Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Mojtaba Nayyeri"'
Autor:
Mojtaba Nayyeri, Modjtaba Rouhani, Hadi Sadoghi Yazdi, Marko M. Mäkelä, Alaleh Maskooki, Yury Nikulin
Publikováno v:
Algorithms, Vol 17, Iss 1, p 49 (2024)
One of the main disadvantages of the traditional mean square error (MSE)-based constructive networks is their poor performance in the presence of non-Gaussian noises. In this paper, we propose a new incremental constructive network based on the corre
Externí odkaz:
https://doaj.org/article/f6a9dcd011524715abbc9bfc53524512
Autor:
Mirza Mohtashim Alam, Md Rashad Al Hasan Rony, Mojtaba Nayyeri, Karishma Mohiuddin, M. S. T. Mahfuja Akter, Sahar Vahdati, Jens Lehmann
Publikováno v:
IEEE Access, Vol 10, Pp 76008-76020 (2022)
Knowledge graph embedding models have become a popular approach for knowledge graph completion through predicting the plausibility of (potential) triples. This is performed by transforming the entities and relations of the knowledge graph into an emb
Externí odkaz:
https://doaj.org/article/b059c825278f45f99207ea8f7e1a7597
Autor:
Mojtaba Nayyeri, Gokce Muge Cil, Sahar Vahdati, Francesco Osborne, Andrey Kravchenko, Simone Angioni, Angelo Salatino, Diego Reforgiato Recupero, Enrico Motta, Jens Lehmann
Publikováno v:
IEEE Access, Vol 9, Pp 116002-116014 (2021)
Knowledge graphs (KGs) are widely used for modeling scholarly communication, performing scientometric analyses, and supporting a variety of intelligent services to explore the literature and predict research dynamics. However, they often suffer from
Externí odkaz:
https://doaj.org/article/bcc90211c03a4ff88a2b59eaaa9a41db
Publikováno v:
IEEE Access, Vol 8, Pp 196459-196471 (2020)
Knowledge Graph Embeddings (KGE) are used for representation learning in Knowledge Graphs (KGs) by measuring the likelihood of a relation between nodes. Rotation-based approaches, specially axis-angle representations, were shown to improve the perfor
Externí odkaz:
https://doaj.org/article/550ac6c55fae4bf4a2354046c2b2473a
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 45:7050-7062
Knowledge graph embedding models have gained significant attention in AI research. The aim of knowledge graph embedding is to embed the graphs into a vector space in which the structure of the graph is preserved. Recent works have shown that the incl
Autor:
Sahar Vahdati, Andrey Kravchenko, Gokce Muge Cil, Simone Angioni, Diego Reforgiato Recupero, Francesco Osborne, Jens Lehmann, Mojtaba Nayyeri, Enrico Motta, Angelo Antonio Salatino
Publikováno v:
IEEE Access, Vol 9, Pp 116002-116014 (2021)
Knowledge graphs (KGs) are widely used for modeling scholarly communication, performing scientometric analyses, and supporting a variety of intelligent services to explore the literature and predict research dynamics. However, they often suffer from
Recent years, Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a Knowledge Graph (KG) into a geometric space and thus have gained increasing attentions. In addition
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff00deb2fd237a28c41861906095aedc
Publikováno v:
The Semantic Web – ISWC 2022 ISBN: 9783031194320
Lecture Notes in Computer Science
Lecture Notes in Computer Science-The Semantic Web – ISWC 2022
Lecture Notes in Computer Science
Lecture Notes in Computer Science-The Semantic Web – ISWC 2022
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e2b7ae2eb0dd7e74a4bbe3277a6ad97
https://doi.org/10.1007/978-3-031-19433-7_2
https://doi.org/10.1007/978-3-031-19433-7_2
Autor:
Mojtaba Nayyeri, Sahar Vahdati, Md Tansen Khan, Mirza Mohtashim Alam, Lisa Wenige, Andreas Behrend, Jens Lehmann
Publikováno v:
The Semantic Web ISBN: 9783031069802
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bde8dea037fb0597592e18fa179718cc
https://doi.org/10.1007/978-3-031-06981-9_15
https://doi.org/10.1007/978-3-031-06981-9_15
Publikováno v:
IJCNN
Among the top approaches of recent years, link prediction using knowledge graph embedding (KGE) models has gained significant attention for knowledge graph completion. Various embedding models have been proposed so far, among which, some recent KGE m