loading
Papers

Research.Publish.Connect.

Paper

Author: Hegler Tissot

Affiliation: C3SL, Universidade Federal do Paraná, Curitiba and Brazil

ISBN: 978-989-758-330-8

Keyword(s): Knowledge Resolution, Knowledge Embedding, Link Prediction, Knowledge Completion, Electronic Health Records.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; BioInformatics & Pattern Discovery ; Computational Intelligence ; Evolutionary Computing ; Foundations of Knowledge Discovery in Databases ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: This paper focuses the problem of learning the knowledge low-dimensional embedding representation for entities and relations extracted from domain-specific datasets. Existing embedding methods aim to represent entities and relations from a knowledge graph as vectors in a continuous low-dimensional space. Different approaches have been proposed, being usually evaluated on standard benchmark knowledge graphs, such as Wordnet and Freebase. However, the nature of such data sources prevents those methods of taking advantage of more detailed and enriched metadata, lacking more accurate results on the evaluation tasks. In this paper, we propose HEXTRATO, a novel embedding approach that extends a traditional baseline model TransE by adding ontology-based constraints in order to better capture the relationships between categorised entities and their symbolic representation in the vector space. Our method is evaluated on an adapted version of Freebase, on a publicly available dataset used on ma chine learning benchmarks, and on two datasets in the clinical domain. Our method outperforms the state-of-the-art accuracy on the link prediction task, evidencing the learnt entity and relation embedding representation can be used to improve more complex embedding models. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.80.6.254

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Tissot, H. (2018). HEXTRATO: Using Ontology-based Constraints to Improve Accuracy on Learning Domain-specific Entity and Relationship Embedding Representation for Knowledge Resolution.In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-330-8, pages 72-81. DOI: 10.5220/0006923700720081

@conference{kdir18,
author={Hegler Tissot.},
title={HEXTRATO: Using Ontology-based Constraints to Improve Accuracy on Learning Domain-specific Entity and Relationship Embedding Representation for Knowledge Resolution},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2018},
pages={72-81},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006923700720081},
isbn={978-989-758-330-8},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - HEXTRATO: Using Ontology-based Constraints to Improve Accuracy on Learning Domain-specific Entity and Relationship Embedding Representation for Knowledge Resolution
SN - 978-989-758-330-8
AU - Tissot, H.
PY - 2018
SP - 72
EP - 81
DO - 10.5220/0006923700720081

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.