed the other methods, correctly answering 16 of 20 questions across all attempts, while the other approaches lagged behind, especially in structured aggregation and single‑document queries.

By Fakabbir Amin