Open Access and Database Anonymization an Open Source Procedure Based on an Italian Case Study

The only method, believed to be compliant to privacy laws, to open a database that contains personal data is anonymization. This work is focused on a car accidents database case study and on the Italian DP law. Database anonymization is described from a procedural point of view and it is explained how it is possible to complete the whole process relying solely on widespread open-source software applications. The proposed approach is empirical and is founded on the letter of the Italian privacy law

SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks

SeMi (SEmantic Modeling machIne) is a tool to semi-automatically build large-scale Knowledge Graphs from structured sources such as CSV, JSON, and XML files. To achieve such a goal, SeMi builds the semantic models of the data sources, in terms of concepts and relations within a domain ontology. Most of the research contributions on automatic semantic modeling is focused on the detection of semantic types of source attributes. However, the inference of the correct semantic relations between these attributes is critical to reconstruct the precise meaning of the data. SeMi covers the entire process of semantic modeling: (i) it provides a semi-automatic step to detect semantic types; (ii) it exploits a novel approach to inference semantic relations, based on a graph neural network trained on background linked data. At the best of our knowledge, this is the first technique that exploits a graph neural network to support the semantic modeling process. Furthermore, the pipeline implemented in SeMi is modular and each component can be replaced to tailor the process to very specific domains or requirements. This contribution can be considered as a step ahead towards automatic and scalable approaches for building Knowledge Graphs

Exploiting Linked Open Data and Natural Language Processing for Classification of Political Speech

This paper shows the effectiveness of a DBpedia-based approach for text categorization in the e-government field. Our use case is the analysis of all the speech transcripts of current White House members. This task is performed by means of TellMeFirst, an open-source software that leverages the DBpedia knowledge base and the English Wikipedia linguistic corpus for topic extraction. Analysis results allow to identify the main political trends addressed by the White House, increasing the citizens' awareness to issues discussed by politicians. Unlike methods based on string recognition, TellMeFirst semantically classifies documents through DBpedia URIs, gathering all the words that belong to a similar area of meaning (such as synonyms, hypernyms and hyponyms of a lemma) under the same unambiguous concept

Visualizing Internet-Measurements Data for Research Purposes: the NeuViz Data Visualization Tool

In this paper we present NeuViz, a data processing and visualization architecture for network measurement experiments. NeuViz has been tailored to work on the data produced by Neubot (Net Neutrality Bot), an Internet bot that performs periodic, active network performance tests. We show that NeuViz is an effective tool to navigate Neubot data to identify cases (to be investigated with more specific network tests) in which a protocol seems discriminated. Also, we suggest how the information provided by the NeuViz Web API can help to automatically detect cases in which a protocol seems discriminated, to raise warnings or trigger more specific test

Training Neural Language Models with SPARQL queries for Semi-Automatic Semantic Mapping

Abstract Knowledge graphs are labeled and directed multi-graphs that encode information in the form of entities and relationships. They are gaining attention in different areas of computer science: from the improvement of search engines to the development of virtual personal assistants. Currently, an open challenge in building large-scale knowledge graphs from structured data available on the Web (HTML tables, CSVs, JSONs) is the semantic integration of heterogeneous data sources. In fact, such diverse and scattered information rarely provide a formal description of metadata that is required to accomplish the integration task. In this paper we propose an approach based on neural networks to reconstruct the semantics of data sources to produce high quality knowledge graphs in terms of semantic accuracy. We developed a neural language model trained on a set of SPARQL queries performed on knowledge graphs. Through this model it is possible to semi-automatically generate a semantic map between the attributes of a data source and a domain ontology

The NeuViz Data Visualization Tool for Visualizing Internet-Measurements Data

In this paper we present NeuViz, a data processing and visualization architecture for network measurement experiments. NeuViz has been tailored to work on the data produced by Neubot (Net Neutrality Bot), an Internet bot that performs periodic, active network performance tests. We show that NeuViz is an effective tool to navigate Neubot data to identify cases (to be investigated with more specific network tests) in which a protocol seems discriminated. Also, we suggest how the information provided by the NeuViz Web API can help to automatically detect cases in which a protocol seems discriminated, to raise warnings or trigger more specific tests

Modeling the semantics of data sources with graph neural networks

Semantic models are fundamental to publish datainto Knowledge Graphs (KGs), since they encodethe precise meaning of data sources, through con-cepts and properties defined within reference on-tologies. However, building semantic models re-quires significant manual effort and expertise. Inthis paper, we present a novel approach based onGraph Neural Networks (GNNs) to build seman-tic models of data sources. GNNs are trained onLinked Data (LD) graphs, which serve as back-ground knowledge to automatically infer the se-mantic relations connecting the attributes of a datasource. At the best of our knowledge, this is thefirst approach that employs GNNs to identify thesemantic relations. We tested our approach on 15target sources from the advertising domain (usedin other studies in the literature), and comparedits performance against two baselines and a tech-nique largely used in the state of the art. Theevaluation showed that our approach outperformsthe state of the art in cases of data source withthe largest amount of semantic relations definedin the ground truth

EXPERIMENTAL MEASURES OF BUS COMFORT LEVELS USING KINEMATIC PARAMETERS RECORDED BY SMARTPHONE

[EN] Comfort on board plays an essential role in the levels of satisfaction of a bus service perceived by passengers. The aim of this paper is to propose a measure of comfort based on two kinds of data: perceptions of passengers (subjective data) and accelerations of bus (objective data). For the collection of subjective data a questionnaire was addressed to a sample of university students, while a smartphone, equipped with GPS device and 3-axis accelerometer, was used to record the accelerations. Based on the recorded parameters, we determined the thresholds of the acceleration values beyond which the level of comfort cannot be considered as good.Dell'aquila, S.; Eboli, L.; Futia, G.; Mazzulla, G.; Pungillo, G. (2016). EXPERIMENTAL MEASURES OF BUS COMFORT LEVELS USING KINEMATIC PARAMETERS RECORDED BY SMARTPHONE. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 2240-2247. https://doi.org/10.4995/CIT2016.2015.3203OCS2240224

On the Integration of Knowledge Graphs into Deep Learning Models for a More Comprehensible AI—Three Challenges for Future Research

Deep learning models contributed to reaching unprecedented results in prediction and classification tasks of Artificial Intelligence (AI) systems. However, alongside this notable progress, they do not provide human-understandable insights on how a specific result was achieved. In contexts where the impact of AI on human life is relevant (e.g., recruitment tools, medical diagnoses, etc.), explainability is not only a desirable property, but it is -or, in some cases, it will be soon-a legal requirement. Most of the available approaches to implement eXplainable Artificial Intelligence (XAI) focus on technical solutions usable only by experts able to manipulate the recursive mathematical functions in deep learning algorithms. A complementary approach is represented by symbolic AI, where symbols are elements of a lingua franca between humans and deep learning. In this context, Knowledge Graphs (KGs) and their underlying semantic technologies are the modern implementation of symbolic AI—while being less flexible and robust to noise compared to deep learning models, KGs are natively developed to be explainable. In this paper, we review the main XAI approaches existing in the literature, underlying their strengths and limitations, and we propose neural-symbolic integration as a cornerstone to design an AI which is closer to non-insiders comprehension. Within such a general direction, we identify three specific challenges for future research—knowledge matching, cross-disciplinary explanations and interactive explanations

ContrattiPubblici.org, a Semantic Knowledge Graph on Public Procurement Information

The Italian anti-corruption Act (law n. 190/2012) requires all public administrations to spread procurement information as open data. Each body is therefore obliged to yearly release standardized XML files, on its public website, containing data that describe all issued public contracts. Though this information is currently available on a machine- readable format, the data is fragmented and published in different files on different websites, without a unified and human-readable view of the information. The ContrattiPubblici.org project aims at developing a se- mantic knowledge graph based on linked open data principles in order to overcome the fragmentation of existent datasets and to allow easy anal- ysis and the reuse of information. The objectives are to increase public awareness about public spending, to improve transparency on the public procurement chain and to help companies to retrieve useful knowledge for their business activities