Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering
arXiv:2605.27164v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems for question answering typically retrieve evidence by semantic similarity between the query and document chunks. While effective for unstructured text, this approach is less reliable on semi-structured corpora where answering may require exact filtering, aggregation, or exhaustive retrieval over structured attributes across multiple documents. Symbolic approaches support such operations, but they are often brittle on noisy natural-language corpora. We address this gap with DualGraph, a RAG framework th
The proliferation of RAG systems highlights current limitations in handling semi-structured data, driving innovation towards hybrid approaches that combine semantic retrieval with symbolic reasoning.
This development addresses a critical weakness in current AI systems, enabling more accurate and robust question answering over complex, real-world datasets that are often semi-structured.
The ability to integrate symbolic processing with semantic retrieval within RAG frameworks significantly expands the types of data and queries AI systems can effectively handle, moving beyond purely unstructured text.
- · Enterprises with complex databases
- · AI platform developers
- · Data scientists
- · Knowledge management systems
- · Purely semantic RAG approaches for structured data
- · Systems highly dependent on perfectly clean, unstructured data
Improved accuracy and utility of AI agents and knowledge systems in environments with diverse data types.
Accelerated adoption of AI for tasks requiring deep understanding and reasoning over complex, semi-structured corporate or scientific data.
Enhanced automation of workflows that currently require manual interpretation of reports, databases, and other non-standardized information.
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Read at arXiv cs.AI