Supporting Named Entity Recognition and Document Classification in a Knowledge Management System for Applied Gaming

Philippe Tamla, Florian Freund, Matthias Hemmje

2020

Abstract

In this research paper, we present a system for named entity recognition and automatic document classification in an innovative knowledge management system for Applied Gaming. The objective of this project is to facilitate the management of machine learning-based named entity recognition models, that can be used for both: extracting different types of named entities and classifying textual documents from heterogeneous knowledge sources on the Web. We present real-world use case scenarios and derive features for training and managing NER models with the Stanford NLP machine learning API. Then, the integration of our developed NER system with an expert rule-based system is presented, which allows an automatic classification of textual documents into different taxonomy categories available in the knowledge management system. Finally, we present the results of a qualitative evaluation that was conducted to optimize the system user interface and enable a suitable integration into the target system.

Paper Citation

in Harvard Style

Tamla P., Freund F. and Hemmje M. (2020). Supporting Named Entity Recognition and Document Classification in a Knowledge Management System for Applied Gaming. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 2: KEOD; ISBN 978-989-758-474-9, SciTePress, pages 108-121. DOI: 10.5220/0010145001080121

in Bibtex Style

@conference{keod20,
author={Philippe Tamla and Florian Freund and Matthias Hemmje},
title={Supporting Named Entity Recognition and Document Classification in a Knowledge Management System for Applied Gaming},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 2: KEOD},
year={2020},
pages={108-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010145001080121},
isbn={978-989-758-474-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 2: KEOD
TI - Supporting Named Entity Recognition and Document Classification in a Knowledge Management System for Applied Gaming
SN - 978-989-758-474-9
AU - Tamla P.
AU - Freund F.
AU - Hemmje M.
PY - 2020
SP - 108
EP - 121
DO - 10.5220/0010145001080121
PB - SciTePress