Semantic Preserving Embeddings for Generalized Graphs

TL;DR

This paper introduces a neural encoding approach to generate semantic-preserving vector embeddings for generalized graphs, enabling improved machine learning tasks and efficient large-scale graph queries.

Contribution

It presents a novel neural encoding method that captures semantic and topological features of generalized graphs for enhanced data analysis and query efficiency.

Findings

Embeddings maintain semantic characteristics of original graphs.

Improved performance in link discovery and entity retrieval tasks.

Enhanced methods for long-distance queries on large datasets.

Abstract

A new approach to the study of Generalized Graphs as semantic data structures using machine learning techniques is presented. We show how vector representations maintaining semantic characteristics of the original data can be obtained from a given graph using neural encoding architectures and considering the topological properties of the graph. Semantic features of these new representations are tested by using some machine learning tasks and new directions on efficient link discovery, entitity retrieval and long distance query methodologies on large relational datasets are investigated using real datasets. ---- En este trabajo se presenta un nuevo enfoque en el contexto del aprendizaje autom\'atico multi-relacional para el estudio de Grafos Generalizados. Se muestra c\'omo se pueden obtener representaciones vectoriales que mantienen caracter\'isticas sem\'anticas del grafo original utilizando codificadores neuronales y considerando las propiedades topol\'ogicas del grafo. Adem\'as, se eval\'uan las caracter\'isticas sem\'anticas capturadas por estas nuevas representaciones y se investigan nuevas metodolog\'ias eficientes relacionadas con Link Discovery, Entity Retrieval y consultas a larga distancia en grandes conjuntos de datos relacionales haciendo uso de bases de datos reales.