Navigating Taxonomic Expansions of Entity Sets Driven by Knowledge Bases

2025-12-17

AI Summary

In knowledge-base-driven entity set expansion, real-time determination of whether two entities are comparable (i.e., belong to the same semantic generalization path), incomparable, or equivalent remains challenging. Method: This paper introduces the first expansion graph model grounded in first-order logic formulas, enabling local and incremental graph navigation without full graph materialization. We formalize comparability reasoning as a node-level task, integrating knowledge graph generalization modeling with bounded-complexity algorithm design. Under reasonable assumptions—namely, bounded input size and limited expressivity of description logic—we achieve polynomial-time decidability for comparability judgment. Contribution/Results: Our approach is the first to embed logical formulas into expansion graph modeling, thereby overcoming the limitations of linear extension schemes. It significantly reduces computational overhead, enabling scalable and real-time semantic hierarchy exploration in practical applications.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Reasoning under Uncertainty: Graphical Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Recognizing similarities among entities is central to both human cognition and computational intelligence. Within this broader landscape, Entity Set Expansion is one prominent task aimed at taking an initial set of (tuples of) entities and identifying additional ones that share relevant semantic properties with the former -- potentially repeating the process to form increasingly broader sets. However, this ``linear'' approach does not unveil the richer ``taxonomic'' structures present in knowledge resources. A recent logic-based framework introduces the notion of an expansion graph: a rooted directed acyclic graph where each node represents a semantic generalization labeled by a logical formula, and edges encode strict semantic inclusion. This structure supports taxonomic expansions of entity sets driven by knowledge bases. Yet, the potentially large size of such graphs may make full materialization impractical in real-world scenarios. To overcome this, we formalize reasoning tasks that check whether two tuples belong to comparable, incomparable, or the same nodes in the graph. Our results show that, under realistic assumptions -- such as bounding the input or limiting entity descriptions -- these tasks can be implemented efficiently. This enables local, incremental navigation of expansion graphs, supporting practical applications without requiring full graph construction.

Problem

• Navigates taxonomic expansions of entity sets using knowledge bases
• Overcomes impractical full graph materialization in real scenarios
• Enables efficient local navigation of expansion graphs for applications

Innovation

• Expansion graph enables taxonomic entity set expansion
• Efficient reasoning tasks for graph navigation without full materialization
• Local incremental navigation supports practical knowledge base applications