arXiv AI
OntoKG-EQ: A provenance-grounded, competency-question-governed knowledge graph for auditable analyst querying
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arXiv AI
Aug 24
Auditable by Construction: An Ontology-Driven Framework for Trustworthy LLM Analytics in Enterprise Finance
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.
arXiv AI
3d ago
FinMultiHopQA: A KG-Grounded Multi-Hop Financial QA Benchmark for Evidence-Driven Reasoning
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.
arXiv AI
Oct 31
What Does an Ontology Catch? Curated, LLM-Generated and Data-Derived Ontologies as Checkers of LLM Agents over Knowledge Graphs
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.
arXiv AI
Aug 25
Composable Trust Infrastructure for Manufacturing Knowledge Graphs: Cross-System Provenance, Temporal Reasoning, and Decision Traceability
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...
arXiv AI
Aug 12
ENTLORE: A Graph-Grounded Benchmark for Latent Organizational Reasoning in Enterprise Question Answering
arXiv:2608. 10679v1 Announce Type: cross Abstract: Enterprise question answering is framed as retrieving internal documents and generating grounded answers.
arXiv AI
Jun 16
Provenance-Enhanced Statements in Knowledge Graphs
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}).