Embedding Approaches for Relational Data

Embedding methods for searching latent representations of the data are very important tools for unsupervised and supervised machine learning as well as informa-.

[2005.06437] On Embeddings in Relational Databases - arXiv

Low-dimensional embeddings aim to encapsulate a concise vector representation for an underlying dataset with minimum loss of information.

Identifying Embedding Spaces for Relational Data - Princeton Math

Methods such as hyperbolic embeddings [9, 10, 6] and Riemannian generative models [7, 5] showed that non-. Euclidean geometries can provide significant ...

Observatory: Characterizing Embeddings of Relational Tables

ABSTRACT. Language models and specialized table embedding models have recently demonstrated strong performance on many tasks over tabular data.

Graph Embedding 101: Unraveling the Magic of Relational Data

Neural Networks: The neural revolution hasn't spared graph embeddings. Approaches like GraphSAGE and GCNs employ neural encoders, which, when ...

Relational Data Embeddings for Feature Enrichment with ... - HAL

We represent the relational data on the entities as a graph and adapt graph-embedding methods to create feature vectors for each entity. We show ...

Translating Embeddings for Modeling Multi-relational Data - NIPS

relational data, most existing methods for multi-relational data have been designed within the frame- work of relational learning from latent attributes, as ...

Embedding Multimodal Relational Data for Knowledge Base ...

Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however ...

EmbDI: Generating Embeddings for Relational Data Integration

Deep learning techniques have been used with promising results for data integration problems. Some methods use pre-trained embeddings that were trained on a ...

Embedding Multimodal Relational Data

Due to these deficiencies, learning the relational knowledge representation has been a focus of active research [1, 2, 32, 9, 19, 26, 3]. These approaches ...

Embedding models for relational data analytics - TEL - HAL Thèses

Analytical pipelines, such as those relying on machine learning models, typically require data in the form of a single table describing the entities under ...

Embedding Approaches for Relational Data - CORE

In this thesis, we examine the problem of developing efficient and reliable embedding methods for revealing, understanding, and exploiting the ...

soda-inria/ken_embeddings: KEN: Relational Data Embeddings

We represent the relational data on the entities as a graph and adapt graph-embedding methods to create feature vectors for each entity. We show that two ...

[1909.01120] Local Embeddings for Relational Data Integration - arXiv

However, this approach blindly treats a tuple as a sentence, thus losing a large amount of contextual information present in the tuple. We ...

Multi-relational Data and Knowledge Graphs

In this chapter, we will continue our focus on shallow embedding methods, and we will introduce techniques to deal with multi-relational graphs. Knowledge graph ...

Translating Embeddings for Modeling Multi-relational Data - NIPS

We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a ...

Embedding Approaches for Relational Data - R Discovery

Embedding approaches are very popular in this field, they typically encode objects and relation types with hidden representations and use the ...

Relational Graph Embeddings for Table Retrieval

We evaluate our approach using two large collections of tables from public. WikiTables and Web tables data, demonstrating substantial improvements over state-of ...

Relational Data Embeddings for Feature Enrichment with ...

We represent the relational data on the entities as a graph and adapt graph-embedding methods to create feature vectors for each entity. We show ...

Selecting Walk Schemes for Database Embedding

Towards that, a common practice is to embed components of structured data into a high-dimensional vector space. We study the embedding of the ...