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
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
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
arXiv:2606. 05415v1 Announce Type: cross Abstract: Real-world data spans tables, documents, and semi-structured files with implicit semantics.
By Padmaja Jonnalagedda, Yuguang Yao, Xiang Gao, Hilaf Hasson, Kamalika Das
arXiv:2607. 25959v1 Announce Type: cross Abstract: Wikipedia and Wikidata are widely used for information access, LLM pre-training, and retrieval-augmented generation.
By Fanfu Wei, Thibault Ehrhart, Rapha\"el Troncy
As "AI Scientists" emerge to drive research via the Model Context Protocol (MCP), systems relying on ephemeral scripts will fail. The sheer scale of stateful, interconnected evidence requires a machine-walkable warranty grounded in a purpose-built database architecture.
arXiv:2605. 26874v2 Announce Type: replace-cross Abstract: LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores.
By Madhulatha Mandarapu, Sandeep Kunkunuru