arXiv:2508.13663v5 Announce Type: replace-cross Abstract: Methods for query answering over incomplete knowledge graphs retrieve entities that are likely to be answers, which is particularly useful when such answers cannot be reached by direct graph traversal due to missing edges. However, existing approaches have focused on queries formalized using first-order-logic. In practice, many real-world queries involve constraints that are inherently vague or context-dependent, such as preferences for attributes or related categories. Addressing this gap, we introduce the problem of query answering wi
The proliferation of knowledge graphs and increasingly complex AI systems necessitates more sophisticated and human-like query capabilities, moving beyond rigid logical constraints.
This development allows AI systems to interpret ambiguous human intent, leading to more flexible and powerful knowledge graph interactions crucial for advanced AI applications.
AI systems can now process queries with 'soft' or vague human constraints, enabling more intuitive and context-aware data retrieval from knowledge graphs.
- · AI developers
- · Knowledge graph providers
- · Enterprises leveraging AI for data analysis
Improved accuracy and utility of AI systems when interacting with diverse and complex datasets.
Faster development and deployment of intelligent agents capable of nuanced decision-making based on incomplete or vaguely defined inputs.
Enhanced automation of complex tasks requiring human-like understanding of context and preference, potentially accelerating the adoption of agentic AI across sectors.
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Read at arXiv cs.LG