Generalized Graph Pattern Matching

by   Pedro Almagro-Blanco, et al.

Most of the machine learning algorithms are limited to learn from flat data: a recordset with prefixed structure. When learning from a record, these types of algorithms don't take into account other objects even though they are directly connected to it and can provide valuable information for the learning task. In this paper we present the concept of Generalized Graph Query, a query tool over graphs or multi-relational data structures. They are built using the same graph structure as generalized graphs and allow to express powerful relational and non-relational restrictions on this type of data. Also, this paper shows mechanisms to build this kind of queries dynamically and how they can be used to perform bottom-up discovery processes through machine laerning techniques. ----- La mayoría de los algoritmos que aprenden a partir de datos están limitados ya que sólo son capaces de aprender a partir de datos estructurados en forma de tabla en la que cada fila representa un registro y cada columna una propiedad asociada. Estos algoritmos, no tienen en cuenta los atributos de las estructuras con las que un registro dado puede estar relacionado, a pesar de que éstos pueden aportar información útil a la hora de llevar a cabo la tarea de aprendizaje. En este artículo presentamos el concepto de Generalized Graph Query, una herramienta de consulta de patrones en grafos generalizados. Dicha herramienta ha sido construida utilizando la estructura de Grafo Generalizado y permite expresar restricciones relacionales y no relacionales sobre este tipo de estructuras. Además, en este artículo se presentan mecanismos para la construcción automática de este tipo de consultas y se muestra cómo éstas pueden ser utilizadas en procesos de descubrimiento tipo bottom-up a través de técnicas relacionadas con el Aprendizaje Automático.


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