%0 Journal Article %1 Aasman17itpro %A Aasman, Jans %D 2017 %J IT Professional %K 01841 ieee paper ai enterprise database knowledge processing %N 6 %P 44--51 %R 10.1109/MITP.2017.4241469 %T Transmuting Information to Knowledge with an Enterprise Knowledge Graph %V 19 %X The enterprise knowledge graph has emerged as one of the most useful applications of graph database technology today. Manifest in three different varieties for internal operations, products and services, and customer 360-views, respectively, these graphs hinge on a linked enterprise data approach to determine relationships between graphs and individual nodes of data. Buttressed by semantic technology standards for ontologies, RDF graphs, and taxonomies, enterprise knowledge graphs provide a range of opportunities for contemporary enterprises when used alongside additional modern developments in artificial intelligence and visual querying.

@article{Aasman17itpro, abstract = {The enterprise knowledge graph has emerged as one of the most useful applications of graph database technology today. Manifest in three different varieties for internal operations, products and services, and customer 360-views, respectively, these graphs hinge on a linked enterprise data approach to determine relationships between graphs and individual nodes of data. Buttressed by semantic technology standards for ontologies, RDF graphs, and taxonomies, enterprise knowledge graphs provide a range of opportunities for contemporary enterprises when used alongside additional modern developments in artificial intelligence and visual querying.}, added-at = {2018-01-15T16:03:08.000+0100}, author = {Aasman, Jans}, biburl = {https://www.bibsonomy.org/bibtex/2dacd0864b4c48af482f80625a796ee15/flint63}, doi = {10.1109/MITP.2017.4241469}, file = {IEEE Digital Library:2017/Aasman17itpro.pdf:PDF}, groups = {public}, interhash = {5512a93cd06f2780e1434a01171a1415}, intrahash = {dacd0864b4c48af482f80625a796ee15}, issn = {1520-9202}, journal = {IT Professional}, keywords = {01841 ieee paper ai enterprise database knowledge processing}, number = 6, pages = {44--51}, timestamp = {2018-04-16T11:48:48.000+0200}, title = {Transmuting Information to Knowledge with an Enterprise Knowledge Graph}, username = {flint63}, volume = 19, year = 2017 }