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| <name><b>HyperE: Hyperbolic Embeddings for Entities</b></name> |
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| <i>Beliz Gunel, Fred Sala, Albert Gu, Christopher R&#</i> |
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| <p>Hyperbolic embeddings have captured the attention of the machine learning community through proposals from <ahref="diseinuak4web.net">Nickel&Kiela ()</a> and <ahref="diseinuak4web.net"> Chamberlain et al. ()</a>. The motivation is to embed structured, discrete objects such as knowledge graphs into a continuous representation that can be used with modern machine learning methods. Hyperbolic embeddings can preserve graph distances and complex relationships in very few dimensions, particularly for hierarchical graphs. In this website, we release hyperbolic embeddings that can be further integrated into applications related to knowledge base completion or can be supplied as features into various NLP tasks such as Question Answering. We provide a brief introduction both to hyperbolic geometry and to our work in this <ahref="diseinuak4web.net">blogpost</a>. In our <ahref="diseinuak4web.net">paper</a>, we present new approaches for solving the underlying optimization problems and show trade-offs for each of these approaches. |
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| <p>We provide the hyperbolic embeddings for chosen hierarchies from <ahref="diseinuak4web.net"> WordNet</a>, <ahref="diseinuak4web.net:Main_Page">Wikidata</a>, <ahref ="diseinuak4web.net">Unified Medical Language System (UMLS)</a> and <ahref="diseinuak4web.net">MusicBrainz</a> and evaluate the intrinsic quality of the embeddings through hyperbolic-analogues of "similarity" and "analogy". We use our combinatorial construction algorithm and our optimization-based approach implemented in PyTorch for all of the embeddings. Preliminary code for the embedding algorithms is publicly available <ahref="diseinuak4web.net">here</a>. Our selected entity embeddings in various dimensions are available to download in <ahref="diseinuak4web.net">GloVe</a> format below. |
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| <p> You can download our entity embeddings with bit precision in various dimensions (10d, 20d, 50d, d) for selected relationships from WordNet, Wikidata, UMLS and MusicBrainz in GloVe format. We also include the scale factor for the embeddings in the top line, since hyperbolic embeddings are not scale invariant, unlike Euclidean embeddings. Input graph distances between entities can be approximately recovered through dividing the hyperbolic distance between their corresponding hyperbolic embeddings by the provided scale factor. You can also find the preprocessing scripts to create edge lists for input graphs <ahref="diseinuak4web.net">here</a>. You can also get started with our <ahref="diseinuak4web.net">demo notebook</a> to see how hyperbolic entity embeddings are used. |
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