Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering
📰 ArXiv cs.AI
Learn to improve question answering on semi-structured data using a combination of symbolic and semantic methods
Action Steps
- Build a dataset for semi-structured question answering using a combination of symbolic and semantic methods
- Run experiments to compare the performance of symbolic and semantic approaches on semi-structured data
- Configure a Retrieval-Augmented Generation (RAG) system to incorporate symbolic querying
- Test the system on a semi-structured dataset to evaluate its performance
- Apply the method to real-world applications such as question answering on databases or knowledge graphs
Who Needs to Know This
NLP engineers and researchers can benefit from this method to enhance their question answering systems, especially when dealing with semi-structured data
Key Insight
💡 Combining symbolic and semantic methods can improve question answering on semi-structured data
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Improve question answering on semi-structured data with a combo of symbolic & semantic methods #NLP #RAG
Key Takeaways
Learn to improve question answering on semi-structured data using a combination of symbolic and semantic methods
Full Article
Title: Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering
Abstract:
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 oft
Abstract:
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 oft
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