Now showing items 1-4 of 4

    • Drug target discovery using knowledge graph embeddings 

      Mohamed, Sameh K.; Nováček, Vít; Nounu, Aayah (Association for Computing Machinery, 2019-04-08)
      The field of drug discovery has entered a plateau stage lately. It is increasingly more expensive and time-demanding to introduce new drugs into the market. One of the main reasons is the slow progress in finding novel ...
    • Identifying equivalent relation paths in knowledge graphs 

      Mohamed, Sameh K.; Muñoz, Emir; Nováček, Vít; Vandenbussche, Pierre-Yves (Springer Verlag, 2017-06-19)
      Relation paths are sequences of relations with inverse that allow for complete exploration of knowledge graphs in a two-way unconstrained manner. They are powerful enough to encode complex relationships between entities ...
    • Knowledge base completion using distinct subgraph paths 

      Mohamed, Sameh K.; Nováček, Vít; Vandenbussche, Pierre-Yves (ACM, 2018-04-09)
      Graph feature models facilitate efficient and interpretable predictions of missing links in knowledge bases with network structure (i.e. knowledge graphs). However, existing graph feature models-e.g. Subgraph Feature ...
    • Link prediction using multi part embeddings 

      Mohamed, Sameh K.; Nováček, Vít (NUI Galway, 2019-06-02)
      Knowledge graph embeddings models are widely used to provide scalable and efficient link prediction for knowledge graphs. They use different techniques to model embeddings interactions, where their tensor factorisation ...