Generating Categories for Sets of Entities

TL;DR

This paper introduces a neural network-based method to automatically generate and rank category suggestions for entity sets, aiding knowledge base expansion and organization.

Contribution

It presents a novel approach combining neural abstractive summarization with hierarchical ranking to improve category generation for knowledge bases.

Findings

Effective candidate category generation demonstrated on Wikipedia data

Improved ranking accuracy using structure, content, and hierarchy features

Enhanced support for knowledge editors in expanding category systems

Abstract

Category systems are central components of knowledge bases, as they provide a hierarchical grouping of semantically related concepts and entities. They are a unique and valuable resource that is utilized in a broad range of information access tasks. To aid knowledge editors in the manual process of expanding a category system, this paper presents a method of generating categories for sets of entities. First, we employ neural abstractive summarization models to generate candidate categories. Next, the location within the hierarchy is identified for each candidate. Finally, structure-, content-, and hierarchy-based features are used to rank candidates to identify by the most promising ones (measured in terms of specificity, hierarchy, and importance). We develop a test collection based on Wikipedia categories and demonstrate the effectiveness of the proposed approach.