Skip Vectors for RDF Data: Extraction Based on the Complexity of Feature Patterns

• URL: http://arxiv.org/abs/2201.01996v2
• Date: Fri, 7 Jan 2022 06:31:02 GMT
• Title: Skip Vectors for RDF Data: Extraction Based on the Complexity of Feature Patterns
• Authors: Yota Minami, Ken Kaneiwa
• Abstract summary: The Resource Description Framework (RDF) is a framework for describing metadata, such as attributes and relationships of resources on the Web. We propose a novel feature vector (called a Skip vector) that represents some features of each resource in an RDF graph by extracting various combinations of neighboring edges and nodes. The classification tasks can be performed by applying the low-dimensional Skip vector of each resource to conventional machine learning algorithms, such as SVMs, the k-nearest neighbors method, neural networks, random forests, and AdaBoost.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Resource Description Framework (RDF) is a framework for describing metadata, such as attributes and relationships of resources on the Web. Machine learning tasks for RDF graphs adopt three methods: (i) support vector machines (SVMs) with RDF graph kernels, (ii) RDF graph embeddings, and (iii) relational graph convolutional networks. In this paper, we propose a novel feature vector (called a Skip vector) that represents some features of each resource in an RDF graph by extracting various combinations of neighboring edges and nodes. In order to make the Skip vector low-dimensional, we select important features for classification tasks based on the information gain ratio of each feature. The classification tasks can be performed by applying the low-dimensional Skip vector of each resource to conventional machine learning algorithms, such as SVMs, the k-nearest neighbors method, neural networks, random forests, and AdaBoost. In our evaluation experiments with RDF data, such as Wikidata, DBpedia, and YAGO, we compare our method with RDF graph kernels in an SVM. We also compare our method with the two approaches: RDF graph embeddings such as RDF2vec and relational graph convolutional networks on the AIFB, MUTAG, BGS, and AM benchmarks.

Related papers

• AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning [9.408189129889006]
  AutoRDF2GML is a framework designed to convert RDF data into data representations tailored for graph machine learning tasks. We present four new benchmark datasets for graph machine learning, created from large RDF knowledge graphs.
  arXiv  Detail & Related papers  (2024-07-26T13:44:06Z)
• RDFGraphGen: A Synthetic RDF Graph Generator based on SHACL Constraints [0.0]
  This paper introduces RDFGraphGen, a domain-independent generator of synthetic RDF graphs based on SHACL constraints. The purpose of RDFGraphGen is the generation of small, medium or large RDF knowledge graphs for the purpose of benchmarking, testing, quality control, training and other similar purposes.
  arXiv  Detail & Related papers  (2024-07-25T10:58:50Z)
• Multimodal Learned Sparse Retrieval with Probabilistic Expansion Control [66.78146440275093]
  Learned retrieval (LSR) is a family of neural methods that encode queries and documents into sparse lexical vectors. We explore the application of LSR to the multi-modal domain, with a focus on text-image retrieval. Current approaches like LexLIP and STAIR require complex multi-step training on massive datasets. Our proposed approach efficiently transforms dense vectors from a frozen dense model into sparse lexical vectors.
  arXiv  Detail & Related papers  (2024-02-27T14:21:56Z)
• Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding [51.75091298017941]
  This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) for attributed graph data. The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets.
  arXiv  Detail & Related papers  (2024-01-12T17:57:07Z)
• RDF-star2Vec: RDF-star Graph Embeddings for Data Mining [1.6492989697868894]
  This study introduces RDF-star2Vec, a novel Knowledge Graph embedding model for RDF-star graphs. We provide a dataset and a benchmarking framework for data mining tasks focused on complex RDF-star graphs.
  arXiv  Detail & Related papers  (2023-12-25T06:32:14Z)
• Learning Implicit Feature Alignment Function for Semantic Segmentation [51.36809814890326]
  Implicit Feature Alignment function (IFA) is inspired by the rapidly expanding topic of implicit neural representations. We show that IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions. Our method can be combined with improvement on various architectures, and it achieves state-of-the-art accuracy trade-off on common benchmarks.
  arXiv  Detail & Related papers  (2022-06-17T09:40:14Z)
• A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features [61.92791503017341]
  Graph Neural Networks (GNNs) with numerical node features and graph structure as inputs have demonstrated superior performance on various supervised learning tasks with graph data. The best models for such data types in most standard supervised learning settings with IID (non-graph) data are not easily incorporated into a GNN. Here we propose a robust stacking framework that fuses graph-aware propagation with arbitrary models intended for IID data.
  arXiv  Detail & Related papers  (2022-06-16T22:46:33Z)
• Relational Graph Convolutional Networks: A Closer Look [1.8428580623654864]
  We describe a reproduction of the Graph Convolutional Network (RGCN) Using our reproduction, we explain the intuition behind the model. Our results empirically validate the correctness of our implementations.
  arXiv  Detail & Related papers  (2021-07-21T11:25:11Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• Graph Neural Networks with Composite Kernels [60.81504431653264]
  We re-interpret node aggregation from the perspective of kernel weighting. We present a framework to consider feature similarity in an aggregation scheme. We propose feature aggregation as the composition of the original neighbor-based kernel and a learnable kernel to encode feature similarities in a feature space.
  arXiv  Detail & Related papers  (2020-05-16T04:44:29Z)
• Towards Exploiting Implicit Human Feedback for Improving RDF2vec Embeddings [2.3605348648054463]
  RDF2vec is a technique for creating vector space embeddings from an RDF knowledge graph. In this paper, we explore the use of external edge weights for guiding the random walks.
  arXiv  Detail & Related papers  (2020-04-09T08:39:19Z)

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.