@inproceedings{f131ced91c2e445aa3b0c59ec40e00b3,
title = "A first experiment on including text literals in KGlove",
abstract = "Graph embedding models produce embedding vectors for entities and relations in Knowledge Graphs, often without taking literal properties into account. We show an initial idea based on the combination of global graph structure with additional information provided by textual information in properties. Our initial experiment shows that this approach might be useful, but does not clearly outperform earlier approaches when evaluated on machine learning tasks.",
keywords = "Attributes, Graph embeddings, Knowledge graph",
author = "Michael Cochez and Martina Garofalo and J{\'e}r{\^o}me Len{\ss}en and Pellegrino, {Maria Angela}",
year = "2018",
month = jan,
day = "1",
language = "English",
volume = "2241",
series = "CEUR Workshop Proceedings",
publisher = "CEUR Workshop Proceedings",
pages = "103--106",
booktitle = "Joint 4th Workshop on Semantic Deep Learning: Natural Language Interfaces for the Web of Data and 9th Question Answering over Linked Data Challenge, SemDeep-4_NLIWOD-4 2018",
note = "Joint 4th Workshop on Semantic Deep Learning: Natural Language Interfaces for the Web of Data and 9th Question Answering over Linked Data Challenge, SemDeep-4_NLIWOD-4 2018 ; Conference date: 08-10-2018 Through 09-10-2018",
}