138 research outputs found
Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion
Multi-modal knowledge graph completion (MMKGC) aims to predict the missing
triples in the multi-modal knowledge graphs by incorporating structural,
visual, and textual information of entities into the discriminant models. The
information from different modalities will work together to measure the triple
plausibility. Existing MMKGC methods overlook the imbalance problem of modality
information among entities, resulting in inadequate modal fusion and
inefficient utilization of the raw modality information. To address the
mentioned problems, we propose Adaptive Multi-modal Fusion and Modality
Adversarial Training (AdaMF-MAT) to unleash the power of imbalanced modality
information for MMKGC. AdaMF-MAT achieves multi-modal fusion with adaptive
modality weights and further generates adversarial samples by
modality-adversarial training to enhance the imbalanced modality information.
Our approach is a co-design of the MMKGC model and training strategy which can
outperform 19 recent MMKGC methods and achieve new state-of-the-art results on
three public MMKGC benchmarks. Our code and data have been released at
https://github.com/zjukg/AdaMF-MAT.Comment: Accepted by LREC-COLING 202
A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects
Temporal characteristics are prominently evident in a substantial volume of
knowledge, which underscores the pivotal role of Temporal Knowledge Graphs
(TKGs) in both academia and industry. However, TKGs often suffer from
incompleteness for three main reasons: the continuous emergence of new
knowledge, the weakness of the algorithm for extracting structured information
from unstructured data, and the lack of information in the source dataset.
Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted
increasing attention, aiming to predict missing items based on the available
information. In this paper, we provide a comprehensive review of TKGC methods
and their details. Specifically, this paper mainly consists of three
components, namely, 1)Background, which covers the preliminaries of TKGC
methods, loss functions required for training, as well as the dataset and
evaluation protocol; 2)Interpolation, that estimates and predicts the missing
elements or set of elements through the relevant available information. It
further categorizes related TKGC methods based on how to process temporal
information; 3)Extrapolation, which typically focuses on continuous TKGs and
predicts future events, and then classifies all extrapolation methods based on
the algorithms they utilize. We further pinpoint the challenges and discuss
future research directions of TKGC
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
Navigating Healthcare Insights: A Birds Eye View of Explainability with Knowledge Graphs
Knowledge graphs (KGs) are gaining prominence in Healthcare AI, especially in
drug discovery and pharmaceutical research as they provide a structured way to
integrate diverse information sources, enhancing AI system interpretability.
This interpretability is crucial in healthcare, where trust and transparency
matter, and eXplainable AI (XAI) supports decision making for healthcare
professionals. This overview summarizes recent literature on the impact of KGs
in healthcare and their role in developing explainable AI models. We cover KG
workflow, including construction, relationship extraction, reasoning, and their
applications in areas like Drug-Drug Interactions (DDI), Drug Target
Interactions (DTI), Drug Development (DD), Adverse Drug Reactions (ADR), and
bioinformatics. We emphasize the importance of making KGs more interpretable
through knowledge-infused learning in healthcare. Finally, we highlight
research challenges and provide insights for future directions.Comment: IEEE AIKE 2023, 8 Page
Edge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs
In the rapidly advancing information era, various human behaviors are being
precisely recorded in the form of data, including identity information,
criminal records, and communication data. Law enforcement agencies can
effectively maintain social security and precisely combat criminal activities
by analyzing the aforementioned data. In comparison to traditional data
analysis methods, deep learning models, relying on the robust computational
power in cloud centers, exhibit higher accuracy in extracting data features and
inferring data. However, within the architecture of cloud centers, the
transmission of data from end devices introduces significant latency, hindering
real-time inference of data. Furthermore, low-latency edge computing
architectures face limitations in direct deployment due to relatively weak
computing and storage capacities of nodes. To address these challenges, a
lightweight distributed knowledge graph completion architecture is proposed.
Firstly, we introduce a lightweight distributed knowledge graph completion
architecture that utilizes knowledge graph embedding for data analysis.
Subsequently, to filter out substandard data, a personnel data quality
assessment method named PDQA is proposed. Lastly, we present a model pruning
algorithm that significantly reduces the model size while maximizing
performance, enabling lightweight deployment. In experiments, we compare the
effects of 11 advanced models on completing the knowledge graph of public
security personnel information. The results indicate that the RotatE model
outperforms other models significantly in knowledge graph completion, with the
pruned model size reduced by 70\%, and hits@10 reaching 86.97\%.}Comment: 20 pages, 6 figures, Has been accepted by Wireless Networ
Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs
Knowledge graphs (KGs) have become valuable knowledge resources in various
applications, and knowledge graph embedding (KGE) methods have garnered
increasing attention in recent years. However, conventional KGE methods still
face challenges when it comes to handling unseen entities or relations during
model testing. To address this issue, much effort has been devoted to various
fields of KGs. In this paper, we use a set of general terminologies to unify
these methods and refer to them collectively as Knowledge Extrapolation. We
comprehensively summarize these methods, classified by our proposed taxonomy,
and describe their interrelationships. Additionally, we introduce benchmarks
and provide comparisons of these methods based on aspects that are not captured
by the taxonomy. Finally, we suggest potential directions for future research.Comment: Accepted to IJCAI 2023 Survey Trac
From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
Knowledge graph completion aims to address the problem of extending a KG with
missing triples. In this paper, we provide an approach GenKGC, which converts
knowledge graph completion to sequence-to-sequence generation task with the
pre-trained language model. We further introduce relation-guided demonstration
and entity-aware hierarchical decoding for better representation learning and
fast inference. Experimental results on three datasets show that our approach
can obtain better or comparable performance than baselines and achieve faster
inference speed compared with previous methods with pre-trained language
models. We also release a new large-scale Chinese knowledge graph dataset
AliopenKG500 for research purpose. Code and datasets are available in
https://github.com/zjunlp/PromptKG/tree/main/GenKGC.Comment: Accepted by WWW 2022 Poste
ProcK: Machine Learning for Knowledge-Intensive Processes
We present a novel methodology to build powerful predictive process models.
Our method, denoted ProcK (Process & Knowledge), relies not only on sequential
input data in the form of event logs, but can learn to use a knowledge graph to
incorporate information about the attribute values of the events and their
mutual relationships. The idea is realized by mapping event attributes to nodes
of a knowledge graph and training a sequence model alongside a graph neural
network in an end-to-end fashion. This hybrid approach substantially enhances
the flexibility and applicability of predictive process monitoring, as both the
static and dynamic information residing in the databases of organizations can
be directly taken as input data. We demonstrate the potential of ProcK by
applying it to a number of predictive process monitoring tasks, including tasks
with knowledge graphs available as well as an existing process monitoring
benchmark where no such graph is given. The experiments provide evidence that
our methodology achieves state-of-the-art performance and improves predictive
power when a knowledge graph is available
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
Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning
This paper investigates cross-lingual temporal knowledge graph reasoning
problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs)
in low-resource languages by transfering knowledge from TKGs in high-resource
ones. The cross-lingual distillation ability across TKGs becomes increasingly
crucial, in light of the unsatisfying performance of existing reasoning methods
on those severely incomplete TKGs, especially in low-resource languages.
However, it poses tremendous challenges in two aspects. First, the
cross-lingual alignments, which serve as bridges for knowledge transfer, are
usually too scarce to transfer sufficient knowledge between two TKGs. Second,
temporal knowledge discrepancy of the aligned entities, especially when
alignments are unreliable, can mislead the knowledge distillation process. We
correspondingly propose a mutually-paced knowledge distillation model MP-KD,
where a teacher network trained on a source TKG can guide the training of a
student network on target TKGs with an alignment module. Concretely, to deal
with the scarcity issue, MP-KD generates pseudo alignments between TKGs based
on the temporal information extracted by our representation module. To maximize
the efficacy of knowledge transfer and control the noise caused by the temporal
knowledge discrepancy, we enhance MP-KD with a temporal cross-lingual attention
mechanism to dynamically estimate the alignment strength. The two procedures
are mutually paced along with model training. Extensive experiments on twelve
cross-lingual TKG transfer tasks in the EventKG benchmark demonstrate the
effectiveness of the proposed MP-KD method.Comment: This paper is accepted by The Web Conference 202
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