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New framework tackles complex knowledge graph queries with multiple variables

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called Neural Scalable Symbolic Search (NS3) to address the challenge of complex query answering over knowledge graphs. Existing methods struggle with queries involving multiple free variables, often relying on less accurate marginal rankings. NS3 approximates joint rankings by first answering marginalized sub-queries, then merging variables into pruned domains controlled by a dynamic budget, and progressively reducing the query complexity. AI

IMPACT Introduces a novel approach for more accurate joint ranking in complex knowledge graph queries, potentially improving AI reasoning capabilities.

RANK_REASON The cluster contains an academic paper detailing a new framework for complex query answering. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework tackles complex knowledge graph queries with multiple variables

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The cluster contains an academic paper detailing a new framework for complex query answering. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Neural Scalable Symbolic Search Framework for Complex Logical Queries with Multiple Free Variables

    Complex Query Answering (CQA) is a fundamental knowledge representation and reasoning task over incomplete knowledge graphs (KGs). Answering existential first-order queries with $k$ free variables (i.e., $\text{EFO}_k$ queries) is a crucial yet challenging problem, as it requires…

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