Prioritizing and Scheduling Conferences for Metadata Harvesting in dblp

TL;DR

This paper proposes a method for prioritizing conferences for metadata harvesting in digital libraries by evaluating various ranking features to optimize data source selection.

Contribution

It introduces a novel approach to conference prioritization using a broad definition of information quality and evaluates it with pseudo-relevance assessments.

Findings

Certain ranking features significantly improve conference selection accuracy.

The proposed approach outperforms baseline methods in identifying promising data sources.

Component-based evaluation confirms the effectiveness of the feature set.

Abstract

Maintaining literature databases and online bibliographies is a core responsibility of metadata aggregators such as digital libraries. In the process of monitoring all the available data sources the question arises which data source should be prioritized. Based on a broad definition of information quality we are looking for different ways to find the best fitting and most promising conference candidates to harvest next. We evaluate different conference ranking features by using a pseudo-relevance assessment and a component-based evaluation of our approach.