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Description Logic EL++Embeddings with Intersectional Closure

Many ontologies, in particular in the biomedical domain, are based on the Description Logic EL++. Several efforts have been made to interpret and exploit EL++ontologies by distributed representation learning. Specifically, concepts within EL++theories have been represented as n-balls within an n-dimensional embedding space. However, the intersectional closure is not satisfied when using n-balls to represent concepts because the intersection of two n-balls is not an n-ball. This leads to challenges when measuring the distance between concepts and inferring equivalence between concepts. To this end, we developed EL Box Embedding (ELBE) to learn Description Logic EL++embeddings using axis-parallel boxes. We generate specially designed box-based geometric constraints from EL++axioms for model training. Since the intersection of boxes remains as a box, the intersectional closure is satisfied. We report extensive experimental results on three datasets and present a case study to demonstrate the effectiveness of the proposed method.

Identiferoai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/676515
Date29 March 2022
CreatorsPeng, Xi
ContributorsHoehndorf, Robert, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Hadwiger, Markus, Huser, Raphaƫl
Source SetsKing Abdullah University of Science and Technology
LanguageEnglish
Detected LanguageEnglish
TypeThesis
Rights2023-04-25, At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2023-04-25.

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