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Contribution Details

Type Book Chapter
Scope Discipline-based scholarship
Title Toward Measuring the Resemblance of Embedding Models for Evolving Ontologies
Organization Unit
  • Romana Pernisch
  • Daniele Dell'Aglio
  • Abraham Bernstein
  • Anna Lisa Gentile
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Booktitle K-CAP '21: Proceedings of the 11th on Knowledge Capture Conference
ISBN 978-1-4503-8457-5
Place of Publication New York
Publisher ACM
Page Range 177 - 184
Date 2021
Abstract Text Updates on ontologies affect the operations built on top of them. But not all changes are equal: some updates drastically change the result of operations; others lead to minor variations, if any. Hence, estimating the impact of a change ex-ante is highly important, as it might make ontology engineers aware of the consequences of their action during editing. However, in order to estimate the impact of changes, we need to understand how to measure them. To address this gap for embeddings, we propose a new measure called Embedding Resemblance Indicator (ERI), which takes into account both the stochasticity of learning embeddings as well as the shortcomings of established comparison methods. We base ERI on (i) a similarity score, (ii) a robustness factor $\hatμ $ (based on the embedding method, similarity measure, and dataset), and (iii) the number of added or deleted entities to the embedding computed with the Jaccard index. To evaluate ERI, we investigate its usage in the context of two biomedical ontologies and three embedding methods---GraRep, LINE, and DeepWalk---as well as the two standard benchmark datasets---FB15k-237 and Wordnet-18-RR---with TransE and RESCAL embeddings. To study different aspects of ERI, we introduce synthetic changes in the knowledge graphs, generating two test-cases with five versions each and compare their impact with the expected behaviour. Our studies suggests that ERI behaves as expected and captures the similarity of embeddings based on the severity of changes. ERI is crucial for enabling further studies into impact of changes on embeddings.
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Digital Object Identifier 10.1145/3460210.3493540
Other Identification Number merlin-id:21600
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