1,720,977 research outputs found
Open Access and Database Anonymization an Open Source Procedure Based on an Italian Case Study
Get PDFThe 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
Neural Networks forBuilding Semantic Models and Knowledge Graphs
Get PDFL'abstract è presente nell'allegato / the abstract is in the attachmen
On the Integration of Knowledge Graphs into Deep Learning Models for a More Comprehensible AI—Three Challenges for Future Research
Get PDFDeep 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
SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks
Get PDFSeMi (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
Get PDFThis 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
Get PDFIn 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
ContrattiPubblici.org, a Semantic Knowledge Graph on Public Procurement Information
No full textThe 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
Towards Named Entity Disambiguation with Graph Embeddings
No full textExtracting structured knowledge from scientific literature is crucial for helping professionals make well-informed decisions. This paper presents an approach to distilling knowledge from biomedical documents within the context of Named Entity Disambiguation (NED). The proposed method leverages a joint representation of biomedical entities, combining pre-trained language models with graph machine learning techniques. A Siamese Neural Network (SNN) is trained to optimize this joint representation by integrating the contextual text embeddings of entity mentions with the graph embeddings of corresponding canonical entities in a biomedical Knowledge Graph (KG). During the inference phase, the SNN model assigns a score to this joint representation to disambiguate the target entity among a set of candidates. To the best of our knowledge, this is the first NED method in the biomedical domain that incorporates graph embeddings using a neural model. We empirically evaluated the effectiveness of our approach against well-known biomedical datasets, such as MedMentions and BC5CDR. The results demonstrate a promising direction in utilizing the relational knowledge captured by graph embeddings for the NED task
Removing Barriers to Transparency: a Case Study on the Use of Semantic Technologies to Tackle Procurement Data Inconsistency
No full textPublic Procurement (PP) information, made available as Open Government Data (OGD), leads to tangible benefits to identify government spending for goods and services. Nevertheless, making data freely available is a necessary, but not sufficient condition for improving transparency. Fragmentation of OGD due to diverse processes adopted by different administrations and inconsistency within data affect opportunities to obtain valuable information. In this article, we propose a solution based on linked data to integrate existing datasets and to enhance information coherence. We present an application of such principles through a semantic layer built on Italian PP information available as OGD. As result, we overcame the fragmentation of datasources and increased the consistency of information, enabling new opportunities for analyzing data to fight corruption and for raising competition between companies in the market
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