Exploring semantically-related concepts from Wikipedia: the case of SeRE

TL;DR

SeRE is a web application that leverages Wikipedia and DBpedia to explore and visualize semantically related concepts, aiding users in understanding complex topics through related entities and their connections.

Contribution

The paper introduces SeRE, a novel tool that computes semantic relatedness using Wikipedia full texts and visualizes related concepts with explanatory snippets.

Findings

SeRE effectively identifies relevant related entities for given topics.

User study shows SeRE helps users discover important concepts and relationships.

The system supports filtering and classification for improved exploration.

Abstract

In this paper we present our web application SeRE designed to explore semantically related concepts. Wikipedia and DBpedia are rich data sources to extract related entities for a given topic, like in- and out-links, broader and narrower terms, categorisation information etc. We use the Wikipedia full text body to compute the semantic relatedness for extracted terms, which results in a list of entities that are most relevant for a topic. For any given query, the user interface of SeRE visualizes these related concepts, ordered by semantic relatedness; with snippets from Wikipedia articles that explain the connection between those two entities. In a user study we examine how SeRE can be used to find important entities and their relationships for a given topic and to answer the question of how the classification system can be used for filtering.