30,057 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
An Interpretable Knowledge Transfer Model for Knowledge Base Completion
Knowledge bases are important resources for a variety of natural language
processing tasks but suffer from incompleteness. We propose a novel embedding
model, \emph{ITransF}, to perform knowledge base completion. Equipped with a
sparse attention mechanism, ITransF discovers hidden concepts of relations and
transfer statistical strength through the sharing of concepts. Moreover, the
learned associations between relations and concepts, which are represented by
sparse attention vectors, can be interpreted easily. We evaluate ITransF on two
benchmark datasets---WN18 and FB15k for knowledge base completion and obtains
improvements on both the mean rank and Hits@10 metrics, over all baselines that
do not use additional information.Comment: Accepted by ACL 2017. Minor updat
STransE: a novel embedding model of entities and relationships in knowledge bases
Knowledge bases of real-world facts about entities and their relationships
are useful resources for a variety of natural language processing tasks.
However, because knowledge bases are typically incomplete, it is useful to be
able to perform link prediction or knowledge base completion, i.e., predict
whether a relationship not in the knowledge base is likely to be true. This
paper combines insights from several previous link prediction models into a new
embedding model STransE that represents each entity as a low-dimensional
vector, and each relation by two matrices and a translation vector. STransE is
a simple combination of the SE and TransE models, but it obtains better link
prediction performance on two benchmark datasets than previous embedding
models. Thus, STransE can serve as a new baseline for the more complex models
in the link prediction task.Comment: V1: In Proceedings of the 2016 Conference of the North American
Chapter of the Association for Computational Linguistics: Human Language
Technologies, NAACL HLT 2016. V2: Corrected citation to (Krompa{\ss} et al.,
2015). V3: A revised version of our NAACL-HLT 2016 paper with additional
experimental results and latest related wor
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