@inproceedings{portisch-etal-2020-kgvec2go,
title = "{KG}vec2go {--} Knowledge Graph Embeddings as a Service",
author = "Portisch, Jan and
Hladik, Michael and
Paulheim, Heiko",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.692",
pages = "5641--5647",
abstract = "In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple semantic benchmarks. The evaluation also reveals that the combination of multiple models can lead to a better outcome than the best individual model.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple semantic benchmarks. The evaluation also reveals that the combination of multiple models can lead to a better outcome than the best individual model.</abstract>
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%0 Conference Proceedings
%T KGvec2go – Knowledge Graph Embeddings as a Service
%A Portisch, Jan
%A Hladik, Michael
%A Paulheim, Heiko
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F portisch-etal-2020-kgvec2go
%X In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple semantic benchmarks. The evaluation also reveals that the combination of multiple models can lead to a better outcome than the best individual model.
%U https://aclanthology.org/2020.lrec-1.692
%P 5641-5647
Markdown (Informal)
[KGvec2go – Knowledge Graph Embeddings as a Service](https://aclanthology.org/2020.lrec-1.692) (Portisch et al., LREC 2020)
ACL
- Jan Portisch, Michael Hladik, and Heiko Paulheim. 2020. KGvec2go – Knowledge Graph Embeddings as a Service. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 5641–5647, Marseille, France. European Language Resources Association.