3 research outputs found
Revisiting Simple Neural Networks for Learning Representations of Knowledge Graphs
We address the problem of learning vector representations for entities and
relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This
problem has received significant attention in the past few years and multiple
methods have been proposed. Most of the existing methods in the literature use
a predefined characteristic scoring function for evaluating the correctness of
KG triples. These scoring functions distinguish correct triples (high score)
from incorrect ones (low score). However, their performance vary across
different datasets. In this work, we demonstrate that a simple neural network
based score function can consistently achieve near start-of-the-art performance
on multiple datasets. We also quantitatively demonstrate biases in standard
benchmark datasets, and highlight the need to perform evaluation spanning
various datasets.Comment: 7 pages, submitted to and accepted in Automated Knowledge Base
Construction (AKBC) Workshop 2017, at NIPS 201
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 useful
to perform knowledge graph completion or link prediction, i.e. predict whether
a relationship not in the knowledge graph is likely to be true. This paper
serves as a comprehensive survey of embedding models of entities and
relationships for knowledge graph completion, summarizing up-to-date
experimental results on standard benchmark datasets and pointing out potential
future research directions.Comment: 13 pages, 2 figures and 6 table
Neural Graph Embedding Methods for Natural Language Processing
Knowledge graphs are structured representations of facts in a graph, where
nodes represent entities and edges represent relationships between them. Recent
research has resulted in the development of several large KGs. However, all of
them tend to be sparse with very few facts per entity. In the first part of the
thesis, we propose two solutions to alleviate this problem: (1) KG
Canonicalization, i.e., identifying and merging duplicate entities in a KG, (2)
Relation Extraction which involves automating the process of extracting
semantic relationships between entities from unstructured text. Traditional
Neural Networks like CNNs and RNNs are constrained to handle Euclidean data.
However, graphs in Natural Language Processing (NLP) are prominent. Recently,
Graph Convolutional Networks (GCNs) have been proposed to address this
shortcoming and have been successfully applied for several problems. In the
second part of the thesis, we utilize GCNs for Document Timestamping problem
and for learning word embeddings using dependency context of a word instead of
sequential context. In this third part of the thesis, we address two
limitations of existing GCN models, i.e., (1) The standard neighborhood
aggregation scheme puts no constraints on the number of nodes that can
influence the representation of a target node. This leads to a noisy
representation of hub-nodes which coves almost the entire graph in a few hops.
(2) Most of the existing GCN models are limited to handle undirected graphs.
However, a more general and pervasive class of graphs are relational graphs
where each edge has a label and direction associated with it. Existing
approaches to handle such graphs suffer from over-parameterization and are
restricted to learning representation of nodes only.Comment: 168 pages, PhD thesis (2019