Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science. Science is increasingly becoming "data-intensive," where large volumes of data are collected and analyzed to discover complex patterns through simulations and experiments, and most scientific reference works have been replaced by online curated data sets. Yet, given a data set, there is no quantitative, consistent, and established way of knowing how it has been used over time, who contributed to its curation, what results have been yielded, or what value it has. The development of a theory and practice of data citation is fundamental for considering data as first-class research objects with the same relevance and centrality of traditional scientific products. Many works in recent years have discussed data citation from different viewpoints: illustrating why data citation is needed, defining the principles and outlining recommendations for data citation systems, and providing computational methods for addressing specific issues of data citation. The current panorama is many-faceted and an overall view that brings together diverse aspects of this topic is still missing. Therefore, this paper aims to describe the lay of the land for data citation, both from the theoretical (the why and what) and the practical (the how) angle.
The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized images and free-text diagnostic reports does not scale with the need for large datasets required to train robust computer-aided diagnosis methods that can target the high variability of clinical cases and data produced. This work proposes and evaluates an approach to eliminate the need for manual annotations to train computer-aided diagnosis tools in digital pathology. The approach includes two components, to automatically extract semantically meaningful concepts from diagnostic reports and use them as weak labels to train convolutional neural networks (CNNs) for histopathology diagnosis. The approach is trained (through 10-fold cross-validation) on 3’769 clinical images and reports, provided by two hospitals and tested on over 11’000 images from private and publicly available datasets. The CNN, trained with automatically generated labels, is compared with the same architecture trained with manual labels. Results show that combining text analysis and end-to-end deep neural networks allows building computer-aided diagnosis tools that reach solid performance (micro-accuracy = 0.908 at image-level) based only on existing clinical data without the need for manual annotations.
Keyword-based access to structured data has been gaining traction both in research and industry as a means to facilitate access to information. In recent years, the research community and big data technology vendors have put much effort into developing new approaches for keyword search over structured data. Accessing these data through structured query languages, such as SQL or SPARQL, can be hard for end-users accustomed to Web-based search systems. To overcome this issue, keyword search in databases is becoming the technology of choice, although its efficiency and effectiveness problems still prevent a large scale diffusion. In this work, we focus on graph data, and we propose the TSA+BM25 and the TSA+VDP keyword search systems over RDF datasets based on the ''virtual documents'' approach. This approach enables high scalability because it moves most of the computational complexity off-line and then exploits highly efficient text retrieval techniques and data structures to carry out the on-line phase. Nevertheless, text retrieval techniques scale well to large datasets but need to be adapted to the complexity of structured data. The new approaches we propose are more efficient and effective compared to state-of-theart systems. In particular, we show that our systems scale to work with RDF datasets composed of hundreds of millions of triples and obtain competitive results in terms of effectiveness. INDEX TERMS Information retrieval, database, keyword search, RDF datasets, search methods, triples (data structure).
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