226 research outputs found
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge
graphs that represent structural relations between entities have become an
increasingly popular research direction towards cognition and human-level
intelligence. In this survey, we provide a comprehensive review of knowledge
graph covering overall research topics about 1) knowledge graph representation
learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph,
and 4) knowledge-aware applications, and summarize recent breakthroughs and
perspective directions to facilitate future research. We propose a full-view
categorization and new taxonomies on these topics. Knowledge graph embedding is
organized from four aspects of representation space, scoring function, encoding
models, and auxiliary information. For knowledge acquisition, especially
knowledge graph completion, embedding methods, path inference, and logical rule
reasoning, are reviewed. We further explore several emerging topics, including
meta relational learning, commonsense reasoning, and temporal knowledge graphs.
To facilitate future research on knowledge graphs, we also provide a curated
collection of datasets and open-source libraries on different tasks. In the
end, we have a thorough outlook on several promising research directions
Graph Neural Networks for Natural Language Processing: A Survey
Deep learning has become the dominant approach in coping with various tasks
in Natural LanguageProcessing (NLP). Although text inputs are typically
represented as a sequence of tokens, there isa rich variety of NLP problems
that can be best expressed with a graph structure. As a result, thereis a surge
of interests in developing new deep learning techniques on graphs for a large
numberof NLP tasks. In this survey, we present a comprehensive overview onGraph
Neural Networks(GNNs) for Natural Language Processing. We propose a new
taxonomy of GNNs for NLP, whichsystematically organizes existing research of
GNNs for NLP along three axes: graph construction,graph representation
learning, and graph based encoder-decoder models. We further introducea large
number of NLP applications that are exploiting the power of GNNs and summarize
thecorresponding benchmark datasets, evaluation metrics, and open-source codes.
Finally, we discussvarious outstanding challenges for making the full use of
GNNs for NLP as well as future researchdirections. To the best of our
knowledge, this is the first comprehensive overview of Graph NeuralNetworks for
Natural Language Processing.Comment: 127 page
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
A Retrieve-and-Read Framework for Knowledge Graph Link Prediction
Knowledge graph (KG) link prediction aims to infer new facts based on
existing facts in the KG. Recent studies have shown that using the graph
neighborhood of a node via graph neural networks (GNNs) provides more useful
information compared to just using the query information. Conventional GNNs for
KG link prediction follow the standard message-passing paradigm on the entire
KG, which leads to superfluous computation, over-smoothing of node
representations, and also limits their expressive power. On a large scale, it
becomes computationally expensive to aggregate useful information from the
entire KG for inference. To address the limitations of existing KG link
prediction frameworks, we propose a novel retrieve-and-read framework, which
first retrieves a relevant subgraph context for the query and then jointly
reasons over the context and the query with a high-capacity reader. As part of
our exemplar instantiation for the new framework, we propose a novel
Transformer-based GNN as the reader, which incorporates graph-based attention
structure and cross-attention between query and context for deep fusion. This
simple yet effective design enables the model to focus on salient context
information relevant to the query. Empirical results on two standard KG link
prediction datasets demonstrate the competitive performance of the proposed
method. Furthermore, our analysis yields valuable insights for designing
improved retrievers within the framework.Comment: Accepted to CIKM'23; Published version DOI:
https://doi.org/10.1145/3583780.3614769 ;12 pages, 4 figure
Using Graph Algorithms to Pretrain Graph Completion Transformers
Recent work on Graph Neural Networks has demonstrated that self-supervised
pretraining can further enhance performance on downstream graph, link, and node
classification tasks. However, the efficacy of pretraining tasks has not been
fully investigated for downstream large knowledge graph completion tasks. Using
a contextualized knowledge graph embedding approach, we investigate five
different pretraining signals, constructed using several graph algorithms and
no external data, as well as their combination. We leverage the versatility of
our Transformer-based model to explore graph structure generation pretraining
tasks (i.e. path and k-hop neighborhood generation), typically inapplicable to
most graph embedding methods. We further propose a new path-finding algorithm
guided by information gain and find that it is the best-performing pretraining
task across three downstream knowledge graph completion datasets. While using
our new path-finding algorithm as a pretraining signal provides 2-3% MRR
improvements, we show that pretraining on all signals together gives the best
knowledge graph completion results. In a multitask setting that combines all
pretraining tasks, our method surpasses the latest and strong performing
knowledge graph embedding methods on all metrics for FB15K-237, on MRR and
Hit@1 for WN18RRand on MRR and hit@10 for JF17K (a knowledge hypergraph
dataset)
DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning
Temporal knowledge graphs (TKGs) model the temporal evolution of events and
have recently attracted increasing attention. Since TKGs are intrinsically
incomplete, it is necessary to reason out missing elements. Although existing
TKG reasoning methods have the ability to predict missing future events, they
fail to generate explicit reasoning paths and lack explainability. As
reinforcement learning (RL) for multi-hop reasoning on traditional knowledge
graphs starts showing superior explainability and performance in recent
advances, it has opened up opportunities for exploring RL techniques on TKG
reasoning. However, the performance of RL-based TKG reasoning methods is
limited due to: (1) lack of ability to capture temporal evolution and semantic
dependence jointly; (2) excessive reliance on manually designed rewards. To
overcome these challenges, we propose an adaptive reinforcement learning model
based on attention mechanism (DREAM) to predict missing elements in the future.
Specifically, the model contains two components: (1) a multi-faceted attention
representation learning method that captures semantic dependence and temporal
evolution jointly; (2) an adaptive RL framework that conducts multi-hop
reasoning by adaptively learning the reward functions. Experimental results
demonstrate DREAM outperforms state-of-the-art models on public datasetComment: 11 page
Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection
Despite considerable advances in automated fake news detection, due to the
timely nature of news, it remains a critical open question how to effectively
predict the veracity of news articles based on limited fact-checks. Existing
approaches typically follow a "Train-from-Scratch" paradigm, which is
fundamentally bounded by the availability of large-scale annotated data. While
expressive pre-trained language models (PLMs) have been adapted in a
"Pre-Train-and-Fine-Tune" manner, the inconsistency between pre-training and
downstream objectives also requires costly task-specific supervision. In this
paper, we propose "Prompt-and-Align" (P&A), a novel prompt-based paradigm for
few-shot fake news detection that jointly leverages the pre-trained knowledge
in PLMs and the social context topology. Our approach mitigates label scarcity
by wrapping the news article in a task-related textual prompt, which is then
processed by the PLM to directly elicit task-specific knowledge. To supplement
the PLM with social context without inducing additional training overheads,
motivated by empirical observation on user veracity consistency (i.e., social
users tend to consume news of the same veracity type), we further construct a
news proximity graph among news articles to capture the veracity-consistent
signals in shared readerships, and align the prompting predictions along the
graph edges in a confidence-informed manner. Extensive experiments on three
real-world benchmarks demonstrate that P&A sets new states-of-the-art for
few-shot fake news detection performance by significant margins.Comment: Accepted to CIKM 2023 (Full Paper
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