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Identifying equivalent relation paths in knowledge graphs
(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 ...
Regularizing knowledge graph embeddings via equivalence and inversion axioms
(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
(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 ...
Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models
(Oxford University Press (OUP), 2017-08-18)
Timely identification of adverse drug reactions (ADRs) is highly important in the domains of public health and pharmacology. Early discovery of potential ADRs can limit their effect on patient lives and also make drug ...
µRaptor: A DOM-based system with appetite for hCard elements
(CEUR-WS.org, 2014)
This paper describes µRaptor, a DOM-based method to extract hCard microformats from HTML pages stripped of microformat markup. µRaptor extracts DOM sub-trees, converts them into rules, and uses them to extract hCard ...
Knowledge base completion using distinct subgraph paths
(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 ...
Using drug similarities for discovery of possible adverse reactions
(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 ...