Entity Ranking in Wikipedia

TL;DR

This paper presents an approach for identifying and ranking entities within Wikipedia documents, leveraging Wikipedia's link structure and categories to improve information retrieval effectiveness.

Contribution

The paper introduces a novel entity ranking system that utilizes Wikipedia's features and proposes a methodology for evaluation, demonstrating improved retrieval performance.

Findings

Categories and link structure significantly enhance retrieval effectiveness

Utilizing entity examples improves ranking accuracy

Proposed system architecture effectively identifies and ranks entities

Abstract

The traditional entity extraction problem lies in the ability of extracting named entities from plain text using natural language processing techniques and intensive training from large document collections. Examples of named entities include organisations, people, locations, or dates. There are many research activities involving named entities; we are interested in entity ranking in the field of information retrieval. In this paper, we describe our approach to identifying and ranking entities from the INEX Wikipedia document collection. Wikipedia offers a number of interesting features for entity identification and ranking that we first introduce. We then describe the principles and the architecture of our entity ranking system, and introduce our methodology for evaluation. Our preliminary results show that the use of categories and the link structure of Wikipedia, together with entity examples, can significantly improve retrieval effectiveness.