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HEXTRATO: Using ontology-based constraints to improve accuracy on learning domain-specific entity and relationship embedding representation for knowledge resolution

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,. (pp. pp. 72-81). (In press).

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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 machine 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

Type: Proceedings paper
Title: HEXTRATO: Using ontology-based constraints to improve accuracy on learning domain-specific entity and relationship embedding representation for knowledge resolution
Event: 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Location: Seville, Spain
Dates: 18-20 September 2018
ISBN-13: 9789897583308
DOI: 10.5220/0006923700720081
Publisher version: http://www.scitepress.org/DigitalLibrary/Link.aspx...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Knowledge Resolution, Knowledge Embedding, Link Prediction, Knowledge Completion, Electronic Health Records
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Pop Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Pop Health Sciences > Institute of Health Informatics > Clinical Epidemiology
URI: http://discovery.ucl.ac.uk/id/eprint/10065713
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