Towards Foundation Models for Relational Databases [Vision Paper]
- URL: http://arxiv.org/abs/2305.15321v1
- Date: Wed, 24 May 2023 16:37:35 GMT
- Title: Towards Foundation Models for Relational Databases [Vision Paper]
- Authors: Liane Vogel, Benjamin Hilprecht, Carsten Binnig
- Abstract summary: We introduce our vision of relational representation learning, that can not only learn from the full relational structure, but also can scale to larger database sizes.
Overall, we argue that this direction can lead to foundation models for relational databases that are today only available for text and images.
- Score: 15.800326697562841
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Tabular representation learning has recently gained a lot of attention.
However, existing approaches only learn a representation from a single table,
and thus ignore the potential to learn from the full structure of relational
databases, including neighboring tables that can contain important information
for a contextualized representation. Moreover, current models are significantly
limited in scale, which prevents that they learn from large databases. In this
paper, we thus introduce our vision of relational representation learning, that
can not only learn from the full relational structure, but also can scale to
larger database sizes that are commonly found in real-world. Moreover, we also
discuss opportunities and challenges we see along the way to enable this vision
and present initial very promising results. Overall, we argue that this
direction can lead to foundation models for relational databases that are today
only available for text and images.
Related papers
- Knowledge-Aware Reasoning over Multimodal Semi-structured Tables [85.24395216111462]
This study investigates whether current AI models can perform knowledge-aware reasoning on multimodal structured data.
We introduce MMTabQA, a new dataset designed for this purpose.
Our experiments highlight substantial challenges for current AI models in effectively integrating and interpreting multiple text and image inputs.
arXiv Detail & Related papers (2024-08-25T15:17:43Z) - Relational Deep Learning: Graph Representation Learning on Relational
Databases [69.7008152388055]
We introduce an end-to-end representation approach to learn on data laid out across multiple tables.
Message Passing Graph Neural Networks can then automatically learn across the graph to extract representations that leverage all data input.
arXiv Detail & Related papers (2023-12-07T18:51:41Z) - Serving Deep Learning Model in Relational Databases [70.53282490832189]
Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains.
We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks.
The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS)
arXiv Detail & Related papers (2023-10-07T06:01:35Z) - Learning Representations without Compositional Assumptions [79.12273403390311]
We propose a data-driven approach that learns feature set dependencies by representing feature sets as graph nodes and their relationships as learnable edges.
We also introduce LEGATO, a novel hierarchical graph autoencoder that learns a smaller, latent graph to aggregate information from multiple views dynamically.
arXiv Detail & Related papers (2023-05-31T10:36:10Z) - Embeddings for Tabular Data: A Survey [8.010589283146222]
Tabular data comprises rows (samples) with the same set of columns (attributes)
Tables are becoming the natural way of storing data among various industries and academia.
New line of research work applies various learning techniques to support various database tasks.
arXiv Detail & Related papers (2023-02-23T04:37:49Z) - Representing Knowledge by Spans: A Knowledge-Enhanced Model for
Information Extraction [7.077412533545456]
We propose a new pre-trained model that learns representations of both entities and relationships simultaneously.
By encoding spans efficiently with span modules, our model can represent both entities and their relationships but requires fewer parameters than existing models.
arXiv Detail & Related papers (2022-08-20T07:32:25Z) - Causal Reasoning Meets Visual Representation Learning: A Prospective
Study [117.08431221482638]
Lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models.
Inspired by the strong inference ability of human-level agents, recent years have witnessed great effort in developing causal reasoning paradigms.
This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods.
arXiv Detail & Related papers (2022-04-26T02:22:28Z) - BERT Meets Relational DB: Contextual Representations of Relational
Databases [4.029818252558553]
We address the problem of learning low dimension representation of entities on relational databases consisting of multiple tables.
We look into ways of using these attention-based model to learn embeddings for entities in the relational database.
arXiv Detail & Related papers (2021-04-30T11:23:26Z) - Relation-Guided Representation Learning [53.60351496449232]
We propose a new representation learning method that explicitly models and leverages sample relations.
Our framework well preserves the relations between samples.
By seeking to embed samples into subspace, we show that our method can address the large-scale and out-of-sample problem.
arXiv Detail & Related papers (2020-07-11T10:57:45Z) - On Embeddings in Relational Databases [11.52782249184251]
We address the problem of learning a distributed representation of entities in a relational database using a low-dimensional embedding.
Recent methods for learning embedding constitute of a naive approach to consider complete denormalization of the database by relationalizing the full join of all tables and representing as a knowledge graph.
In this paper we demonstrate; a better methodology for learning representations by exploiting the underlying semantics of columns in a table while using the relation joins and the latent inter-row relationships.
arXiv Detail & Related papers (2020-05-13T17:21:27Z)
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.