Now showing items 1-13 of 13

    • CORAAL - Towards Deep Exploitation of Textual Resources in Life Sciences 

      Nováček, Vít; Groza, Tudor; Handschuh, Siegfried (Springer Verlag, 2009)
      Prominent biomedical literature search tools like ScienceDirect, PubMed Central or MEDLINE allow for efficient retrieval of resources based on key words. Due to vast amounts of data available in life sciences, key word ...
    • 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 ...
    • Extending Community Ontology Using Automatically Generated Suggestions 

      Nováček, Vít; Dabrowski, Maciej; Kruk, Sebastian Ryszard; Handschuh, Siegfried (AAAI Press, 2007)
      In this paper we propose an ontology (formal knowledge base) creation methodology based on integrating external ontologies into the one developed by a community of the do- main experts. We present the MarcOntX agent, a ...
    • 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 ...
    • Imprecise Empirical Ontology Refinement: Application to Taxonomy Acquisition 

      Nováček, Vít (INSTICC, 2007)
      The significance of uncertainty representation has become obvious in the Semantic Web community recently. This paper presents new results of our research on uncertainty incorporation into ontologies created automatically ...
    • Invited Talk: Can We Deal with Emergent Knowledge Yet? 

      Nováček, Vít (VSE Prague, 2010)
      This overview paper briefly describes problems we need to tackle if we want to meaningfully and efficiently process emergent knowledge. By this term we essentially mean knowledge continually emerging in a bottom-up manner ...
    • 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 ...
    • Knowledge-Based Search for Oncological Literature: Technical Architecture and User Perspectives 

      Nováček, Vít; Groza, Tudor; Handschuh, Siegfried (IEEE, 2009)
      Using the current state of the art in life science publication search (e.g., PubMed), one can efficiently search for resources containing particular key-words or their combinations. It is impossible to search for abstract ...
    • 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 ...
    • Regularizing knowledge graph embeddings via equivalence and inversion axioms 

      Minervini, Pasquale; Costabello, Luca; Muñoz, Emir; Nováček, Vít; Vandenbussche, Pierre-Yves (Springer Verlag, 2017-12-30)
      Learning embeddings of entities and relations using neural architectures is an effective method of performing statistical learning on large-scale relational data, such as knowledge graphs. In this paper, we consider the ...
    • SemanTex: semantic text exploration using document links implied by conceptual networks extracted from the texts 

      Aldarra, Suad; Muñoz, Emir; Vandenbussche, Pierre-Yves; Nováček, Vít (ACMCEUR-WS.org, 2014)
      Despite of advances in digital document processing, exploration of implicit relationships within large amounts of textual resources can still be daunting. This is partly due to the ‘black-box’ nature of most current ...
    • Towards Lightweight and Robust Large Scale Emergent Knowledge Processing 

      Nováček, Vít; Decker, Stefan (Springer, 2009)
      We present a lightweight framework for processing uncertain emergent knowledge that comes from multiple resources with varying relevance. The framework is essentially RDF-compatible, but allows also for direct representation ...
    • Using drug similarities for discovery of possible adverse reactions 

      Muñoz, Emir; Nováček, Vít; Vandenbussche, Pierre-Yves (AMIA, 2017-02-10)
      We propose a new computational method for discovery of possible adverse drug reactions. The method consists of two key steps. First we use openly available resources to semi-automatically compile a consolidated data set ...