Entity search by leveraging attributive terms in sentential queries over RDF data

Abstract

This paper proposes methods of finding a ranked list of entities using RDF data for a given sentential query (e.g. Cars 3, Toy Story 4, or Incredibles 2 for the query animated films pixar) by leveraging different types of modifiers in the query through identifying corresponding properties (e.g. released and movie type for the modifiers upcoming and animated, respectively). While major search engines provide the entity search functionality that returns a list of entities based on users' queries, entities are neither presented for a wide variety of search queries, nor in the order that users expect. To enhance the efficiency of entity search, we propose two entity ranking methods. Our first proposed method is a Web-based entity ranking that directly finds highly relevant entities from Web search results returned in response to the query as a whole, and propagates the estimated relevance to the other entities. The second proposed method is a property-based entity ranking that ranks entities based on properties corresponding to modifier terms in the query. To this end, we propose a novel method that identifies a set of relevant properties based on the combination of the frequency of property values containing the modifier, co-occurrence of the modifier and property names, and difference in property value distributions of entities in the search results for a query. The experimental results showed that our proposed property identification method could predict more relevant properties than using each criterion separately. Moreover, we achieved the best performance for returning a ranked list of relevant entities when using both of the Web-based and property-based entity ranking methods.