17,825 research outputs found
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
There has been a surge of recent interest in learning representations for
graph-structured data. Graph representation learning methods have generally
fallen into three main categories, based on the availability of labeled data.
The first, network embedding (such as shallow graph embedding or graph
auto-encoders), focuses on learning unsupervised representations of relational
structure. The second, graph regularized neural networks, leverages graphs to
augment neural network losses with a regularization objective for
semi-supervised learning. The third, graph neural networks, aims to learn
differentiable functions over discrete topologies with arbitrary structure.
However, despite the popularity of these areas there has been surprisingly
little work on unifying the three paradigms. Here, we aim to bridge the gap
between graph neural networks, network embedding and graph regularization
models. We propose a comprehensive taxonomy of representation learning methods
for graph-structured data, aiming to unify several disparate bodies of work.
Specifically, we propose a Graph Encoder Decoder Model (GRAPHEDM), which
generalizes popular algorithms for semi-supervised learning on graphs (e.g.
GraphSage, Graph Convolutional Networks, Graph Attention Networks), and
unsupervised learning of graph representations (e.g. DeepWalk, node2vec, etc)
into a single consistent approach. To illustrate the generality of this
approach, we fit over thirty existing methods into this framework. We believe
that this unifying view both provides a solid foundation for understanding the
intuition behind these methods, and enables future research in the area
Transductive Classification Methods for Mixed Graphs
In this paper we provide a principled approach to solve a transductive
classification problem involving a similar graph (edges tend to connect nodes
with same labels) and a dissimilar graph (edges tend to connect nodes with
opposing labels). Most of the existing methods, e.g., Information
Regularization (IR), Weighted vote Relational Neighbor classifier (WvRN) etc,
assume that the given graph is only a similar graph. We extend the IR and WvRN
methods to deal with mixed graphs. We evaluate the proposed extensions on
several benchmark datasets as well as two real world datasets and demonstrate
the usefulness of our ideas.Comment: 8 Pages, 2 Tables, 2 Figures, KDD Workshop - MLG'11 San Diego, CA,
US
Information Extraction from Scientific Literature for Method Recommendation
As a research community grows, more and more papers are published each year.
As a result there is increasing demand for improved methods for finding
relevant papers, automatically understanding the key ideas and recommending
potential methods for a target problem. Despite advances in search engines, it
is still hard to identify new technologies according to a researcher's need.
Due to the large variety of domains and extremely limited annotated resources,
there has been relatively little work on leveraging natural language processing
in scientific recommendation. In this proposal, we aim at making scientific
recommendations by extracting scientific terms from a large collection of
scientific papers and organizing the terms into a knowledge graph. In
preliminary work, we trained a scientific term extractor using a small amount
of annotated data and obtained state-of-the-art performance by leveraging large
amount of unannotated papers through applying multiple semi-supervised
approaches. We propose to construct a knowledge graph in a way that can make
minimal use of hand annotated data, using only the extracted terms,
unsupervised relational signals such as co-occurrence, and structural external
resources such as Wikipedia. Latent relations between scientific terms can be
learned from the graph. Recommendations will be made through graph inference
for both observed and unobserved relational pairs.Comment: Thesis Proposal. arXiv admin note: text overlap with arXiv:1708.0607
Relation Extraction : A Survey
With the advent of the Internet, large amount of digital text is generated
everyday in the form of news articles, research publications, blogs, question
answering forums and social media. It is important to develop techniques for
extracting information automatically from these documents, as lot of important
information is hidden within them. This extracted information can be used to
improve access and management of knowledge hidden in large text corpora.
Several applications such as Question Answering, Information Retrieval would
benefit from this information. Entities like persons and organizations, form
the most basic unit of the information. Occurrences of entities in a sentence
are often linked through well-defined relations; e.g., occurrences of person
and organization in a sentence may be linked through relations such as employed
at. The task of Relation Extraction (RE) is to identify such relations
automatically. In this paper, we survey several important supervised,
semi-supervised and unsupervised RE techniques. We also cover the paradigms of
Open Information Extraction (OIE) and Distant Supervision. Finally, we describe
some of the recent trends in the RE techniques and possible future research
directions. This survey would be useful for three kinds of readers - i)
Newcomers in the field who want to quickly learn about RE; ii) Researchers who
want to know how the various RE techniques evolved over time and what are
possible future research directions and iii) Practitioners who just need to
know which RE technique works best in various settings
Parallel and Distributed Approaches for Graph Based Semi-supervised Learning
Two approaches for graph based semi-supervised learning are proposed. The
firstapproach is based on iteration of an affine map. A key element of the
affine map iteration is sparsematrix-vector multiplication, which has several
very efficient parallel implementations. The secondapproach belongs to the
class of Markov Chain Monte Carlo (MCMC) algorithms. It is based onsampling of
nodes by performing a random walk on the graph. The latter approach is
distributedby its nature and can be easily implemented on several processors or
over the network. Boththeoretical and practical evaluations are provided. It is
found that the nodes are classified intotheir class with very small error. The
sampling algorithm's ability to track new incoming nodesand to classify them is
also demonstrated
A Survey on Semi-Supervised Learning Techniques
Semisupervised learning is a learning standard which deals with the study of
how computers and natural systems such as human beings acquire knowledge in the
presence of both labeled and unlabeled data. Semisupervised learning based
methods are preferred when compared to the supervised and unsupervised learning
because of the improved performance shown by the semisupervised approaches in
the presence of large volumes of data. Labels are very hard to attain while
unlabeled data are surplus, therefore semisupervised learning is a noble
indication to shrink human labor and improve accuracy. There has been a large
spectrum of ideas on semisupervised learning. In this paper we bring out some
of the key approaches for semisupervised learning.Comment: 5 Pages, 3 figures, Published with International Journal of Computer
Trends and Technology (IJCTT
Machine Learning with World Knowledge: The Position and Survey
Machine learning has become pervasive in multiple domains, impacting a wide
variety of applications, such as knowledge discovery and data mining, natural
language processing, information retrieval, computer vision, social and health
informatics, ubiquitous computing, etc. Two essential problems of machine
learning are how to generate features and how to acquire labels for machines to
learn. Particularly, labeling large amount of data for each domain-specific
problem can be very time consuming and costly. It has become a key obstacle in
making learning protocols realistic in applications. In this paper, we will
discuss how to use the existing general-purpose world knowledge to enhance
machine learning processes, by enriching the features or reducing the labeling
work. We start from the comparison of world knowledge with domain-specific
knowledge, and then introduce three key problems in using world knowledge in
learning processes, i.e., explicit and implicit feature representation,
inference for knowledge linking and disambiguation, and learning with direct or
indirect supervision. Finally we discuss the future directions of this research
topic
Hypergraph p-Laplacian Regularization for Remote Sensing Image Recognition
It is of great importance to preserve locality and similarity information in
semi-supervised learning (SSL) based applications. Graph based SSL and manifold
regularization based SSL including Laplacian regularization (LapR) and
Hypergraph Laplacian regularization (HLapR) are representative SSL methods and
have achieved prominent performance by exploiting the relationship of sample
distribution. However, it is still a great challenge to exactly explore and
exploit the local structure of the data distribution. In this paper, we present
an effect and effective approximation algorithm of Hypergraph p-Laplacian and
then propose Hypergraph p-Laplacian regularization (HpLapR) to preserve the
geometry of the probability distribution. In particular, p-Laplacian is a
nonlinear generalization of the standard graph Laplacian and Hypergraph is a
generalization of a standard graph. Therefore, the proposed HpLapR provides
more potential to exploiting the local structure preserving. We apply HpLapR to
logistic regression and conduct the implementations for remote sensing image
recognition. We compare the proposed HpLapR to several popular manifold
regularization based SSL methods including LapR, HLapR and HpLapR on UC-Merced
dataset. The experimental results demonstrate the superiority of the proposed
HpLapR.Comment: 9 pages, 6 figure
Semi-Supervised Multi-aspect Detection of Misinformation using Hierarchical Joint Decomposition
Distinguishing between misinformation and real information is one of the most
challenging problems in today's interconnected world. The vast majority of the
state-of-the-art in detecting misinformation is fully supervised, requiring a
large number of high-quality human annotations. However, the availability of
such annotations cannot be taken for granted, since it is very costly,
time-consuming, and challenging to do so in a way that keeps up with the
proliferation of misinformation. In this work, we are interested in exploring
scenarios where the number of annotations is limited. In such scenarios, we
investigate how tapping on a diverse number of resources that characterize a
news article, henceforth referred to as "aspects" can compensate for the lack
of labels. In particular, our contributions in this paper are twofold: 1) We
propose the use of three different aspects: article content, context of social
sharing behaviors, and host website/domain features, and 2) We introduce a
principled tensor based embedding framework that combines all those aspects
effectively. We propose HiJoD a 2-level decomposition pipeline which not only
outperforms state-of-the-art methods with F1-scores of 74% and 81% on Twitter
and Politifact datasets respectively but also is an order of magnitude faster
than similar ensemble approaches
Semi-Supervised Online Structure Learning for Composite Event Recognition
Online structure learning approaches, such as those stemming from Statistical
Relational Learning, enable the discovery of complex relations in noisy data
streams. However, these methods assume the existence of fully-labelled training
data, which is unrealistic for most real-world applications. We present a novel
approach for completing the supervision of a semi-supervised structure learning
task. We incorporate graph-cut minimisation, a technique that derives labels
for unlabelled data, based on their distance to their labelled counterparts. In
order to adapt graph-cut minimisation to first order logic, we employ a
suitable structural distance for measuring the distance between sets of logical
atoms. The labelling process is achieved online (single-pass) by means of a
caching mechanism and the Hoeffding bound, a statistical tool to approximate
globally-optimal decisions from locally-optimal ones. We evaluate our approach
on the task of composite event recognition by using a benchmark dataset for
human activity recognition, as well as a real dataset for maritime monitoring.
The evaluation suggests that our approach can effectively complete the missing
labels and eventually, improve the accuracy of the underlying structure
learning system
- …