A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and their Usage

TL;DR

This paper introduces a scalable, practical ontology for modeling scholarly artifacts and their usage, enabling large-scale analysis of scholarly data while addressing privacy and data archiving challenges.

Contribution

It presents a novel, scalable ontology tailored for large-scale scholarly data, supporting usage research and computational analysis of up to 50 million articles and 1 billion usage events.

Findings

Ontology supports large-scale data modeling

Enables statistical analysis of scholarly usage

Provides inference rules for artifact metrics

Abstract

The large-scale analysis of scholarly artifact usage is constrained primarily by current practices in usage data archiving, privacy issues concerned with the dissemination of usage data, and the lack of a practical ontology for modeling the usage domain. As a remedy to the third constraint, this article presents a scholarly ontology that was engineered to represent those classes for which large-scale bibliographic and usage data exists, supports usage research, and whose instantiation is scalable to the order of 50 million articles along with their associated artifacts (e.g. authors and journals) and an accompanying 1 billion usage events. The real world instantiation of the presented abstract ontology is a semantic network model of the scholarly community which lends the scholarly process to statistical analysis and computational support. We present the ontology, discuss its instantiation, and provide some example inference rules for calculating various scholarly artifact metrics.