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        <title>Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations</title>
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    <abstract>Knowledge graphs are a versatile framework to encode richly structured data relationships, but it can be challenging to combine these graphs with unstructured data. Methods for retrofitting pre-trained entity representations to the structure of a knowledge graph typically assume that entities are embedded in a connected space and that relations imply similarity. However, useful knowledge graphs often contain diverse entities and relations (with potentially disjoint underlying corpora) which do not accord with these assumptions. To overcome these limitations, we present Functional Retrofitting, a framework that generalizes current retrofitting methods by explicitly modeling pairwise relations. Our framework can directly incorporate a variety of pairwise penalty functions previously developed for knowledge graph completion. Further, it allows users to encode, learn, and extract information about relation semantics. We present both linear and neural instantiations of the framework. Functional Retrofitting significantly outperforms existing retrofitting methods on complex knowledge graphs and loses no accuracy on simpler graphs (in which relations do imply similarity). Finally, we demonstrate the utility of the framework by predicting new drug–disease treatment pairs in a large, complex health knowledge graph.</abstract>
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%0 Conference Proceedings
%T Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations
%A Lengerich, Ben
%A Maas, Andrew
%A Potts, Christopher
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F lengerich-etal-2018-retrofitting
%X Knowledge graphs are a versatile framework to encode richly structured data relationships, but it can be challenging to combine these graphs with unstructured data. Methods for retrofitting pre-trained entity representations to the structure of a knowledge graph typically assume that entities are embedded in a connected space and that relations imply similarity. However, useful knowledge graphs often contain diverse entities and relations (with potentially disjoint underlying corpora) which do not accord with these assumptions. To overcome these limitations, we present Functional Retrofitting, a framework that generalizes current retrofitting methods by explicitly modeling pairwise relations. Our framework can directly incorporate a variety of pairwise penalty functions previously developed for knowledge graph completion. Further, it allows users to encode, learn, and extract information about relation semantics. We present both linear and neural instantiations of the framework. Functional Retrofitting significantly outperforms existing retrofitting methods on complex knowledge graphs and loses no accuracy on simpler graphs (in which relations do imply similarity). Finally, we demonstrate the utility of the framework by predicting new drug–disease treatment pairs in a large, complex health knowledge graph.
%U https://aclanthology.org/C18-1205
%P 2423-2436

Markdown (Informal)

[Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations](https://aclanthology.org/C18-1205) (Lengerich et al., COLING 2018)

• Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations (Lengerich et al., COLING 2018)

ACL

• Ben Lengerich, Andrew Maas, and Christopher Potts. 2018. Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2423–2436, Santa Fe, New Mexico, USA. Association for Computational Linguistics.