[AI Seminar] AI Seminar sponsored by Apple -- Otilia Stretcu

[Han Zhao]

Dear faculty and students:

We look forward to seeing you next Tuesday, Apr. 16th, at noon in *NSH
3305 *for our AI Seminar sponsored by Apple. To learn more about the
seminar series,
please visit the website <http://www.cs.cmu.edu/~aiseminar/>.
On Tuesday, Otilia Stretcu will give the following talk:
*Title: **Contextual Parameter Generation for Knowledge Graph Link
Prediction*

*Abstract: *Knowledge graph link prediction is the task of inferring
missing relationships between entities in a knowledge graph. It can be seen
as a form of question answering, where given a question consisting of an
entity and a relation (e.g., “Shakespeare” and “bornIn”), we are tasked
with predicting the most likely answer entity (e.g., “England”). Recent
methods have focused on inferring answers by learning entity and relation
embeddings. For example, the current state-of-the-art, ConvE, stacks the
question entity and relation embeddings together and uses a convolutional
neural network to predict the answer entity. However, we argue that
entities and relations model different kinds of information and thus,
integrating them in this way does not represent a meaningful inductive bias
for the model. Therefore, we instead propose to treat relations as the
“context” in which question entities are interpreted and transformed to
produce answer entities. Concretely, we use relation embeddings to generate
the parameters of a model operating over entity embeddings, which in turn
produces a distribution over possible answers. This proposed model
outperforms all existing methods by a significant margin on several
established datasets, thus establishing the new state-of-the-art for this
problem, while at the same time reducing training time by up to 98%.
-- 

*Han ZhaoMachine Learning Department*


*School of Computer ScienceCarnegie Mellon UniversityMobile: +1-*
*412-652-4404*
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