The paper introduces the Knowledge-Driven Analytics Framework (KDAF), an ontology‑driven approach for large language model analytics in enterprise finance that prioritizes auditability. KDAF constructs knowledge systems through six iterative stages and retrieves evidence via Context‑Aware Relevance Propagation (CARP), ensuring each fact includes its relationship type, confidence, and source lineage. Evaluation on FinanceBench shows that while retrieval improves accuracy marginally, KDAF significantly outperforms other methods in citation traceability and provenance completeness, demonstrating that auditability is the key advantage of ontology‑grounded retrieval.
By Sergiy Lunyakin
FinMultiHopQA is a new benchmark comprising 15,600 financial questions that require multi‑hop reasoning across filing sections, fiscal years, and peer companies. It is grounded in FinReflectKG, a temporally indexed financial knowledge graph with over 17.5 million triples derived from SEC 10‑K filings. The benchmark uniquely supports controlled multi‑hop taxonomies, document topologies, motif‑level reproducibility, and provenance‑linked auditability, and it allows researchers to test how different evidence presentations affect large language model performance.
By Abhinav Arun, Reetu Raj Harsh, Bhaskarjit Sarmah, Stefano Pasquali
The paper investigates how different types of ontologies—curated, LLM-generated, and data-derived—serve as checkers for large language model agents answering questions over knowledge graphs. By evaluating these ontologies as prompt context, as checkers, or both, across both open-weight and proprietary agents on public and a large private graph, the study quantifies the trade-offs between errors caught and false alarms. It finds that curated ontologies can flag more correct answers than other sources but are only marginally more precise than random, and that combining sources or providing specific feedback offers limited benefits.
By Michal Orzechowski, Rund Tawfiq, Tomasz Kosciolek
arXiv:2608.21418v1 Announce Type: new
Abstract: Manufacturing knowledge graphs that integrate data from heterogeneous industrial systems face a trust deficit: consumers cannot determine whether queri...
By Grama Chethan
arXiv:2608. 10679v1 Announce Type: cross Abstract: Enterprise question answering is framed as retrieving internal documents and generating grounded answers.
By Akrin Zheng, Alexander Wu, Alaia Liu
arXiv:2606. 15246v1 Announce Type: cross Abstract: Provenance-enhanced statements of the form "according to $X$, $\varphi$" are pervasive in contemporary knowledge graphs, especially in domains where graph content primarily represents claims, interpretations, and hypotheses (\emph{capta}) rather than observer-independent facts (\emph{data}).
By Fabio Vitali, Valentina Pasqual
Enterprise question answering is framed as retrieving internal documents and generating grounded answers. Routine enterprise records, however, are work by-products in which required organizational relations remain implicit across heterogeneous sources.
arXiv:2608.28594v1 Announce Type: new
Abstract: Conversational analytics systems assume the user already has a well-formed question, leaving a non-expert facing a blank query box on an unfamiliar ent...
By Harmohit Singh, Rahul Sharma
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
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:2608. 06331v1 Announce Type: cross Abstract: From natural-language query interfaces to automated report generation, data analysis tools need a description of the data: the real-world entities it contains, which columns function as measures or identifiers, and how tables connect into units of analysis.
By Donna Hooshmand, Shubham Shahi, Cameron Barrie, Abhratanu Dutta, Marko Sterbentz, Harper Pack, Kristian J. Hammond
arXiv:2608.28642v1 Announce Type: new
Abstract: Knowledge graphs used by agentic systems are often treated as flat stores of extracted triples, with little record of who owns a fact, why it was admit...
By Pranav Bykampadi, Neel Mokaria, Vishesh Narayan, Faizan Wajid, Ashok Agrawala