🤖 AI Summary
This work addresses the challenge of compiling natural language queries into backend query languages in document-centric, hybrid, and heterogeneous data environments, where semantic intent is often ambiguous or incomplete. The authors propose the NLIQ framework, which introduces a “goal sufficiency” criterion to classify queries according to their semantic determinacy. It emphasizes that when intermediate goals must be dynamically constructed, intermediate representations should serve as core semantic objects rather than mere syntactic intermediaries. Through conceptual analysis, case modeling, and formal categorization, the study establishes a unified query paradigm that integrates goal recognition, intermediate representation design, and heterogeneous execution. This framework provides a theoretical foundation for natural language querying in complex data settings and opens new research directions in semantic goal construction, heterogeneous compilation, and answer generation.
📝 Abstract
Natural-language-initiated querying is usually framed as translation into a predetermined backend language such as SQL, Cypher, or SPARQL. That framing is appropriate when the semantic target is known in advance, but it does not cover the full space of natural-language query workloads. In document-centric, mixed, and heterogeneous environments, the first semantic problem may be to determine what target should be constructed before backend-specific execution can begin. This paper proposes the $\textit{NLIQ}~$ lens for this broader space. It introduces target adequacy as the criterion for distinguishing settings in which the target is given, only partially specified, or must itself be constructed, and argues that intermediate representations in the latter regimes are not merely implementation devices but first-class semantic objects. The paper develops a compact framework of $\textit{NLIQ}~$ regimes, illustrates the distinction through representative examples, and identifies a new research terrain around semantic target formation, intermediate representation design, heterogeneous compilation, and answer formation in complex data environments.