36,579 research outputs found
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
Open-World Knowledge Graph Completion
Knowledge Graphs (KGs) have been applied to many tasks including Web search,
link prediction, recommendation, natural language processing, and entity
linking. However, most KGs are far from complete and are growing at a rapid
pace. To address these problems, Knowledge Graph Completion (KGC) has been
proposed to improve KGs by filling in its missing connections. Unlike existing
methods which hold a closed-world assumption, i.e., where KGs are fixed and new
entities cannot be easily added, in the present work we relax this assumption
and propose a new open-world KGC task. As a first attempt to solve this task we
introduce an open-world KGC model called ConMask. This model learns embeddings
of the entity's name and parts of its text-description to connect unseen
entities to the KG. To mitigate the presence of noisy text descriptions,
ConMask uses a relationship-dependent content masking to extract relevant
snippets and then trains a fully convolutional neural network to fuse the
extracted snippets with entities in the KG. Experiments on large data sets,
both old and new, show that ConMask performs well in the open-world KGC task
and even outperforms existing KGC models on the standard closed-world KGC task.Comment: 8 pages, accepted to AAAI 201
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Knowledge graph embedding aims to learn distributed representations for
entities and relations, and is proven to be effective in many applications.
Crossover interactions --- bi-directional effects between entities and
relations --- help select related information when predicting a new triple, but
haven't been formally discussed before. In this paper, we propose CrossE, a
novel knowledge graph embedding which explicitly simulates crossover
interactions. It not only learns one general embedding for each entity and
relation as most previous methods do, but also generates multiple triple
specific embeddings for both of them, named interaction embeddings. We evaluate
embeddings on typical link prediction tasks and find that CrossE achieves
state-of-the-art results on complex and more challenging datasets. Furthermore,
we evaluate embeddings from a new perspective --- giving explanations for
predicted triples, which is important for real applications. In this work, an
explanation for a triple is regarded as a reliable closed-path between the head
and the tail entity. Compared to other baselines, we show experimentally that
CrossE, benefiting from interaction embeddings, is more capable of generating
reliable explanations to support its predictions.Comment: This paper is accepted by WSDM201
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