2,748 research outputs found
Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach
Knowledge base completion (KBC) aims to predict missing information in a
knowledge base.In this paper, we address the out-of-knowledge-base (OOKB)
entity problem in KBC:how to answer queries concerning test entities not
observed at training time. Existing embedding-based KBC models assume that all
test entities are available at training time, making it unclear how to obtain
embeddings for new entities without costly retraining. To solve the OOKB entity
problem without retraining, we use graph neural networks (Graph-NNs) to compute
the embeddings of OOKB entities, exploiting the limited auxiliary knowledge
provided at test time.The experimental results show the effectiveness of our
proposed model in the OOKB setting.Additionally, in the standard KBC setting in
which OOKB entities are not involved, our model achieves state-of-the-art
performance on the WordNet dataset. The code and dataset are available at
https://github.com/takuo-h/GNN-for-OOKBComment: This paper has been accepted by IJCAI1
Large Margin Nearest Neighbor Embedding for Knowledge Representation
Traditional way of storing facts in triplets ({\it head\_entity, relation,
tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively
displayed and easily acquired by mankind, but hardly computed or even reasoned
by AI machines. Inspired by the success in applying {\it Distributed
Representations} to AI-related fields, recent studies expect to represent each
entity and relation with a unique low-dimensional embedding, which is different
from the symbolic and atomic framework of displaying knowledge in triplets. In
this way, the knowledge computing and reasoning can be essentially facilitated
by means of a simple {\it vector calculation}, i.e. . We thus contribute an effective model to learn better embeddings
satisfying the formula by pulling the positive tail entities to
get together and close to {\bf h} + {\bf r} ({\it Nearest Neighbor}), and
simultaneously pushing the negatives away from the positives
via keeping a {\it Large Margin}. We also design a corresponding
learning algorithm to efficiently find the optimal solution based on {\it
Stochastic Gradient Descent} in iterative fashion. Quantitative experiments
illustrate that our approach can achieve the state-of-the-art performance,
compared with several latest methods on some benchmark datasets for two
classical applications, i.e. {\it Link prediction} and {\it Triplet
classification}. Moreover, we analyze the parameter complexities among all the
evaluated models, and analytical results indicate that our model needs fewer
computational resources on outperforming the other methods.Comment: arXiv admin note: text overlap with arXiv:1503.0815
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