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New Embedded Representations and Evaluation Protocols for Inferring Transitive Relations
Beyond word embeddings, continuous representations of knowledge graph (KG)
components, such as entities, types and relations, are widely used for entity
mention disambiguation, relation inference and deep question answering. Great
strides have been made in modeling general, asymmetric or antisymmetric KG
relations using Gaussian, holographic, and complex embeddings. None of these
directly enforce transitivity inherent in the is-instance-of and is-subtype-of
relations. A recent proposal, called order embedding (OE), demands that the
vector representing a subtype elementwise dominates the vector representing a
supertype. However, the manner in which such constraints are asserted and
evaluated have some limitations. In this short research note, we make three
contributions specific to representing and inferring transitive relations.
First, we propose and justify a significant improvement to the OE loss
objective. Second, we propose a new representation of types as
hyper-rectangular regions, that generalize and improve on OE. Third, we show
that some current protocols to evaluate transitive relation inference can be
misleading, and offer a sound alternative. Rather than use black-box deep
learning modules off-the-shelf, we develop our training networks using
elementary geometric considerations.Comment: Accepted at SIGIR 201
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