arXiv:2606. 08477v1 Announce Type: new Abstract: Knowledge extraction from symbolic data often produces abstractions that are formally defined but not immediately interpretable by users.
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The paper examines convergence problems in Relational Concept Analysis (RCA) when applied to AOC-posets instead of full concept lattices. It explains why RCA’s iterative process may fail to converge in the AOC-poset setting, identifies conditions that can still guarantee convergence, and proposes a convergent variant that preserves the AOC-poset structure by never removing relational attributes. The study also discusses data transformations that can restore convergence.
arXiv:2607. 01773v1 Announce Type: new Abstract: Ontology construction requires deciding which objects, attributes, and structural relations should be accepted as valid knowledge.
Ontology construction requires deciding which objects, attributes, and structural relations should be accepted as valid knowledge. Language models can propose such structures from text, but their outputs can still be unsupported or inconsistent.
The article examines how Large Language Models can aid in creating Entity-Relationship diagrams from natural language requirements. It tests three LLMs with three prompting strategies—Zero-Shot, Chain of Thought, and Chain of Thought + Verifier—on scenarios of increasing complexity. Findings show that while LLMs perform adequately on simpler tasks, their reliability drops with more complex requirements, leading to inconsistencies, ambiguities, and constraint representation failures.
arXiv:2607. 29553v1 Announce Type: new Abstract: Organizations increasingly define operational metrics in structured, machine-readable formats to monitor systems, processes, and compliance.
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.