KGvec2go – Knowledge Graph Embeddings as a Service

Jan Portisch; Michael Hladik; Heiko Paulheim

KGvec2go – Knowledge Graph Embeddings as a Service

Jan Portisch, Michael Hladik, Heiko Paulheim

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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.

Anthology ID:: 2020.lrec-1.692
Volume:: Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:: May
Year:: 2020
Address:: Marseille, France
Editors:: Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:: LREC
SIG:
Publisher:: European Language Resources Association
Note:
Pages:: 5641–5647
Language:: English
URL:: https://aclanthology.org/2020.lrec-1.692/
DOI:
Bibkey:
Cite (ACL):: Jan Portisch, Michael Hladik, and Heiko Paulheim. 2020. KGvec2go – Knowledge Graph Embeddings as a Service. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5641–5647, Marseille, France. European Language Resources Association.
Cite (Informal):: KGvec2go – Knowledge Graph Embeddings as a Service (Portisch et al., LREC 2020)
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PDF:: https://aclanthology.org/2020.lrec-1.692.pdf

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@inproceedings{portisch-etal-2020-kgvec2go,
    title = "{KG}vec2go {--} Knowledge Graph Embeddings as a Service",
    author = "Portisch, Jan  and
      Hladik, Michael  and
      Paulheim, Heiko",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Twelfth 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",
    language = "eng",
    ISBN = "979-10-95546-34-4",
    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."
}

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%0 Conference Proceedings
%T KGvec2go – Knowledge Graph Embeddings as a Service
%A Portisch, Jan
%A Hladik, Michael
%A Paulheim, Heiko
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G eng
%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

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Markdown (Informal)

[KGvec2go – Knowledge Graph Embeddings as a Service](https://aclanthology.org/2020.lrec-1.692/) (Portisch et al., LREC 2020)

KGvec2go – Knowledge Graph Embeddings as a Service (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 Twelfth Language Resources and Evaluation Conference, pages 5641–5647, Marseille, France. European Language Resources Association.