Navigating Taxonomic Expansions of Entity Sets Driven by Knowledge Bases

TL;DR

This paper introduces a method for navigating taxonomic expansions of entity sets using expansion graphs, enabling efficient local reasoning without full graph materialization, thus improving knowledge base applications.

Contribution

It formalizes reasoning tasks for entity set expansion graphs, allowing practical, incremental navigation under realistic constraints.

Findings

Reasoning tasks can be efficiently implemented with bounded input or entity descriptions.

Local navigation of expansion graphs is feasible without full graph materialization.

Supports practical applications in knowledge base expansion and entity recognition.

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.