[ 14] In their contribution Automatic Medical Encoding with SNOMED Categories they present two information retrieval approaches that address both the retrieval of SNOMED CT concepts and the automated encoding of free text and report on a first evaluation of a prototype.
In addition, Big Data problems including semantic indexing, data tagging, and fast information retrieval can be addressed better with the aid of Deep Learning.
Our results address information retrieval's co-authorship network, highly productive authors, highly cited journals and papers, author-assigned keywords, active institutions, and the import of ideas from other disciplines.
Among studies aimed at improving content-based visual information retrieval systems, this one particularly addresses effectiveness improvement (i.e. system's quality improvements through search, setting the automatic and online similarity threshold to be adopted).
Although there are plenty of information extraction and information retrieval tools, few of them can address the two needs that RGD has simultaneously: Ontologies are constantly changing.
This paper addresses the relations between information retrieval (IR) and AI.
More specifically, Big Data problems such as semantic indexing, data tagging, fast information retrieval, and discriminative modeling can be better addressed with the aid of Deep Learning.
The Universal Biological Indexer and Organizer (uBio [ 4]), is a complementary taxonomic information system designed to provide the core services that address name-based impediments to information retrieval [ 5, 6].
The problem of automatically collecting related documents is addressed in Chapter 2, where robust Information Retrieval methods are applied to form high-quality links between documents.
The papers address issues such as Data Science, Big Data, Data Mining, Natural Language Processing, Text Mining, Information Retrieval, Machine Learning, Semantic Web, Ontologies, Web Mining, Knowledge Representation and Linked Open Data, Social Web and Web Science, Information Visualization.
We address that in this work by sketching the information retrieval intellectual landscape through visualizations of citation behaviors.
