It's well known that 80\% of the effort of a data scientist is spent on data preparation. Semantic integration is arguably the best way to spend this effort more efficiently and to reuse it between tasks, projects and organizations. Knowledge Graphs (KG) and Linked Open Data (LOD) have become very popular recently. They are used by Google, Amazon, Bing, Samsung, Springer Nature, Microsoft Academic, AirBnb… and any large enterprise that would like to have a holistic (360 degree) view of its business. The Semantic Web (web 3.0) is a way to build a Giant Global Graph, just like the normal web is a Global Web of Documents. IEEE already talks about Big Data Semantics. We review the topic of KGs and their applicability to Machine Learning.
%0 Generic
%1 Alexiev2019-devbg
%A Alexiev, Vladimir
%C dev.bg Machine Learning interest group, Sofia, Bulgaria
%D 2019
%K deep_learning knowledge_graph machine_learning
%T Semantic Integration Is What You Do Before The Deep Learning
%U https://dev.bg/събитие/machine-learning-semantic-integration-is-what-you-do-before-the-deep-learning/
%X It's well known that 80\% of the effort of a data scientist is spent on data preparation. Semantic integration is arguably the best way to spend this effort more efficiently and to reuse it between tasks, projects and organizations. Knowledge Graphs (KG) and Linked Open Data (LOD) have become very popular recently. They are used by Google, Amazon, Bing, Samsung, Springer Nature, Microsoft Academic, AirBnb… and any large enterprise that would like to have a holistic (360 degree) view of its business. The Semantic Web (web 3.0) is a way to build a Giant Global Graph, just like the normal web is a Global Web of Documents. IEEE already talks about Big Data Semantics. We review the topic of KGs and their applicability to Machine Learning.
@misc{Alexiev2019-devbg,
abstract = {It's well known that 80\% of the effort of a data scientist is spent on data preparation. Semantic integration is arguably the best way to spend this effort more efficiently and to reuse it between tasks, projects and organizations. Knowledge Graphs (KG) and Linked Open Data (LOD) have become very popular recently. They are used by Google, Amazon, Bing, Samsung, Springer Nature, Microsoft Academic, AirBnb… and any large enterprise that would like to have a holistic (360 degree) view of its business. The Semantic Web (web 3.0) is a way to build a Giant Global Graph, just like the normal web is a Global Web of Documents. IEEE already talks about Big Data Semantics. We review the topic of KGs and their applicability to Machine Learning.},
added-at = {2021-08-25T16:07:36.000+0200},
address = {dev.bg Machine Learning interest group, Sofia, Bulgaria},
author = {Alexiev, Vladimir},
biburl = {https://www.bibsonomy.org/bibtex/244cc58e7195b61e8994077cd4908164a/valexiev},
howpublished = {presentation},
interhash = {35b241a7c223d63bf554d9dce7b6815c},
intrahash = {44cc58e7195b61e8994077cd4908164a},
keywords = {deep_learning knowledge_graph machine_learning},
month = may,
timestamp = {2021-08-25T16:07:36.000+0200},
title = {Semantic Integration Is What You Do Before The Deep Learning},
url = {https://dev.bg/събитие/machine-learning-semantic-integration-is-what-you-do-before-the-deep-learning/},
url_ppt = {http://rawgit2.com/VladimirAlexiev/my/master/pres/20190513-Semantics-and-ML-dev.bg.pptx},
url_slides = {https://www.slideshare.net/valexiev1/semantics-and-machine-learning},
year = 2019
}