80 research outputs found
Analysis-Sensitive Conversion of Administrative Data into Statistical Information Systems
No full textGraphDBLP: a system for analysing networks of computer scientists through graph databases
No full textThis paper presents GraphDBLP, a system that models the DBLP bibliography as a graph database for performing graph-based queries and social network analyses. GraphDBLP also enriches the DBLP data through semantic keyword similarities computed via word-embedding. In this paper, we discuss how the system was formalized as a multi-graph, and how similarity relations were identified through word2vec. We also provide three meaningful queries for exploring the DBLP community to (i) investigate author profiles by analysing their publication records; (ii) identify the most prolific authors on a given topic, and (iii) perform social network analyses over the whole community. To date, GraphDBLP contains 5+ million nodes and 24+ million relationships, enabling users to explore the DBLP data by referencing more than 3.3 million publications, 1.7 million authors, and more than 5 thousand publication venues. Through the use of word-embedding, more than 7.5 thousand keywords and related similarity values were collected. GraphDBLP was implemented on top of the Neo4j graph database. The whole dataset and the source code are publicly available to foster the improvement of GraphDBLP in the whole computer science community
A probabilistic spatio-temporal neural network to forecast COVID-19 counts
No full textGeo-referenced and temporal data are becoming more and more ubiquitous in a wide range of fields such as medicine and economics. Particularly in the realm of medical research, spatio-temporal data play a pivotal role in tracking and understanding the spread and dynamics of diseases, enabling researchers to predict outbreaks, identify hot spots, and formulate effective intervention strategies. To forecast these types of data we propose a Probabilistic Spatio-Temporal Neural Network that (1) estimates, with computational efficiency, models with spatial and temporal components; and (2) combines the flexibility of a Neural Network-which is free from distributional assumptions-with the uncertainty quantification of probabilistic models. Our architecture is compared with the established INLA method, as well as with other baseline models, on COVID-19 data from Italian regions. Our empirical analysis demonstrates the superior predictive effectiveness of our method across multiple temporal ranges and offers insights for shaping targeted health interventions and strategies
The Italian E-Government Plans: Experiences in the Job Marketplace and in Statistical Information Systems
No full textGraphDBLP Released: Querying the Computer Scientists Network as a Graph
No full textIn this paper we introduce GraphDBLP, a tool that models the DBLP bibliography as a graph, and enriches the DBLP data through semantic keyword similarities computed via word-embedding. GraphDBLP has been implemented on top of the Neo4j graph-database, and it can be queried through the Cypher query language. We also provide three meaningful queries for exploring the DBLP community to (i) investigate author profiles by analysing their publication records; (ii) identify the most prolific authors on a given topic,and (iii) perform social network analyses over the whole community. GraphDBLP is available on Github. To date, it contains 5+ million nodes and 24+ million relationships, enabling users to explore the DBLP data by referencing more than 3.3 million publications, 1.7 million authors and more than 5 thousand publication venues. Thanks to the use of word-embedding, more than 7.5 thousand keywords and related similarity values were collected
A Solution to Knowledge Management in Information-Based Services Based on Coopetition, A Case Study Concerning Work Market Services
No full text
Classification of web job advertisements: A case study
No full textThis work is concerned with classifying Web job advertise- ments against a standard classification system of occupations, by apply- ing and comparing different text classification techniques. As a first step, we evaluated the classification algorithms using a hit/not-hit approach, that is either the prediction is correct or not compared to a gold classi- fication provided by domain experts. Then, we built a distance function on top of the affinity relationship between occupations provided by the classification system. Both the classification scores we computed and the affinity distance employed have allowed a more finely grained evaluation of the classified outcomes, providing to authors useful insights towards the improvement of the classification process
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