Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

TBGA: a large-scale Gene-Disease Association dataset for Biomedical Relation Extraction

Background: Databases are fundamental to advance biomedical science. However, most of them are populated and updated with a great deal of human effort. Biomedical Relation Extraction (BioRE) aims to shift this burden to machines. Among its different applications, the discovery of Gene-Disease Associations (GDAs) is one of BioRE most relevant tasks. Nevertheless, few resources have been developed to train models for GDA extraction. Besides, these resources are all limited in size—preventing models from scaling effectively to large amounts of data. Results: To overcome this limitation, we have exploited the DisGeNET database to build a large-scale, semi-automatically annotated dataset for GDA extraction. DisGeNET stores one of the largest available collections of genes and variants involved in human diseases. Relying on DisGeNET, we developed TBGA: a GDA extraction dataset generated from more than 700K publications that consists of over 200K instances and 100K gene-disease pairs. Each instance consists of the sentence from which the GDA was extracted, the corresponding GDA, and the information about the gene-disease pair. Conclusions: TBGA is amongst the largest datasets for GDA extraction. We have evaluated state-of-the-art models for GDA extraction on TBGA, showing that it is a challenging and well-suited dataset for the task. We made the dataset publicly available to foster the development of state-of-the-art BioRE models for GDA extraction

MedTAG: a portable and customizable annotation tool for biomedical documents

Background: Semantic annotators and Natural Language Processing (NLP) methods for Named Entity Recognition and Linking (NER+L) require plenty of training and test data, especially in the biomedical domain. Despite the abundance of unstructured biomedical data, the lack of richly annotated biomedical datasets poses hindrances to the further development of NER+L algorithms for any effective secondary use. In addition, manual annotation of biomedical documents performed by physicians and experts is a costly and time-consuming task. To support, organize and speed up the annotation process, we introduce MedTAG, a collaborative biomedical annotation tool that is open-source, platform-independent, and free to use/distribute. Results: We present the main features of MedTAG and how it has been employed in the histopathology domain by physicians and experts to annotate more than seven thousand clinical reports manually. We compare MedTAG with a set of well-established biomedical annotation tools, including BioQRator, ezTag, MyMiner, and tagtog, comparing their pros and cons with those of MedTag. We highlight that MedTAG is one of the very few open-source tools provided with an open license and a straightforward installation procedure supporting cross-platform use. Conclusions: MedTAG has been designed according to five requirements (i.e. available, distributable, installable, workable and schematic) defined in a recent extensive review of manual annotation tools. Moreover, MedTAG satisfies 20 over 22 criteria specified in the same study

Learning to rank from relevance judgments distributions

LEarning TO Rank (LETOR) algorithms are usually trained on annotated corpora where a single relevance label is assigned to each available document-topic pair. Within the Cranfield framework, relevance labels result from merging either multiple expertly curated or crowdsourced human assessments. In this paper, we explore how to train LETOR models with relevance judgments distributions (either real or synthetically generated) assigned to document-topic pairs instead of single-valued relevance labels. We propose five new probabilistic loss functions to deal with the higher expressive power provided by relevance judgments distributions and show how they can be applied both to neural and gradient boosting machine (GBM) architectures. Moreover, we show how training a LETOR model on a sampled version of the relevance judgments from certain probability distributions can improve its performance when relying either on traditional or probabilistic loss functions. Finally, we validate our hypothesis on real-world crowdsourced relevance judgments distributions. Overall, we observe that relying on relevance judgments distributions to train different LETOR models can boost their performance and even outperform strong baselines such as LambdaMART on several test collections

Credit distribution in relational scientific databases

Digital data is a basic form of research product for which citation, and the generation of credit or recognition for authors, are still not well understood. The notion of data credit has therefore recently emerged as a new measure, defined and based on data citation groundwork. Data credit is a real value representing the importance of data cited by a research entity. We can use credit to annotate data contained in a curated scientific database and then as a proxy of the significance and impact of that data in the research world. It is a method that, together with citations, helps recognize the value of data and its creators. In this paper, we explore the problem of Data Credit Distribution, the process by which credit is distributed to the database parts responsible for producing data being cited by a research entity. We adopt as use case the IUPHAR/BPS Guide to Pharmacology (GtoPdb), a widely-used curated scientific relational database. We focus on Select-Project-Join (SPJ) queries under bag semantics, and we define three distribution strategies based on how-provenance, responsibility, and the Shapley value. Using these distribution strategies, we show how credit can highlight frequently used database areas and how it can be used as a new bibliometric measure for data and their curators. In particular, credit rewards data and authors based on their research impact, not only on the citation count. We also show how these distribution strategies vary in their sensitivity to the role of an input tuple in the generation of the output data and reward input tuples differently

Data citation and the citation graph

The citation graph is a computational artifact that is widely used to represent the domain of published literature. It represents connections between published works, such as citations and authorship. Among other things, the graph supports the computation of bibliometric measures such as h-indexes and impact factors. There is now an increasing demand that we should treat the publication of data in the same way that we treat conventional publications. In particular, we should cite data for the same reasons that we cite other publications. In this paper we discuss what is needed for the citation graph to represent data citation. We identify two challenges: to model the evolution of credit appropriately (through references) over time and to model data citation not only to a data set treated as a single object but also to parts of it. We describe an extension of the current citation graph model that addresses these challenges. It is built on two central concepts: citable units and reference subsumption. We discuss how this extension would enable data citation to be represented within the citation graph and how it allows for improvements in current practices for bibliometric computations, both for scientific publications and for data