Traditional knowledge graph embedding (KGE) aims to map entities and relations into continuous space vectors to provide high-quality data feature representation for downstream tasks. However, relations in most KGs often only reflect connections between static entities, but cannot represent dynamic activities and state changes of related entities, which makes the KGE models unable to effectively learn rich and comprehensive entity representation. In this paper, we verify the importance of embedding event knowledge in KG representation learning, and propose a novel event KGE model based on event causal transfer (EventKGE), which can effectively maintain the semantic information of events, entities, and relations in the event KG. First, we define a six tuple-based event representation model consisting of event trigger words, event arguments and event description text, which can effectively express events in a structured form. Second, for a given event KG, the event nodes and entity nodes in the KG are integrated through the constructed heterogeneous graph. Meanwhile, in the heterogeneous graph, the event nodes and entity nodes are connected through the event arguments type, and the event nodes are connected through the causal relationship. Finally, an information transfer method based on an attention network is designed, which is used for the relations between events, events and entities, and entities and entities, to integrate event information into KGE. Comprehensive experiments on the ACE2005 corpus and CEC2.0 dataset verify that the event KGE model we designed is efficient and stable in multiple downstream tasks. 

EventKGE: Event knowledge graph embedding with event causal transfer

With the booming development of social media, temporal link prediction (TLP), as a core technology, has been receiving increasing attention. However, current methods are based on graph neural networks, which suffer from the over-smoothing issue and easily yield indistinguishable node representations, degrading the prediction accuracy. Besides, they lack the ability to eliminate noisy temporal information and ignore the importance of high-order neighbor information for measuring the link probability between nodes. To solve these issues, we design a cross-view graph contrastive learning (GCL) framework for TLP, called Tacl. We first design two augmented views for GCL by enhancing the temporal and topological information to obtain distinguishable node representations. Then, we learn the evolution rule of temporal networks to help constrain consistency of node representations and eliminate noise. Finally, we incorporate the high-order neighbor information to measure the link probability between nodes. Extensive experiments demonstrate the effectiveness and robustness of Tacl.

Temporal and Topological Augmentation-based Cross-view Contrastive Learning Model for Temporal Link Prediction

<italic>Knowledge hypergraph representation learning</italic>, which projects entities and <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq1-3365727.gif"/></alternatives></inline-formula>-ary relations into a low-dimensional vector space, remains a challenging area to be explored despite the ubiquity of <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq2-3365727.gif"/></alternatives></inline-formula>-ary relational facts in the real world. Current methods are always extensions of those used for knowledge graphs with shallow or deep structures. However, shallow and linear models limit the extraction capacity of the latent knowledge, while deep and non-linear models lead to the overabundance of parameters. In this paper, we propose a novel knowledge hypergraph completion model called HJE, which utilizes the powerful capability of convolutional neural networks for efficient representation learning. Interaction-enhanced 3D convolution and relation-aware 2D convolution are jointly utilized by HJE to extract explicit and implicit global knowledge and semantic information effectively without compromising the translation property of the model. Moreover, HJE constructs a unified learnable embedding matrix to capture entity position information in knowledge tuples. The entity mask mechanism can naturally couple the multilinear scoring approach for <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq3-3365727.gif"/></alternatives></inline-formula>-ary facts to speed up the training convergence of the model. Extensive experimental results on real datasets of knowledge hypergraphs and knowledge graphs demonstrate the superior performance of HJE compared with state-of-the-art baselines.

HJE: Joint Convolutional Representation Learning for Knowledge Hypergraph Completion

Graph neural networks (GNNs) have drawn increasing attention in recent years and achieved remarkable performance in many graph-based tasks, especially in semi-supervised learning on graphs. However, most existing GNNs are based on the message-passing paradigm to iteratively aggregate neighborhood information in a single topology space. Despite their success, the expressive power of GNNs is limited by some drawbacks, such as inflexibility of message source expansion, negligence of node-level message output discrepancy, and restriction of single message space. To address these drawbacks, we present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction. To verify its validity, we instantiate the new message-passing paradigm as a Dual-Perception Graph Neural Network (DPGNN), which applies a node-to-step attention mechanism to aggregate node-specific multi-step neighborhood information adaptively. Our proposed DPGNN can capture the structural neighborhood information and the feature-related information simultaneously for graph representation learning. Experimental results on six benchmark datasets with different topological structures demonstrate that our method outperforms the latest state-of-the-art models, which proves the superiority and versatility of our method. To our knowledge, we are the first to consider node-specific message passing in the GNNs. 

DPGNN: Dual-perception graph neural network for representation learning

In recent years, knowledge graphs (KGs) have gained considerable traction across various domains, especially in the realm of recommender systems, where their integration has garnered significant interest. These integrations aim to enhance the accuracy of recommendations by leveraging user-item interaction data and item attributes. However, existing methods encounter several challenges, including excessive smoothing, sparse data, redundancy, inadequate consideration of auxiliary information, and limitations in constructing deep networks. To address these challenges and enhance knowledge-graph-based recommendation methods that ignore auxiliary information and framework redundancy in neighborhood information aggregation, this study proposes a novel graph neural network recommendation model based on interactive embedding. This model capitalizes on both the KG and user-item interaction matrix to extract valuable information, refine aggregation methods, and optimize the overall performance. Specifically, user–relation interactive embedding is formed by establishing connections between users and relations using the user–item interaction matrix and KG as the foundation. This interactive embedding merges with the user through convolutional neural networks (CNNs), independently participating in the aggregation of neighborhood information to provide more contextual information for recommendation. The set of users who interact with items is extracted employing the user-item interaction matrix and creating item–user interactive embedding. It is then merged with the item using a CNN. Lastly, the final representations of the users and items for prediction are obtained. Experimental evaluations conducted on six real recommendation datasets demonstrate that our proposed model outperforms existing baselines. 

KGIE: Knowledge graph convolutional network for recommender system with interactive embedding

Knowledge hypergraph embedding, which projects entities and n-ary relations into a low-dimensional continuous vector space to predict missing links, remains a challenging area to be explored despite the ubiquity of n-ary relational facts in the real world. Currently, knowledge hypergraph link prediction methods are essentially simple extensions of those used in knowledge graphs, where n-ary relational facts are decomposed into different subelements. Convolutional neural networks have been shown to have remarkable information extraction capabilities in previous work on knowledge graph link prediction. In this paper, we propose a novel embedding-based knowledge hypergraph link prediction model named HyConvE, which exploits the powerful learning ability of convolutional neural networks for effective link prediction. Specifically, we employ 3D convolution to capture the deep interactions of entities and relations to efficiently extract explicit and implicit knowledge in each n-ary relational fact without compromising its translation property. In addition, appropriate relation and position-aware filters are utilized sequentially to perform two-dimensional convolution operations to capture the intrinsic patterns and position information in each n-ary relation, respectively. Extensive experimental results on real datasets of knowledge hypergraphs and knowledge graphs demonstrate the superior performance of HyConvE compared with state-of-the-art baselines.

HyConvE: A Novel Embedding Model for Knowledge Hypergraph Link Prediction with Convolutional Neural Networks

Aiming to help improve quality of life of the visually impaired people, this paper presents a novel wearable aid in the shape of a helmet for helping them find objects in indoor scenes. An object-goal navigation system based on a wearable device is developed, which consists of four modules: object relation prior knowledge (ORPK), perception, decision and feedback. To make the aid also work well in unfamiliar environment, ORPK is used for sub-goal inference to help the user find the target goal. And a method that learns the ORPK from unlabelled images by utilising a scene graph and knowledge graph is proposed. The effectiveness of the aid is demonstrated in real world experiments. 

Knowledge driven indoor object-goal navigation aid for visually impaired people

Heterogeneous information networks (HINs) have been proven to be powerful in modeling various real-world networks, such as academic networks and social media networks. By distinguishing the types of relationships and nodes, HINs obtain the capability to model multifarious data, while bringing challenges to data mining and analysis. Specifically, how to comprehensively mine the semantic information in HINs remains an open question. Although recently studied network schemas such as meta-paths or meta-graphs have achieved empirical success, they still have limitations in modeling the complex underlying relations to facilitate the graph neural networks, e.g., different schemas may overlap in semantics and lead to redundant computations. To address this issue, we leverage a recently introduced higher-order network schema known as the motif clique (abbreviated as m-clique). This schema offers greater expressiveness, enabling us to effectively construct the semantic neighborhood of nodes. We further propose a novel heterogeneous graph attention network with m-cliques, named HAMC, which employs a two-level attention mechanism (node-level and semantic-level) to learn node representations. The two-level attention measures the importance of neighbors and m-cliques for each node, respectively. Extensive experiments demonstrate that the proposed HAMC outperforms the state-of-the-art methods in many heterogeneous network analytic tasks such as node classification and clustering. Our code is available at https://github.com/wcx21/HAMC-Heterogeneous-Graph-Attention-Network-with-Motif-Clique. 

Heterogeneous graph attention network with motif clique

Sequential recommender models typically generate predictions in a single step during testing, without considering additional prediction correction to enhance performance as humans would. To improve the accuracy of these models, some researchers have attempted to simulate human analogical reasoning to correct predictions for testing data by drawing analogies with the prediction errors of similar training data. However, there are inherent gaps between testing and training data, which can make this approach unreliable. To address this issue, we propose an \textit{Abductive Prediction Correction} (APC) framework for sequential recommendation. Our approach simulates abductive reasoning to correct predictions. Specifically, we design an abductive reasoning task that infers the most probable historical interactions from the future interactions predicted by a recommender, and minimizes the discrepancy between the inferred and true historical interactions to adjust the predictions.We perform the abductive inference and adjustment using a reversed sequential model in the forward and backward propagation manner of neural networks. Our APC framework is applicable to various differentiable sequential recommender models. We implement it on three backbone models and demonstrate its effectiveness. We release the code at https://github.com/zyang1580/APC.

pdf/prediction-then-correction-an-abductive-prediction-wuf9sywa.pdf

Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Chenxu Wang

Author Tools

Chat about Author