Unsupervised Descriptive Text Mining for Knowledge Graph Learning

Giacomo Frisoni, Gianluca Moro, Antonella Carbonaro

2020

Abstract

The use of knowledge graphs (KGs) in advanced applications is constantly growing, as a consequence of their ability to model large collections of semantically interconnected data. The extraction of relational facts from plain text is currently one of the main approaches for the construction and expansion of KGs. In this paper, we introduce a novel unsupervised and automatic technique of KG learning from corpora of short unstructured and unlabeled texts. Our approach is unique in that it starts from raw textual data and comes to: i) identify a set of relevant domain-dependent terms; ii) extract aggregate and statistically significant semantic relationships between terms, documents and classes; iii) represent the accurate probabilistic knowledge as a KG; iv) extend and integrate the KG according to the Linked Open Data vision. The proposed solution is easily transferable to many domains and languages as long as the data are available. As a case study, we demonstrate how it is possible to automatically learn a KG representing the knowledge contained within the conversational messages shared on social networks such as Facebook by patients with rare diseases, and the impact this can have on creating resources aimed to capture the “voice of patients”.

Paper Citation

in Harvard Style

Frisoni G., Moro G. and Carbonaro A. (2020). Unsupervised Descriptive Text Mining for Knowledge Graph Learning. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR; ISBN 978-989-758-474-9, SciTePress, pages 316-324. DOI: 10.5220/0010153603160324

in Bibtex Style

@conference{kdir20,
author={Giacomo Frisoni and Gianluca Moro and Antonella Carbonaro},
title={Unsupervised Descriptive Text Mining for Knowledge Graph Learning},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR},
year={2020},
pages={316-324},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010153603160324},
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 1: KDIR
TI - Unsupervised Descriptive Text Mining for Knowledge Graph Learning
SN - 978-989-758-474-9
AU - Frisoni G.
AU - Moro G.
AU - Carbonaro A.
PY - 2020
SP - 316
EP - 324
DO - 10.5220/0010153603160324
PB - SciTePress