arXiv:2606.00203v1 Announce Type: new Abstract: Dominant approaches to Knowledge Base Question Answering (KBQA) fall into two categories. First is the generation of a formal query that suffers from brittleness and limited explainability, and the second is direct answer retrieval through KB exploration that is computationally costly and prone to hallucination. To combine the strengths of both paradigms while mitigating their respective weaknesses, we introduce DeSQ (Decomposition-based SPARQL Query Generation), a KB-agnostic framework that operates in three stages. First, it decomposes complex
The increasing complexity of knowledge bases and the limitations of current KBQA approaches (brittleness, hallucination, computational cost) necessitate new, more robust methodologies.
Improving the accuracy and explainability of knowledge base question answering is crucial for advancing AI's ability to interact with and utilize vast structured data, impacting various applications from research to enterprise solutions.
This new framework offers a path to combine the strengths of query generation and direct answer retrieval, potentially making knowledge base querying more resilient, transparent, and computationally efficient.
- · AI developers
- · Data scientists
- · Knowledge base heavy industries
- · Enterprises adopting AI
- · Inefficient KBQA systems
- · Companies reliant on brittle query generation
- · Organizations with high hallucination rates
More accurate and explainable AI systems for querying structured data will emerge, reducing development time and improving user trust.
This could accelerate the adoption of knowledge graphs and structured data in complex AI applications, leading to richer, more reliable intelligent agents.
Increased reliability in data extraction and reasoning from knowledge bases might enable new autonomous AI agents that can operate with greater precision and less human oversight.
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Read at arXiv cs.CL