Research Project: Text Engineering Tool for Ontological Scientometry

TL;DR

This paper proposes a semi-automatic text engineering tool that categorizes scientific papers and extracts relationships to build an ontological database, aiming to improve discoverability and analysis of scientific literature.

Contribution

It introduces a novel semi-automatic categorization and relationship-extraction system for scientific papers, integrating human correction via a Wikipedia-like interface.

Findings

Conceptual framework for categorizing papers by contribution type

Design of a semi-automatic relationship extraction process

Potential to enhance literature discovery and funding analysis

Abstract

The number of scientific papers grows exponentially in many disciplines. The share of online available papers grows as well. At the same time, the period of time for a paper to loose at chance to be cited anymore shortens. The decay of the citing rate shows similarity to ultradiffusional processes as for other online contents in social networks. The distribution of papers per author shows similarity to the distribution of posts per user in social networks. The rate of uncited papers for online available papers grows while some papers 'go viral' in terms of being cited. Summarized, the practice of scientific publishing moves towards the domain of social networks. The goal of this project is to create a text engineering tool, which can semi-automatically categorize a paper according to its type of contribution and extract relationships between them into an ontological database. Semi-automatic categorization means that the mistakes made by automatic pre-categorization and relationship-extraction will be corrected through a wikipedia-like front-end by volunteers from general public. This tool should not only help researchers and the general public to find relevant supplementary material and peers faster, but also provide more information for research funding agencies.