Papers Knowledge Graph Completion

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 …

On Multi-Relational Link Prediction with Bilinear Models

We study bilinear embedding models for the task of multi-relational link prediction and knowledge graph completion. Bilinear models belong to the most basic models for this task, they are comparably efficient to train an…

Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations

Knowledge graphs are a versatile framework to encode richly structured data relationships, but it can be challenging to combine these graphs with unstructured data. Methods for retrofitting pre-trained entity representat…

Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding

Integrating text and knowledge into a unified semantic space has attracted significant research interests recently. However, the ambiguity in the common space remains a challenge, namely that the same mention phrase usua…

Iterative Entity Alignment via Joint Knowledge Embeddings

Entity alignment aims to link entities and their counterparts among multiple knowledge graphs (KGs). Most existing methods typically rely on external information of entities such as Wikipedia links and require costly man…

Joint Matrix-Tensor Factorization for Knowledge Base Inference

While several matrix factorization (MF) and tensor factorization (TF) models have been proposed for knowledge base (KB) inference, they have rarely been compared across various datasets. Is there a single model that perf…

Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence

Knowledge graphs (KGs), which could provide essential relational information between entities, have been widely utilized in various knowledge-driven applications. Since the overall human knowledge is innumerable that sti…

A survey of embedding models of entities and relationships for knowledge graph completion

Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge graphs are typically incomplete, it is…

Embedding Knowledge Graphs Based on Transitivity and Antisymmetry of Rules

Representation learning of knowledge graphs encodes entities and relation types into a continuous low-dimensional vector space, learns embeddings of entities and relation types. Most existing methods only concentrate on …

Knowledge Graph Completion via Complex Tensor Factorization

In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edg…

On the Equivalence of Holographic and Complex Embeddings for Link Prediction

We show the equivalence of two state-of-the-art link prediction/knowledge graph completion methods: Nickel et al's holographic embedding and Trouillon et al.'s complex embedding. We first consider a spectral version of t…

Towards Time-Aware Knowledge Graph Completion

Knowledge graph (KG) completion adds new facts to a KG by making inferences from existing facts. Most existing methods ignore the time information and only learn from time-unknown fact triples. In dynamic environments th…

ProjE: Embedding Projection for Knowledge Graph Completion

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 addre…

Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion

Joint representation learning of text and knowledge within a unified semantic space enables us to perform knowledge graph completion more accurately. In this work, we propose a novel framework to embed words, entities an…

Image-embodied Knowledge Representation Learning

Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information…

A Translation-Based Knowledge Graph Embedding Preserving Logical Property of Relations

Knowledge Transfer with Medical Language Embeddings

Identifying relationships between concepts is a key aspect of scientific knowledge synthesis. Finding these links often requires a researcher to laboriously search through scien- tific papers and databases, as the size o…

Type-Constrained Representation Learning in Knowledge Graphs

Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. Latent variable models have…

Knowledge Graph Embedding via Dynamic Mapping Matrix

Learning Entity and Relation Embeddings for Knowledge Graph Completion

Knowledge graph completion aims to perform link prediction between entities. In this paper, we consider the approach of knowledge graph embeddings. Recently, models such as TransE and TransH build entity and relation emb…