Rule-Guided Joint Embedding Learning over Knowledge Graphs

• URL: http://arxiv.org/abs/2401.02968v3
• Date: Sat, 30 Aug 2025 21:16:07 GMT
• Title: Rule-Guided Joint Embedding Learning over Knowledge Graphs
• Authors: Qisong Li, Ji Lin, Sijia Wei, Neng Liu,
• Abstract summary: We propose a novel model that integrates both contextual and textual signals into entity and relation embeddings.<n>To better utilize context, we introduce two metrics: confidence, computed via a rule-based method, and relatedness, derived from textual representations.
• Score: 2.797512394739081
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies on knowledge graph embedding focus on mapping entities and relations into low-dimensional vector spaces. While most existing models primarily exploit structural information, knowledge graphs also contain rich contextual and textual information that can enhance embedding effectiveness. In this work, we propose a novel model that integrates both contextual and textual signals into entity and relation embeddings through a graph convolutional network. To better utilize context, we introduce two metrics: confidence, computed via a rule-based method, and relatedness, derived from textual representations. These metrics enable more precise weighting of contextual information during embedding learning. Extensive experiments on two widely used benchmark datasets demonstrate the effectiveness of our approach, showing consistent improvements over strong baselines.

Related papers

• Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods [0.12289361708127876]
  This survey provides a comprehensive review of methods handling n-ary relational data, covering both knowledge hypergraphs and hyper-relational knowledge graphs literatures.<n>We propose a two-dimensional taxonomy: the first dimension categorises models based on their methodology, i.e., translation-based models, deep neural network-based models, logic rules-based models, and hyperedge expansion-based models.<n>The second dimension classifies models according to their awareness of entity roles and positions in n-ary relations, dividing them into aware-less, position-aware, and role-aware approaches.
  arXiv  Detail & Related papers  (2025-06-05T22:59:39Z)
• Cross-Document Contextual Coreference Resolution in Knowledge Graphs [0.9752323911408618]
  This study introduces an innovative method aimed at identifying and resolving references to the same entities that appear across differing texts.<n>Our method employs a dynamic linking mechanism that associates entities in the knowledge graph with their corresponding textual mentions.
  arXiv  Detail & Related papers  (2025-04-08T07:47:07Z)
• Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning [51.0864247376786]
  We introduce a Knowledge Graph Enhanced Generative Multi-modal model (KG-GMM) that builds an evolving knowledge graph throughout the learning process. During testing, we propose a Knowledge Graph Augmented Inference method that locates specific categories by analyzing relationships within the generated text.
  arXiv  Detail & Related papers  (2025-03-24T07:20:43Z)
• Detecting text level intellectual influence with knowledge graph embeddings [0.0]
  We collect a corpus of open source journal articles and generate Knowledge Graph representations using the Gemini LLM. We attempt to predict the existence of citations between sampled pairs of articles using previously published methods and a novel Graph Neural Network based embedding model.
  arXiv  Detail & Related papers  (2024-10-31T15:21:27Z)
• Bridging Local Details and Global Context in Text-Attributed Graphs [62.522550655068336]
  GraphBridge is a framework that bridges local and global perspectives by leveraging contextual textual information. Our method achieves state-of-theart performance, while our graph-aware token reduction module significantly enhances efficiency and solves scalability issues.
  arXiv  Detail & Related papers  (2024-06-18T13:35:25Z)
• CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation [25.56539617837482]
  A novel context-aware graph-attention model (Context-aware GAT) is proposed. It assimilates global features from relevant knowledge graphs through a context-enhanced knowledge aggregation mechanism. Empirical results demonstrate that our framework outperforms conventional GNN-based language models in terms of performance.
  arXiv  Detail & Related papers  (2023-05-10T16:31:35Z)
• State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
  Graph-level learning has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures. Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
  arXiv  Detail & Related papers  (2023-01-14T09:15:49Z)
• Joint Language Semantic and Structure Embedding for Knowledge Graph Completion [66.15933600765835]
  We propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information. Our method embeds knowledge graphs for the completion task via fine-tuning pre-trained language models. Our experiments on a variety of knowledge graph benchmarks have demonstrated the state-of-the-art performance of our method.
  arXiv  Detail & Related papers  (2022-09-19T02:41:02Z)
• Learning Representations of Entities and Relations [0.0]
  This thesis focuses on improving knowledge graph representation with the aim of tackling the link prediction task. The first contribution is HypER, a convolutional model which simplifies and improves upon the link prediction performance. The second contribution is TuckER, a relatively straightforward linear model, which, at the time of its introduction, obtained state-of-the-art link prediction performance. The third contribution is MuRP, first multi-relational graph representation model embedded in hyperbolic space.
  arXiv  Detail & Related papers  (2022-01-31T09:24:43Z)
• Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis [96.53859361560505]
  We propose a knowledge graph augmented network (KGAN) to incorporate external knowledge with explicitly syntactic and contextual information. KGAN captures the sentiment feature representations from multiple perspectives, i.e., context-, syntax- and knowledge-based. Experiments on three popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN.
  arXiv  Detail & Related papers  (2022-01-13T08:25:53Z)
• Deep Relational Metric Learning [84.95793654872399]
  This paper presents a deep relational metric learning framework for image clustering and retrieval. We learn an ensemble of features that characterizes an image from different aspects to model both interclass and intraclass distributions. Experiments on the widely-used CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate that our framework improves existing deep metric learning methods and achieves very competitive results.
  arXiv  Detail & Related papers  (2021-08-23T09:31:18Z)
• Edge: Enriching Knowledge Graph Embeddings with External Text [32.01476220906261]
  We propose a knowledge graph enrichment and embedding framework named Edge. Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level. To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original graph and augmented graph.
  arXiv  Detail & Related papers  (2021-04-11T03:47:06Z)
• Knowledge Graph Embedding using Graph Convolutional Networks with Relation-Aware Attention [3.803929794912623]
  Knowledge graph embedding methods learn embeddings of entities and relations in a low dimensional space. Various graph convolutional network methods have been proposed which use different types of information to learn the features of entities and relations. We propose a relation-aware graph attention model that leverages relation information to compute different weights to the neighboring nodes for learning embeddings of entities and relations.
  arXiv  Detail & Related papers  (2021-02-14T17:19:44Z)
• Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion [53.31911669146451]
  Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks. These graphs are usually incomplete, urging auto-completion of them. graph embedding approaches, e.g., TransE, learn structured knowledge via representing graph elements into dense embeddings. textual encoding approaches, e.g., KG-BERT, resort to graph triple's text and triple-level contextualized representations.
  arXiv  Detail & Related papers  (2020-04-30T13:50:34Z)
• Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning [73.0598186896953]
  We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training.
  arXiv  Detail & Related papers  (2020-04-29T14:22:42Z)
• A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts [81.4757750425247]
  We study question answering over a dynamic textual environment. We develop a graph neural network over the constructed graph, and train the model in an end-to-end manner.
  arXiv  Detail & Related papers  (2020-04-25T04:53:54Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)
• Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs [96.73259297063619]
  We consider a novel formulation, zero-shot learning, to free this cumbersome curation. For newly-added relations, we attempt to learn their semantic features from their text descriptions. We leverage Generative Adrial Networks (GANs) to establish the connection between text and knowledge graph domain.
  arXiv  Detail & Related papers  (2020-01-08T01:19:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.