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.
arXiv:2607. 00828v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows.
arXiv:2607. 17963v1 Announce Type: new Abstract: Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete.
The paper presents an end‑to‑end pipeline for translating natural language planning descriptions into PDDL problem instances using large language models. It incorporates multiple checks—syntactic parsing, planner success, domain conformance, an LLM critic, and iterative repair—to ensure faithfulness to the original task. Experiments on Planetarium, AutoPlanBench, and curated PDDL~2.1 problems reveal that operational success can diverge from benchmark‑reference reconstruction, and that structured repair improves outcomes while PDDL~2.1 remains challenging for reference reconstruction.
Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows. Although recent advances have substantially improved workflow generation and execution, the semantic information required to operationalize analytical concepts often lies beyond what is explicitly represented in database schemas and data values.
arXiv:2610.33949v1 Announce Type: cross Abstract: Pipelines that require structured outputs from hosted language models often specify a Pydantic schema in the prompt, validate the response, and retry...
arXiv:2608.30250v1 Announce Type: new Abstract: This paper addresses the problem of translating natural-language routing rules written by business administrators into executable workflow graphs for e...
arXiv:2610.35054v1 Announce Type: new Abstract: Transportation agencies maintain extensive crash databases to identify safety priorities, evaluate countermeasures, and guide resource allocation. Maki...
The paper introduces Structured Four-Stage Legal Translation (S4L→Prolog), a reasoning-guided framework that converts raw traffic rules into Prolog logic by performing semantic role extraction, scene completion, logical mapping, and rule generation in a single prompt. Compared to baseline approaches (NL→Prolog and LE→Prolog), S4L achieves higher accuracy—formalizing 75 % of twenty real-world traffic rules versus 60 % and 55 % for the baselines. Qualitative analysis shows S4L better captures implicit causal relations, deontic modality, and exception structures.
arXiv:2606. 24619v1 Announce Type: new Abstract: Competency Questions (CQs) are the central component of CQ-verification, an established process in which an ontology is evaluated against a set of natural language questions to determine whether the intended purpose of the ontology has been properly modelled.
arXiv:2609.14652v1 Announce Type: cross Abstract: Large Language Model (LLM) applications often transfer domain concepts into the model's context informally, through prompt prose, schema dumps, and e...
The paper proposes 5W1H+Which, a semantic indexing framework that separates source‑grounded content extraction from progressive ontology binding. It organizes content into independently addressable units based on the 5W1H questions, while the "Which" component records links to versioned ontology elements, mapping relations, applicability scopes, and validation states. The design tracks business‑valid time, system knowledge time, and operational traces to enable revalidation and incremental maintenance, and illustrates its use with cloud migration and temporal policy examples.