37 research outputs found
Few-Shot Knowledge Graph Completion
Knowledge graphs (KGs) serve as useful resources for various natural language
processing applications. Previous KG completion approaches require a large
number of training instances (i.e., head-tail entity pairs) for every relation.
The real case is that for most of the relations, very few entity pairs are
available. Existing work of one-shot learning limits method generalizability
for few-shot scenarios and does not fully use the supervisory information;
however, few-shot KG completion has not been well studied yet. In this work, we
propose a novel few-shot relation learning model (FSRL) that aims at
discovering facts of new relations with few-shot references. FSRL can
effectively capture knowledge from heterogeneous graph structure, aggregate
representations of few-shot references, and match similar entity pairs of
reference set for every relation. Extensive experiments on two public datasets
demonstrate that FSRL outperforms the state-of-the-art
Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion
Knowledge graphs (KGs) are known for their large scale and knowledge
inference ability, but are also notorious for the incompleteness associated
with them. Due to the long-tail distribution of the relations in KGs, few-shot
KG completion has been proposed as a solution to alleviate incompleteness and
expand the coverage of KGs. It aims to make predictions for triplets involving
novel relations when only a few training triplets are provided as reference.
Previous methods have mostly focused on designing local neighbor aggregators to
learn entity-level information and/or imposing sequential dependency assumption
at the triplet level to learn meta relation information. However, valuable
pairwise triplet-level interactions and context-level relational information
have been largely overlooked for learning meta representations of few-shot
relations. In this paper, we propose a hierarchical relational learning method
(HiRe) for few-shot KG completion. By jointly capturing three levels of
relational information (entity-level, triplet-level and context-level), HiRe
can effectively learn and refine the meta representation of few-shot relations,
and consequently generalize very well to new unseen relations. Extensive
experiments on two benchmark datasets validate the superiority of HiRe against
other state-of-the-art methods.Comment: 10 pages, 5 figure
Framing Few-Shot Knowledge Graph Completion with Large Language Models
Knowledge Graph Completion (KGC) from text involves identifying known or unknown entities (nodes) as well as relations (edges) among these entities. Recent work has started to explore the use of Large Language Models (LLMs) for entity detection and relation extraction, due to their Natural Language Understanding (NLU) capabilities. However, LLM performance varies across models and depends on the quality of the prompt engineering. We examine specific relation extraction cases and present a set of examples collected from well-known resources in a small corpus. We provide a set of annotations and identify various issues that occur when using different LLMs for this task. As LLMs will remain a focal point of future KGC research, we conclude with suggestions for improving the KGC process
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
Graphs represent interconnected structures prevalent in a myriad of
real-world scenarios. Effective graph analytics, such as graph learning
methods, enables users to gain profound insights from graph data, underpinning
various tasks including node classification and link prediction. However, these
methods often suffer from data imbalance, a common issue in graph data where
certain segments possess abundant data while others are scarce, thereby leading
to biased learning outcomes. This necessitates the emerging field of imbalanced
learning on graphs, which aims to correct these data distribution skews for
more accurate and representative learning outcomes. In this survey, we embark
on a comprehensive review of the literature on imbalanced learning on graphs.
We begin by providing a definitive understanding of the concept and related
terminologies, establishing a strong foundational understanding for readers.
Following this, we propose two comprehensive taxonomies: (1) the problem
taxonomy, which describes the forms of imbalance we consider, the associated
tasks, and potential solutions; (2) the technique taxonomy, which details key
strategies for addressing these imbalances, and aids readers in their method
selection process. Finally, we suggest prospective future directions for both
problems and techniques within the sphere of imbalanced learning on graphs,
fostering further innovation in this critical area.Comment: The collection of awesome literature on imbalanced learning on
graphs: https://github.com/Xtra-Computing/Awesome-Literature-ILoG
On the Evolution of Knowledge Graphs: A Survey and Perspective
Knowledge graphs (KGs) are structured representations of diversified
knowledge. They are widely used in various intelligent applications. In this
article, we provide a comprehensive survey on the evolution of various types of
knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs)
and techniques for knowledge extraction and reasoning. Furthermore, we
introduce the practical applications of different types of KGs, including a
case study in financial analysis. Finally, we propose our perspective on the
future directions of knowledge engineering, including the potential of
combining the power of knowledge graphs and large language models (LLMs), and
the evolution of knowledge extraction, reasoning, and representation
Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancement
Although extensive research has been conducted on 3D point cloud
segmentation, effectively adapting generic models to novel categories remains a
formidable challenge. This paper proposes a novel approach to improve point
cloud few-shot segmentation (PC-FSS) models. Unlike existing PC-FSS methods
that directly utilize categorical information from support prototypes to
recognize novel classes in query samples, our method identifies two critical
aspects that substantially enhance model performance by reducing contextual
gaps between support prototypes and query features. Specifically, we (1) adapt
support background prototypes to match query context while removing extraneous
cues that may obscure foreground and background in query samples, and (2)
holistically rectify support prototypes under the guidance of query features to
emulate the latter having no semantic gap to the query targets. Our proposed
designs are agnostic to the feature extractor, rendering them readily
applicable to any prototype-based methods. The experimental results on S3DIS
and ScanNet demonstrate notable practical benefits, as our approach achieves
significant improvements while still maintaining high efficiency. The code for
our approach is available at
https://github.com/AaronNZH/Boosting-Few-shot-3D-Point-Cloud-Segmentation-via-Query-Guided-EnhancementComment: Accepted to ACM MM 202
Relational structure-aware knowledge graph representation in complex space
Relations in knowledge graphs have rich relational structures and various binary relational patterns. Various relation modelling strategies are proposed for embedding knowledge graphs, but they fail to fully capture both features of relations, rich relational structures and various binary relational patterns. To address the problem of insufficient embedding due to the complexity of the relations, we propose a novel knowledge graph representation model in complex space, namely MARS, to exploit complex relations to embed knowledge graphs. MARS takes the mechanisms of complex numbers and message-passing and then embeds triplets into relation-specific complex hyperplanes. Thus, MARS can well preserve various relation patterns, as well as structural information in knowledge graphs. In addition, we find that the scores generated from the score function approximate a Gaussian distribution. The scores in the tail cannot effectively represent triplets. To address this particular issue and improve the precision of embeddings, we use the standard deviation to limit the dispersion of the score distribution, resulting in more accurate embeddings of triplets. Comprehensive experiments on multiple benchmarks demonstrate that our model significantly outperforms existing state-of-the-art models for link prediction and triple classification. © 2022 by the authors. Licensee MDPI, Basel, Switzerland