Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering
arXiv:2502.11491v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in natural language processing. However, in knowledge graph question answering tasks (KGQA), there remains the issue of answering questions that require multi-hop reasoning. Existing methods rely on entity vector matching, but the purpose of the question is abstract and difficult to match with specific entities. As a result, it is difficult to establish reasoning paths to the purpose, which leads to information loss and redundancy. To address this issue, inspired by human r
The continuous improvement of LLMs and increasing demand for accurate, multi-hop reasoning over complex knowledge graphs are driving innovation in KGQA techniques.
Improving LLM capabilities in knowledge graph question answering unlocks more reliable AI applications for complex data analysis, potentially accelerating research and development across various sectors.
New methodologies like Ontology-Guided Reverse Thinking offer a more robust approach to multi-hop reasoning, mitigating issues of information loss and redundancy in existing LLM-based KGQA systems.
- · AI developers
- · Data analytics companies
- · Researchers
- · Enterprises with complex data
- · LLMs with rudimentary KGQA capabilities
- · Traditional semantic search engines
- · Methods solely relying on entity vector matching
LLMs will become more adept at answering complex, inferential questions from structured data.
This improved accuracy will lead to more trustworthy AI agents capable of higher-level decision support directly from knowledge bases.
Enhanced KGQA could accelerate scientific discovery and enterprise automation by allowing AIs to synthesize insights from vast and interconnected knowledge graphs more effectively.
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Read at arXiv cs.AI