Notable Characteristics Search through Knowledge Graphs

TL;DR

This paper introduces a probabilistic method for identifying notable, surprising characteristics of entities in knowledge graphs to enhance query explanations and user understanding.

Contribution

It presents a novel probabilistic approach for detecting notable characteristics by comparing entities and analyzing attribute distributions within knowledge graphs.

Findings

Successfully retrieves similar entities for comparison.

Effectively identifies interesting and relevant notable characteristics.

Preliminary experiments validate the approach's robustness.

Abstract

Query answering routinely employs knowledge graphs to assist the user in the search process. Given a knowledge graph that represents entities and relationships among them, one aims at complementing the search with intuitive but effective mechanisms. In particular, we focus on the comparison of two or more entities and the detection of unexpected, surprising properties, called notable characteristics. Such characteristics provide intuitive explanations of the peculiarities of the selected entities with respect to similar entities. We propose a solid probabilistic approach that first retrieves entity nodes similar to the query nodes provided by the user, and then exploits distributional properties to understand whether a certain attribute is interesting or not. Our preliminary experiments demonstrate the solidity of our approach and show that we are able to discover notable characteristics that are indeed interesting and relevant for the user.