Bibliographic Classification using the ADS Databases

TL;DR

This paper presents two techniques for classifying bibliographic records in the ADS databases, one based on text content analysis and the other on citation relationships, improving discipline assignment accuracy.

Contribution

It introduces two novel classification methods for bibliographic records, enhancing the organization and retrieval within the ADS astrophysics database.

Findings

Both techniques effectively classify records by subject and database relevance.

Methods improve accuracy of discipline and database assignment.

Tools assist in managing large bibliographic collections.

Abstract

We discuss two techniques used to characterize bibliographic records based on their similarity to and relationship with the contents of the NASA Astrophysics Data System (ADS) databases. The first method has been used to classify input text as being relevant to one or more subject areas based on an analysis of the frequency distribution of its individual words. The second method has been used to classify existing records as being relevant to one or more databases based on the distribution of the papers citing them. Both techniques have proven to be valuable tools in assigning new and existing bibliographic records to different disciplines within the ADS databases.