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Found 21 papers, 5 papers with code
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.
Binarized Knowledge Graph Embeddings
This limitation is expected to become more stringent as existing knowledge graphs, which are already huge, keep steadily growing in scale.
Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction
This paper addresses the tasks of automatic seed selection for bootstrapping relation extraction, and noise reduction for distantly supervised relation extraction.
Learning Decorrelated Representations Efficiently Using Fast Fourier Transform
Barlow Twins and VICReg are self-supervised representation learning models that use regularizers to decorrelate features.
A Fast and Easy Regression Technique for k-NN Classification Without Using Negative Pairs
This paper proposes an inexpensive way to learn an effective dissimilarity function to be used for $k$-nearest neighbor ($k$-NN) classification.
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.
An Algebraic Formalization of Forward and Forward-backward Algorithms
back propagation) on computation graphs with addition and multiplication, and so on.
Ridge Regression, Hubness, and Zero-Shot Learning
This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space.
Developments in the theory of randomized shortest paths with a comparison of graph node distances
In particular, we see that the results obtained with the free energy distance are among the best in all the experiments.
Data-dependent Learning of Symmetric/Antisymmetric Relations for Knowledge Base Completion
Embedding-based methods for knowledge base completion (KBC) learn representations of entities and relations in a vector space, along with the scoring function to estimate the likelihood of relations between entities.
Neural Tensor Networks with Diagonal Slice Matrices
Although neural tensor networks (NTNs) have been successful in many NLP tasks, they require a large number of parameters to be estimated, which often leads to overfitting and a long training time.
A Non-commutative Bilinear Model for Answering Path Queries in Knowledge Graphs
Although they perform well in predicting atomic relations, composite relations (relation paths) cannot be modeled naturally by the product of relation matrices, as the product of diagonal matrices is commutative and hence invariant with the order of relations.
Binarized Canonical Polyadic Decomposition for Knowledge Graph Completion
Methods based on vector embeddings of knowledge graphs have been actively pursued as a promising approach to knowledge graph completion. However, embedding models generate storage-inefficient representations, particularly when the number of entities and relations, and the dimensionality of the real-valued embedding vectors are large.
A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings
This paper presents a simple and effective discrete optimization method for training binarized knowledge graph embedding model B-CP.
Transductive Data Augmentation with Relational Path Rule Mining for Knowledge Graph Embedding
For knowledge graph completion, two major types of prediction models exist: one based on graph embeddings, and the other based on relation path rule induction.
Action Class Relation Detection and Classification Across Multiple Video Datasets
The Meta Video Dataset (MetaVD) provides annotated relations between action classes in major datasets for human action recognition in videos.
Rethinking Loss Functions for Fact Verification
We explore loss functions for fact verification in the FEVER shared task.
