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.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/676515 |
| Date | 29 March 2022 |
| Creators | Peng, Xi |
| Contributors | Hoehndorf, Robert, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Hadwiger, Markus, Huser, Raphaël |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | 2023-04-25, 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. |
Page generated in 0.0018 seconds