Search Results for author:
Found 22 papers, 10 papers with code
On Aligning OpenIE Extractions with Knowledge Bases: A Case Study
First, we investigate OPIEC triples and DBpedia facts having the same arguments by comparing the information on the OIE surface relation with the KB rela- tion.
Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction
We propose KGT5-context, a simple sequence-to-sequence model for link prediction (LP) in knowledge graphs (KG).
Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings
Knowledge graph embedding (KGE) models are an effective and popular approach to represent and reason with multi-relational data.
Good Intentions: Adaptive Parameter Management via Intent Signaling
Parameter management is essential for distributed training of large machine learning (ML) tasks.
Sequence-to-Sequence Knowledge Graph Completion and Question Answering
These methods have recently been applied to KG link prediction and question answering over incomplete KGs (KGQA).
NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access
Parameter servers (PSs) facilitate the implementation of distributed training for large machine learning tasks.
Differentiable Implicit Layers
In this paper, we introduce an efficient backpropagation scheme for non-constrained implicit functions.
LibKGE - A knowledge graph embedding library for reproducible research
LibKGE ( https://github. com/uma-pi1/kge ) is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction.
Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction
An evaluation in such a setup raises the question if a correct prediction is actually a new fact that was induced by reasoning over the open knowledge graph or if it can be trivially explained.
You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings
A vast number of KGE techniques for multi-relational link prediction have been proposed in the recent literature, often with state-of-the-art performance.
Dynamic Parameter Allocation in Parameter Servers
To keep up with increasing dataset sizes and model complexity, distributed training has become a necessity for large machine learning tasks.
OPIEC: An Open Information Extraction Corpus
In this paper, we release, describe, and analyze an OIE corpus called OPIEC, which was extracted from the text of English Wikipedia.
A Relational Tucker Decomposition for Multi-Relational Link Prediction
We propose the Relational Tucker3 (RT) decomposition for multi-relational link prediction in knowledge graphs.
On Evaluating Embedding Models for Knowledge Base Completion
In this paper, we explore whether recent models work well for knowledge base completion and argue that the current evaluation protocols are more suited for question answering rather than knowledge base completion.
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval
In this study, we investigate learning-to-rank and query refinement approaches for information retrieval in the pharmacogenomic domain.
On Multi-Relational Link Prediction with Bilinear Models
Bilinear models belong to the most basic models for this task, they are comparably efficient to train and use, and they can provide good prediction performance.
MinIE: Minimizing Facts in Open Information Extraction
The goal of Open Information Extraction (OIE) is to extract surface relations and their arguments from natural-language text in an unsupervised, domain-independent manner.
Senti-LSSVM: Sentiment-Oriented Multi-Relation Extraction with Latent Structural SVM
Extracting instances of sentiment-oriented relations from user-generated web documents is important for online marketing analysis.
