Knowledge extraction from scientific and technological sources
Currently, innovation, development and research activities, and in particular science and technology, generate large amount of information derived from the scientific method, such as articles, theses, patents, clinical trials, among others. In fact, the total volume increases year by year, following a constant growth pattern.
In this sense, the global system of science and technology can be understood as a complex structure in which academic and industrial actors, and the global knowledge generated, relate to each other, and can be represented as a network or graph. Thus, there can be nodes related to people (researchers), institutions, countries, documents, concepts, journals, etc. Between these, different types of relationships can be established. Furthermore, in this network, relationships are heterogeneous and dynamic, i.e., relationships are not only established between two nodes of the same type, but maybe involved in different types of nodes, and these may change over time.
Traditionally, the extraction of knowledge from this type of data has been developed using a bibliometric perspective. But now, it is necessary to design and develop new models, algorithms and software tools capable of extracting hidden knowledge from large volumes of information with a multitude of heterogeneous and dynamic relationships between its components employing techniques and method from artificial intelligence, machine learning and complex systems.
Responsible: Manuel Jesús Cobo Martín
|Cabrerizo Lorite, Francisco Javier||cabrerizo@decsaGwRJHIm2qfi.ugr.es||Computational Intelligence Area||PhD|
|Cobo Martín, Manuel Jesús||manueljesus.cobo@u@A1kOApca.es||DaSCI Technology Applications Area||PhD|
|Herrera Viedma, Enriquefirstname.lastname@example.orgTebwr.es||Computational Intelligence Area||PhD|