83 research outputs found
Path Ranking with Attention to Type Hierarchies
The objective of the knowledge base completion problem is to infer missing
information from existing facts in a knowledge base. Prior work has
demonstrated the effectiveness of path-ranking based methods, which solve the
problem by discovering observable patterns in knowledge graphs, consisting of
nodes representing entities and edges representing relations. However, these
patterns either lack accuracy because they rely solely on relations or cannot
easily generalize due to the direct use of specific entity information. We
introduce Attentive Path Ranking, a novel path pattern representation that
leverages type hierarchies of entities to both avoid ambiguity and maintain
generalization. Then, we present an end-to-end trained attention-based RNN
model to discover the new path patterns from data. Experiments conducted on
benchmark knowledge base completion datasets WN18RR and FB15k-237 demonstrate
that the proposed model outperforms existing methods on the fact prediction
task by statistically significant margins of 26% and 10%, respectively.
Furthermore, quantitative and qualitative analyses show that the path patterns
balance between generalization and discrimination.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20
Proactive Robot Assistance via Spatio-Temporal Object Modeling
Proactive robot assistance enables a robot to anticipate and provide for a
user's needs without being explicitly asked. We formulate proactive assistance
as the problem of the robot anticipating temporal patterns of object movements
associated with everyday user routines, and proactively assisting the user by
placing objects to adapt the environment to their needs. We introduce a
generative graph neural network to learn a unified spatio-temporal predictive
model of object dynamics from temporal sequences of object arrangements. We
additionally contribute the Household Object Movements from Everyday Routines
(HOMER) dataset, which tracks household objects associated with human
activities of daily living across 50+ days for five simulated households. Our
model outperforms the leading baseline in predicting object movement, correctly
predicting locations for 11.1% more objects and wrongly predicting locations
for 11.5% fewer objects used by the human user
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