dc.contributor.author | Cochez, Michael | |
dc.contributor.author | Ristoski, Petar | |
dc.contributor.author | Ponzetto, Simone Paolo | |
dc.contributor.author | Paulheim, Heiko | |
dc.date.accessioned | 2017-12-14T11:57:25Z | |
dc.date.available | 2017-12-14T11:57:25Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Cochez, M., Ristoski, P., Ponzetto, S. P., & Paulheim, H. (2017). Biased GraphWalks for RDF Graph Embeddings. In <i>WIMS '17 : Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics</i> (Article 21). ACM. <a href="https://doi.org/10.1145/3102254.3102279" target="_blank">https://doi.org/10.1145/3102254.3102279</a> | |
dc.identifier.other | CONVID_27135714 | |
dc.identifier.other | TUTKAID_74537 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/56339 | |
dc.description.abstract | Knowledge Graphs have been recognized as a valuable source
for background information in many data mining, information
retrieval, natural language processing, and knowledge extraction
tasks. However, obtaining a suitable feature vector representation
from RDF graphs is a challenging task. In this paper, we extend the
RDF2Vec approach, which leverages language modeling techniques
for unsupervised feature extraction from sequences of entities. We
generate sequences by exploiting local information from graph substructures,
harvested by graph walks, and learn latent numerical
representations of entities in RDF graphs. We extend the way we
compute feature vector representations by comparing twelve di erent
edge weighting functions for performing biased walks on the
RDF graph, in order to generate higher quality graph embeddings.
We evaluate our approach using di erent machine learning, as well
as entity and document modeling benchmark data sets, and show
that the naive RDF2Vec approach can be improved by exploiting
Biased Graph Walks. | |
dc.format.extent | 268 | |
dc.language.iso | eng | |
dc.publisher | ACM | |
dc.relation.ispartof | WIMS '17 : Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics | |
dc.subject.other | graph embeddings | |
dc.title | Biased GraphWalks for RDF Graph Embeddings | |
dc.type | conferenceObject | |
dc.identifier.urn | URN:NBN:fi:jyu-201712114609 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
dc.date.updated | 2017-12-11T13:15:19Z | |
dc.relation.isbn | 978-1-4503-5225-3 | |
dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
dc.description.reviewstatus | peerReviewed | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.conference | International Conference on Web Intelligence, Mining and Semantics | |
dc.subject.yso | tiedonlouhinta | |
dc.subject.yso | avoin tieto | |
dc.subject.yso | yhdistetty avoin tieto | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p5520 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p26655 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p26001 | |
dc.relation.doi | 10.1145/3102254.3102279 | |
dc.type.okm | A4 | |