Exploring semantically-related concepts from Wikipedia: the case of SeRE
This work addresses the need for better semantic exploration tools for users like researchers or information seekers, but it is incremental as it builds on existing data sources and methods.
The authors tackled the problem of exploring semantically related concepts by developing SeRE, a web application that uses Wikipedia and DBpedia to extract and rank entities based on semantic relatedness from full text, resulting in a visualization of relevant concepts with explanatory snippets. In a user study, they examined SeRE's effectiveness in finding important entities and relationships, and how classification systems can be used for filtering.
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.