2022
DOI: 10.1007/978-3-031-11609-4_25
|View full text |Cite
|
Sign up to set email alerts
|

Walk This Way!

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2022
2022
2023
2023

Publication Types

Select...
3
3

Relationship

0
6

Authors

Journals

citations
Cited by 7 publications
(3 citation statements)
references
References 4 publications
0
3
0
Order By: Relevance
“…This too changes the nature of sampled walks, namely in that predicates are more likely to be sampled. The difference between sampling entities versus predicates was discussed in [44] and termed e-walks and p-walks, respectively. In short, it was shown that e-walks are better at capturing the relatedness of entities, and p-walks are better at capturing their semantic similarity.…”
Section: Resultsmentioning
confidence: 99%
“…This too changes the nature of sampled walks, namely in that predicates are more likely to be sampled. The difference between sampling entities versus predicates was discussed in [44] and termed e-walks and p-walks, respectively. In short, it was shown that e-walks are better at capturing the relatedness of entities, and p-walks are better at capturing their semantic similarity.…”
Section: Resultsmentioning
confidence: 99%
“…In future work, we will implement new parameters to allow these walks. Furthermore, we will incorporate other walk flavors [24], [25].…”
Section: Parameter Analysis and Discussionmentioning
confidence: 99%
“…Moreover, Portisch et al introduced an order-aware RDF2Vec (RDF2Vec oa ) [23] using structured word2vec [14], focusing on the original word2vec model's positional insensitivity. Additionally, they proposed similarity-oriented and relevance-oriented walk methods and identified the advantages of each method [24]. Steenwinckel et al [25] showed that the WL graph kernel offers little improvements in the context of a single KG with respect to walk embeddings, and proposed five alternative walking strategies.…”
Section: Related Work a Knowledge Graph Embeddings For Rdfmentioning
confidence: 99%