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.
|Title of host publication||K-CAP 2021|
|Subtitle of host publication||Proceedings of the 11th Knowledge Capture Conference|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||8|
|Publication status||Published - Dec 2021|
|Event||11th ACM International Conference on Knowledge Capture, K-CAP 2021 - Virtual, Online, United States|
Duration: 2 Dec 2021 → 3 Dec 2021
|Conference||11th ACM International Conference on Knowledge Capture, K-CAP 2021|
|Period||2/12/21 → 3/12/21|
Bibliographical noteFunding Information:
We thank the Swiss National Science Foundation for their partial support under contract number 407550_167177. This work was also partially funded by Elsevier’s Discovery Lab.
© 2021 ACM.
- embedding similarity
- knowledge graph embeddings
- ontology evolution