Leveraging Usage Data for Linked Data Movie Entity Summarization

TL;DR

This paper presents a novel method for movie entity summarization in linked data by leveraging usage data to identify significant features through similarity measures and a TF-IDF-like downgrading factor.

Contribution

It introduces a usage data-driven approach for entity summarization in the movie domain, utilizing similarity and feature importance measures to improve summaries.

Findings

Effective identification of important features for movies

Improved entity similarity measurement using usage data

Enhanced summarization quality demonstrated on linked movie data

Abstract

Novel research in the field of Linked Data focuses on the problem of entity summarization. This field addresses the problem of ranking features according to their importance for the task of identifying a particular entity. Next to a more human friendly presentation, these summarizations can play a central role for semantic search engines and semantic recommender systems. In current approaches, it has been tried to apply entity summarization based on patterns that are inherent to the regarded data. The proposed approach of this paper focuses on the movie domain. It utilizes usage data in order to support measuring the similarity between movie entities. Using this similarity it is possible to determine the k-nearest neighbors of an entity. This leads to the idea that features that entities share with their nearest neighbors can be considered as significant or important for these entities. Additionally, we introduce a downgrading factor (similar to TF-IDF) in order to overcome the high number of commonly occurring features. We exemplify the approach based on a movie-ratings dataset that has been linked to Freebase entities.