A framework for constructing a huge name disambiguation dataset: algorithms, visualization and human collaboration

TL;DR

This paper introduces a large, high-accuracy author name disambiguation dataset called WhoisWho, along with a collaborative annotation framework and an inductive disambiguation model, advancing research in author disambiguation.

Contribution

It presents a novel large-scale dataset, an efficient human-computer annotation framework, and a new inductive model that outperforms existing methods in author name disambiguation.

Findings

The proposed model outperforms other algorithms on WhoisWho.

The annotation framework improves accuracy and efficiency.

Author disambiguation remains a challenging problem.

Abstract

We present a manually-labeled Author Name Disambiguation(AND) Dataset called WhoisWho, which consists of 399,255 documents and 45,187 distinct authors with 421 ambiguous author names. To label such a great amount of AND data of high accuracy, we propose a novel annotation framework where the human and computer collaborate efficiently and precisely. Within the framework, we also propose an inductive disambiguation model to classify whether two documents belong to the same author. We evaluate the proposed method and other state-of-the-art disambiguation methods on WhoisWho. The experiment results show that: (1) Our model outperforms other disambiguation algorithms on this challenging benchmark. (2) The AND problem still remains largely unsolved and requires more in-depth research. We believe that such a large-scale benchmark would bring great value for the author name disambiguation task. We also conduct several experiments to prove our annotation framework could assist annotators to make accurate results efficiently and eliminate wrong label problems made by human annotators effectively.