37 citations · 72 across the 7 of their papers we have counts for
4 papers · 1 filter
Toward Understanding The Effect Of Loss function On Then Performance Of Knowledge Graph Embedding
Mojtaba Nayyeri, Chengjin Xu, Yadollah Yaghoobzadeh +2
Knowledge graphs (KGs) represent world's facts in structured forms. KG completion exploits the existing facts in a KG to discover new ones. Translation-based embedding model (Trans…
LogicENN: A Neural Based Knowledge Graphs Embedding Model with Logical Rules
Mojtaba Nayyeri, Chengjin Xu, Jens Lehmann +1
Knowledge graph embedding models have gained significant attention in AI research. Recent works have shown that the inclusion of background knowledge, such as logical rules, can im…
MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs
Afshin Sadeghi, Damien Graux, Hamed Shariat Yazdi +1
Over the past decade, knowledge graphs became popular for capturing structured domain knowledge. Relational learning models enable the prediction of missing links inside knowledge…
Soft Marginal TransE for Scholarly Knowledge Graph Completion
Mojtaba Nayyeri, Sahar Vahdati, Jens Lehmann +1
Knowledge graphs (KGs), i.e. representation of information as a semantic graph, provide a significant test bed for many tasks including question answering, recommendation, and link…