Does Wikidata Support Analogical Reasoning?

TL;DR

This paper investigates Wikidata's potential for analogical reasoning, revealing inconsistencies and the need for manual effort, while proposing metrics for automatic analogy extraction to support future research.

Contribution

It is the first to analyze Wikidata's relational knowledge for analogical reasoning and proposes metrics for automatic analogy extraction from Wikidata.

Findings

Relational knowledge in Wikidata is often inconsistent or incomplete.

Wikidata can be used for analogy classification with manual effort.

Proposed metrics can guide automatic analogy extraction from Wikidata.

Abstract

Analogical reasoning methods have been built over various resources, including commonsense knowledge bases, lexical resources, language models, or their combination. While the wide coverage of knowledge about entities and events make Wikidata a promising resource for analogical reasoning across situations and domains, Wikidata has not been employed for this task yet. In this paper, we investigate whether the knowledge in Wikidata supports analogical reasoning. Specifically, we study whether relational knowledge is modeled consistently in Wikidata, observing that relevant relational information is typically missing or modeled in an inconsistent way. Our further experiments show that Wikidata can be used to create data for analogy classification, but this requires much manual effort. To facilitate future work that can support analogies, we discuss key desiderata, and devise a set of metrics to guide an automatic method for extracting analogies from Wikidata.