Message Passing Query Embedding
- URL: http://arxiv.org/abs/2002.02406v2
- Date: Wed, 24 Jun 2020 11:35:19 GMT
- Title: Message Passing Query Embedding
- Authors: Daniel Daza and Michael Cochez
- Abstract summary: We propose a graph neural network to encode a graph representation of a query.
We show that the model learns entity embeddings that capture the notion of entity type without explicit supervision.
- Score: 4.035753155957698
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent works on representation learning for Knowledge Graphs have moved
beyond the problem of link prediction, to answering queries of an arbitrary
structure. Existing methods are based on ad-hoc mechanisms that require
training with a diverse set of query structures. We propose a more general
architecture that employs a graph neural network to encode a graph
representation of the query, where nodes correspond to entities and variables.
The generality of our method allows it to encode a more diverse set of query
types in comparison to previous work. Our method shows competitive performance
against previous models for complex queries, and in contrast with these models,
it can answer complex queries when trained for link prediction only. We show
that the model learns entity embeddings that capture the notion of entity type
without explicit supervision.
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