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Baoxu Shi

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AAAI Conference 2018 Conference Paper

Open-World Knowledge Graph Completion

Baoxu Shi
Tim Weninger

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 openworld 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 relationshipdependent 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.

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AAAI Conference 2017 Conference Paper

ProjE: Embedding Projection for Knowledge Graph Completion

Baoxu Shi
Tim Weninger

With the large volume of new information created every day, determining the validity of information in a knowledge graph and ﬁlling in its missing parts are crucial tasks for many researchers and practitioners. To address this challenge, a number of knowledge graph completion methods have been developed using low-dimensional graph embeddings. Although researchers continue to improve these models using an increasingly complex feature space, we show that simple changes in the architecture of the underlying model can outperform state-of-the-art models without the need for complex feature engineering. In this work, we present a shared variable neural network model called ProjE that ﬁlls-in missing information in a knowledge graph by learning joint embeddings of the knowledge graph’s entities and edges, and through subtle, but important, changes to the standard loss function. In doing so, ProjE has a parameter size that is smaller than 11 out of 15 existing methods while performing 37% better than the current-best method on standard datasets. We also show, via a new fact checking task, that ProjE is capable of accurately determining the veracity of many declarative statements.

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