A number of accessible RDF stores are populating the linked open data world. The navigation on data reticular relationships is becoming every day more relevant. Several knowledge base present relevant links to common vocabularies while many others are going to be discovered increasing the reasoning capabilities of our knowledge base applications. In this paper, the Linked Open Graph, LOG, is presented. It is a web tool for collaborative browsing and navigation on multiple SPARQL entry points. The paper presented an overview of major problems to be addressed, a comparison with the state of the arts tools, and some details about the LOG graph computation to cope with high complexity of large Linked Open Dada graphs. The LOG.disit.org tool is also presented by means of a set of examples involving multiple RDF stores and putting in evidence the new provided features and advantages using dbPedia, Getty, Europeana, Geonames, etc. The LOG tool is free to be used, and it has been adopted, developed and/or improved in multiple projects: such as ECLAP for social media cultural heritage, Sii-Mobility for smart city, and ICARO for cloud ontology analysis, OSIM for competence / knowledge mining and analysis. Keywords LOD, LOD browsing, knowledge base browsing, SPARQL entry points.
From Exploratory Search to Web Search and back - PIKM 2010Roku
The power of search is with no doubt one of the main aspects for the success of the Web. Currently available search engines on the Web allow to return results with a high precision. Nevertheless, if we limit our attention only to lookup search we are missing another important search task. In exploratory search, the user is willing not only to find documents relevant with respect to her query but she is also interested in learning, discovering and understanding novel knowledge on complex and sometimes unknown topics.
In the paper we address this issue presenting LED, a web based system that aims to improve (lookup) Web search by enabling users to properly explore knowledge associated to her query. We rely on DBpedia to explore the semantics of keywords within the query thus suggesting potentially interesting related topics/keywords to the user.
Semantic Tags Generation and Retrieval for Online Advertising - CIKM 2010Roku
One of the main problems in online advertising is to display ads which are relevant and appropriate \wrt what the user is looking for. Often search engines fail to reach this goal as they do not consider semantics attached to keywords. In this paper we propose a system that tackles the problem by two different angles: help (i) advertisers to create more efficient ads campaigns and (ii) ads providers to properly match ads content to keywords in search engines.
We exploit semantic relations stored in the DBpedia dataset and use an hybrid ranking system to rank keywords and to expand queries formulated by the user. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results and social tagging systems.
We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users.
Jana Parvanova, Vladimir Alexiev and Stanislav Kostadinov. In workshop Collaborative Annotations in Shared Environments: metadata, vocabularies and techniques in the Digital Humanities (DH-CASE 2013). Collocated with DocEng 2013. Florence, Italy, Sep 2013.
Open Knowledge Foundation Edinburgh meet-up #3Gill Hamilton
Lightning talks by
Gordon Dunsire on library standards and linked data
Gill Hamilton on recent initiatives with open and linked open data at National Library of Scotland
From Exploratory Search to Web Search and back - PIKM 2010Roku
The power of search is with no doubt one of the main aspects for the success of the Web. Currently available search engines on the Web allow to return results with a high precision. Nevertheless, if we limit our attention only to lookup search we are missing another important search task. In exploratory search, the user is willing not only to find documents relevant with respect to her query but she is also interested in learning, discovering and understanding novel knowledge on complex and sometimes unknown topics.
In the paper we address this issue presenting LED, a web based system that aims to improve (lookup) Web search by enabling users to properly explore knowledge associated to her query. We rely on DBpedia to explore the semantics of keywords within the query thus suggesting potentially interesting related topics/keywords to the user.
Semantic Tags Generation and Retrieval for Online Advertising - CIKM 2010Roku
One of the main problems in online advertising is to display ads which are relevant and appropriate \wrt what the user is looking for. Often search engines fail to reach this goal as they do not consider semantics attached to keywords. In this paper we propose a system that tackles the problem by two different angles: help (i) advertisers to create more efficient ads campaigns and (ii) ads providers to properly match ads content to keywords in search engines.
We exploit semantic relations stored in the DBpedia dataset and use an hybrid ranking system to rank keywords and to expand queries formulated by the user. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results and social tagging systems.
We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users.
Jana Parvanova, Vladimir Alexiev and Stanislav Kostadinov. In workshop Collaborative Annotations in Shared Environments: metadata, vocabularies and techniques in the Digital Humanities (DH-CASE 2013). Collocated with DocEng 2013. Florence, Italy, Sep 2013.
Open Knowledge Foundation Edinburgh meet-up #3Gill Hamilton
Lightning talks by
Gordon Dunsire on library standards and linked data
Gill Hamilton on recent initiatives with open and linked open data at National Library of Scotland
Digital Humanities in a Linked Data World - Semnantic AnnotationsDov Winer
Presentation by Dov Winer at the 1st International Seminar on Digital Humanities
University of Sao Paulo, Brazil, 23-25 October 2013
Primeiro Seminario Internacional em Humanidades Digitais,
Universidade Sao Paulo, Biblioteca Brasiliana Mindlin23-25 de Outubro 2013
Digital Humanities in a Linked Data World - Semantic Annotations
Dov Winer
1st International Seminar on Digital Humanities
University of Sao Paulo - Brasiliana Mindlin Library
October 2013
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
Introduction to the Data Web, DBpedia and the Life-cycle of Linked DataSören Auer
Over the past 4 years, the Semantic Web activity has gained momentum with the widespread publishing of structured data as RDF. The Linked Data paradigm has therefore evolved from a practical research idea into
a very promising candidate for addressing one of the biggest challenges
of computer science: the exploitation of the Web as a platform for data
and information integration. To translate this initial success into a
world-scale reality, a number of research challenges need to be
addressed: the performance gap between relational and RDF data
management has to be closed, coherence and quality of data published on
the Web have to be improved, provenance and trust on the Linked Data Web
must be established and generally the entrance barrier for data
publishers and users has to be lowered. This tutorial will discuss
approaches for tackling these challenges. As an example of a successful
Linked Data project we will present DBpedia, which leverages Wikipedia
by extracting structured information and by making this information
freely accessible on the Web. The tutorial will also outline some recent advances in DBpedia, such as the mappings Wiki, DBpedia Live as well as
the recently launched DBpedia benchmark.
Analysing & Improving Learning Resources Markup on the WebStefan Dietze
Talk at WWW2017 on LRMI adoption, quality and usage. Full paper here: http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/companion/p283.pdf.
ICT e per la Gestione soccorso integrato nelle maxi emergenzePaolo Nesi
ICT e per la Gestione
Corso per il Master di I Livello, “ Il soccorso integrato nelle maxi emergenze: il management sanitario”, AA. 2012‐2013
Paolo Nesi, Ivan Bruno
Distributed and Internet Technology Lab, Dipartimento di Ingegneria dell’informazione
Paolo.nesi@unifi.it, Http://www.disit.dinfo.unifi.it
Parti: Le reti di calcolatori, Protocolli ed Internet, WEB, Architetture Client Server, Pagine HTML, Comunicazioni Wireless e protocolli, Reti WiFi e Cellulari, Esercitazioni varie, Comunicazioni in condizioni di emergenza, Sistemi di Comunicazione Satellitari, Sistemi Operativi per Sistemi Mobili, Sensori dei Sistemi Mobili, La proposta di Mobile Emergency
Digital Humanities in a Linked Data World - Semnantic AnnotationsDov Winer
Presentation by Dov Winer at the 1st International Seminar on Digital Humanities
University of Sao Paulo, Brazil, 23-25 October 2013
Primeiro Seminario Internacional em Humanidades Digitais,
Universidade Sao Paulo, Biblioteca Brasiliana Mindlin23-25 de Outubro 2013
Digital Humanities in a Linked Data World - Semantic Annotations
Dov Winer
1st International Seminar on Digital Humanities
University of Sao Paulo - Brasiliana Mindlin Library
October 2013
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
Introduction to the Data Web, DBpedia and the Life-cycle of Linked DataSören Auer
Over the past 4 years, the Semantic Web activity has gained momentum with the widespread publishing of structured data as RDF. The Linked Data paradigm has therefore evolved from a practical research idea into
a very promising candidate for addressing one of the biggest challenges
of computer science: the exploitation of the Web as a platform for data
and information integration. To translate this initial success into a
world-scale reality, a number of research challenges need to be
addressed: the performance gap between relational and RDF data
management has to be closed, coherence and quality of data published on
the Web have to be improved, provenance and trust on the Linked Data Web
must be established and generally the entrance barrier for data
publishers and users has to be lowered. This tutorial will discuss
approaches for tackling these challenges. As an example of a successful
Linked Data project we will present DBpedia, which leverages Wikipedia
by extracting structured information and by making this information
freely accessible on the Web. The tutorial will also outline some recent advances in DBpedia, such as the mappings Wiki, DBpedia Live as well as
the recently launched DBpedia benchmark.
Analysing & Improving Learning Resources Markup on the WebStefan Dietze
Talk at WWW2017 on LRMI adoption, quality and usage. Full paper here: http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/companion/p283.pdf.
ICT e per la Gestione soccorso integrato nelle maxi emergenzePaolo Nesi
ICT e per la Gestione
Corso per il Master di I Livello, “ Il soccorso integrato nelle maxi emergenze: il management sanitario”, AA. 2012‐2013
Paolo Nesi, Ivan Bruno
Distributed and Internet Technology Lab, Dipartimento di Ingegneria dell’informazione
Paolo.nesi@unifi.it, Http://www.disit.dinfo.unifi.it
Parti: Le reti di calcolatori, Protocolli ed Internet, WEB, Architetture Client Server, Pagine HTML, Comunicazioni Wireless e protocolli, Reti WiFi e Cellulari, Esercitazioni varie, Comunicazioni in condizioni di emergenza, Sistemi di Comunicazione Satellitari, Sistemi Operativi per Sistemi Mobili, Sensori dei Sistemi Mobili, La proposta di Mobile Emergency
ECLAP 2013 tutorial at Porto, April 2013Paolo Nesi
ECLAP tutorial, from metadata to architecutre, from lod and social media to content aggregation, from semantic computing to automated content processing, social graph, . mystoryplayer
Modelli Semantici e Gestione della Conoscenza: Social Network vs Knowledge Ma...Paolo Nesi
Modelli Semantici e Gestione della Conoscenza: Social Network vs Knowledge Management Systems. Seminario per il dottorato in Ingegneria Informatica e delle telecomunicazioni, Univ. Studi di Firenze, 2013
DISIT lab Overview on Tourism and Training, June 2014Paolo Nesi
Semantic Model and Tools
• OSIM, knowledge mining, competence management
• RDF stores, Open Data, Linked Open Data management
• Content Tools
• Content modeling, indexing and querying, recommendations
• Content management and distribution
• Intellectual property management and conditional access (enabling
and actuating legal control)
• Devices:
• iOS Apple, Android, Windows Phone, Smart TV
• Tourism
• Open Data and Smart City data
• Training E‐learning
• Tools for e‐learning/distance/blended learning, anywhere learning,
certified e‐learning, social learning and networking
Technologies for Enhancing Knowledge and Training, the future of e-learning t...Paolo Nesi
E-learning infrastructure
Tools for e-learning/distance learning
References
Support for blended learning
Distance testing and assessment
Advanced aspects with respect to Moodle, Atutor, etc.
Content production and development
Content management and distribution
anywhere learning
certified e-learning
social learning and networking
Knowledge management
Legal, Intellectual property management and conditional access (enabling and actuating legal control)
Scalability: Hardware/Software
ECLAP: life long learning, social learning
http://www.eclap.eu
FirstClass: certified blended learning, paid courses
http://fad.fclass.it
IUF.Csavri: blended learning for entrepreneurs incubation and spin-offs
http://iuf.csavri.org
APRETOSCANA: formation for researchers
http://www.apretoscana.org
DISIT.DINFO.UNIFI.IT: research management and dissemination
http://www.disit.dinfo.uinifi.it
OSIM.UNIFI: knowledge management for UNIFI
http://openmind.disit.org
DISIT Lab overview: smart city, big data, semantic computing, cloudPaolo Nesi
Smart City
• Projects: http://www.disit.org/5501
– Sii-Mobility, http://www.sii-mobility.org
– Service Map: http://servicemap.disit.org
– Social Innovation: Coll@bora http://www.disit.org/5479
– Navigation Indoor/outdoor: Mobile Emergency http://www.disit.org/5404
– Mobility and Transport: TRACE-IT, RAISSS, TESYSRAIL
• Tools: http://www.disit.org/5489
– Data gathering, data mining and reconciliation
– Data reasoning, deduction, prediction
– Smart city ontology and reasoning tools
– Service analysis and recommendations
– Autonomous train operator, train signaling
– Risk analysis, decision support systems
– Mobile Applications
Data Analytics - Big data
• Projects: http://www.disit.org/5501
– Linked Open Graph: http://LOG.disit.org
– Sii-Mobility, http://www.sii-mobility.org
– Service on a number of projects
• Tools: http://www.disit.org/5489
– Open data and Linked Open Data
– LOG LOD service and tools
– Data mining and reconciliation
– Data reasoning, deduction, prediction, decision support
– SN Analysis and recommendations
– User behavior monitoring and analysis
Smart Cloud - Computing
• Projects: http://www.disit.org/5501
– ICARO: http://www.disit.org/5482
– Cloud ontology: http://www.disit.org/5604
– Cloud simulator:
– Smart Cloud: http://www.disit.org/6544
• Tools: http://www.disit.org/5489
– Cloud Monitoring
– Smart Cloud Engine and reasoner,
– Service Level Analyzer and control
– Configuration analysis and checker
– Cloud Simulation
Text and Web Mining
• Projects: http://www.disit.org/5501
– OSIM: http://www.disit.org/5482
– SACVAR: http://www.disit.org/5604
– Blog/Twitter Vigilance
• Tools: http://www.disit.org/5489
– Text and web mining, Natural Language Processing
– Service localization
– Web Crawling
– Competence analysis
– Blog Vigiliance, sentiment analysis
Social Media and e-Learning
• Projects: http://www.disit.org/5501
– ECLAP, http://www.eclap.eu
– ApreToscana: http://www.apretoscana.org
– Others: AXMEDIS, VARIAZIONI, SMNET, etc.
– Samsung Smart TV: http://www.disit.org/6534
• Tools: http://www.disit.org/5489
– XLMS, Cross Media Learning System
– IPR and content protection and distribution
– Mobile and SmartTv Applications
– Suggestions and recommendations
– Matchmaking solutions
– Media Tools for cross media content
Mobile Computing
• Projects:
– ECLAP: http://www.eclap.eu
– Mobile Medicine: http://mobmed.axmedis.org
– Mobile Emergency: http://www.disit.org/5500
– Smart City, FODD 2015: http://www.disit.org/6593
– Resolute: Mobiles as sensors
• Tools and support:
– Content distribution: e-learning
– Integrated Indoor/outdoor navigation
– User networking and collaboration
– Service localization
– Smart city and services
– OS: iOS, Android, Windows Phone, etc.
– Tech: IOT, iBeacoms, NFC, QR, ….
Graph Databases Lifecycle Methodology and Tool to Support Index/Store Versio...Paolo Nesi
Abstract— Graph databases are taking place in many different applications: smart city, smart cloud, smart education, etc. In most cases, the applications imply the creation of ontologies and the integration of a large set of knowledge to build a knowledge base as an RDF KB store, with ontologies, static data, historical data and real time data. Most of the RDF stores are endowed of inferential engines that materialize some knowledge as triples during indexing or querying. In these cases, deleting concepts may imply the removal and change of many triples, especially if the triples are those modeling the ontological part of the knowledge base, or are referred by many other concepts. For these solutions, the graph database versioning feature is not provided at level of the RDF stores tool, and it is quite complex and time consuming to be addressed as black box approach. In most cases the indexing is a time consuming process, and the rebuilding of the KB may imply manually edited long scripts that are error prone. Therefore, in order to solve these kinds of problems, this paper proposes a lifecycle methodology and a tool supporting versioning of indexes for RDF KB store. The solution proposed has been developed on the basis of a number of knowledge oriented projects as Sii-Mobility (smart city), RESOLUTE (smart city risk assessment), ICARO (smart cloud). Results are reported in terms of time saving and reliability.
Keywords — RDF Knowledge base versioning, graph stores versioning, RDF store management, knowledge base life cycle.
Smart Cloud Engine and Solution based on Knowledge BasePaolo Nesi
Complexity of cloud infrastructures needs models and tools for process management, configuration, scaling, elastic computing and healthiness control. This paper presents a Smart Cloud solution based on a Knowledge Base, KB, with the aim of modeling cloud resources, Service Level Agreements and their evolution, and enabling the reasoning on structures by implementing strategies of efficient smart cloud management and intelligence. The solution proposed provides formal verification tools and intelligence for cloud control. It can be easily integrated with any cloud configuration manager, cloud orchestrator, and monitoring tool, since the connections with these tools are performed by using REST calls and XML files. It has been validated in the large ICARO Cloud project with a national cloud service provider.
Open Data Day 2016, Km4City, L’universita’ come aggregatore di Open Data del ...Paolo Nesi
Open Data Day, UNIMORE, Modena, 5 Marzo 2016.
Aggregazione dati, experienza di Firenze,
Smart City, Km4City,
Smart Decision Support,
Data Ingestion manager,
Data aggregation,
User profiling on demand.
Mobilità: inter-modalità, bigliettazione integrata, sostenibile, scambiatori, sfruttamento stazioni, etc.,
Servizi: gov ..SUAP, edu, turismo, beni culturali, salute, etc.,
Energia: risparmio energetico, riduzione amissioni, inquinamento, etc.,
Ambiente: qualità dell’aria, fiumi, meteo, rifiuti, etc.,
… commercio, industria, etc.
... Infrastrutture critiche. resilienza
Collezionamento dati statici, quasi statici e real time, stream
Dati open: geo localizzati, servizi, statistiche, censimenti, etc.
Dati privati degli operatori: con licenze limitate per non permettere di fare profitto ad altri operatori sulla base dei loro dati
Dati personali delle persone: profili, comportamenti tramite APP, IOT, sensori, web, etc.
Integrazione dati per renderli semanticamente interoperabili, ed operare deduzioni (time, space… )
I tradizionali collettori di open data danno visioni statistiche ma non sono adatti a produrre servizi integrati
Integrazione con modelli semantici unificanti come Km4City
Control Room delle Città Metropolitane devono:
arrivare a supervisionare domini multipli e le interdipendenze fra mobilità, energia, comunicazione, servizi, flussi traffico, flussi pedonali, turismo, etc.
Migliorare la loro Resilienza, capacità di reazione ed assorbimento
ridurre i costi sociali della mobilità per le persone
consentendo minori disagi, maggiore efficienza,
maggiore sensibilità verso le necessità del cittadino,
minori emissioni, migliori condizioni ambientali;
percorsi info-formativi in modo che il cittadino cambi le abitudini non virtuose;
ridurre i costi di trasporto ed i tempi di percorrenza per gli utenti, per i gestori e le amministrazioni, tramite soluzioni di ottimizzazione.
Talk at 3th Keystone Training School - Keyword Search in Big Linked Data - Institute for Software Technology and Interactive Systems, TU Wien, Austria, 2017
NLP on Hadoop: A Distributed Framework for NLP-Based Keyword and Keyphrase Ex...Paolo Nesi
Abstract—The recent growth of the World Wide Web at increasing rate and speed and the number of online available resources populating Internet represent a massive source of knowledge for various research and business interests. Such knowledge is, for the most part, embedded in the textual content of web pages and documents, which is largely represented as unstructured natural language formats. In order to automatically ingest and process such huge amounts of data, single-machine, non-distributed architectures are proving to be inefficient for tasks like Big Data mining and intensive text processing and analysis. Current Natural Language Processing (NLP) systems are growing in complexity, and computational power needs have been significantly increased, requiring solutions such as distributed frameworks and parallel computing programming paradigms. This paper presents a distributed framework for executing NLP related tasks in a parallel environment. This has been achieved by integrating the APIs of the widespread GATE open source NLP platform in a multi-node cluster, built upon the open source Apache Hadoop file system. The proposed framework has been evaluated against a real corpus of web pages and documents.
Online Index Extraction from Linked Open Data SourcesFabio Benedetti
This presentation has been held by me at the Workshop titled Linked Data for Information Extraction 2014 (LD4IE) held at the International Semantic Web Conference 2014. The related paper is titled "Online Index Extraction from Linked Open Data Sources" and here is the link: http://ceur-ws.org/Vol-1267/LD4IE2014_Benedetti.pdf
DSpace-CRIS_An open source solution for Research_EDU15Michele Mennielli
The research area is a complex world to manage. It involves collecting data, supporting researchers and administrators, monitoring results, allocating resources efficiently, enhancing visibility, and strengthening national and international collaborations. RIMs manage these activities, but they might be too expensive. This is why Cineca developed DSpace-CRIS, and released it in open source.
Ontology Building vs Data Harvesting and Cleaning for Smart-city ServicesPaolo Nesi
Presently, a very large number of public and private data sets are available around the local governments. In most cases, they are not semantically interoperable and a huge human effort is needed to create integrated ontologies and knowledge base for smart city. Smart City ontology is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity and reasoning. In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to smart-city ontology and stored into an RDF-Store where they are available for applications via SPARQL queries to provide new services to the users. The paper presents the process adopted to produce the ontology and the knowledge base and the mechanisms adopted for the verification, reconciliation and validation. Some examples about the possible usage of the coherent knowledge base produced are also offered and are accessible from the RDF-Store and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection. Keywords Smart city, knowledge base construction, reconciliation, validation and verification of knowledge base, smart city ontology, linked open graph.
Km4City: Smart City Ontology Building for Effective Erogation of ServicesPaolo Nesi
Provides a unique point of service with integrated and aggregated data and tools for
-- Qualified users: public administrations à developers
-- Operators: mobility, energy, SME, shops, ….. à developers
-- Final users à citizens, students, pendular, tourists
Problems:
--Aggregated Data are not available:
not semantically interoperable, heterogeneous for: format, vocabulary, structure, velocity, volume, ownership/control, access / license, …
---As OD, LD, LOD, private data, ..
---Lack of Services and tools to make the adoption simple
Final Users tools:
--Km4City mobile app with personal assistant is coming…
--Km4City mobile applications: Google Play, Apple Store, …
--Km4City web application: http://www.km4city.org
--Open Source Mobile Application, FODD: an example in open source http://www.disit.org/6595
Public administrator tools:
--Smart decision support system, http://smartds.disit.org
--Developers http://www.disit.org/km4city tools:
--Service Map Server, plus API, http://servicemap.disit.org
--LOG LOD browser: an ultimate visual tool to browse the RDF Store.
--Ontology Documentation: an ultimate tool to understand,
if needed !!
The dirty work of Km4City service
--Data Ingestion Manager, DIM
--RDF Indexer Manager, RIM
--RDF Store Methodology
--RDF store enricher with dbPedia
--Distributed SCE Scheduler, DISCES
--SCE: Smart City Engine
--Doc and info on http://www.disit.org/km4city
This paper surveys the landscape of linked open data projects in cultural heritage, exam- ining the work of groups from around the world. Traditionally, linked open data has been ranked using the five star method proposed by Tim Berners-Lee. We found this ranking to be lacking when evaluating how cultural heritage groups not merely develop linked open datasets, but find ways to used linked data to augment user experience. Building on the five-star method, we developed a six-stage life cycle describing both dataset development and dataset usage. We use this framework to describe and evaluate fifteen linked open data projects in the realm of cultural heritage.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Linked Open Graph: browsing multiple SPARQL entry points to build your own LOD views
1. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
http://log.disit.org
Linked Open Graph: browsing
multiple SPARQL entry points
to build your own LOD views
Pierfrancesco Bellini, Paolo Nesi,
Alessandro Venturi
Dipartimento di Ingegneria dell’Informazione, DINFO
Università degli Studi di Firenze
Via S. Marta 3, 50139, Firenze, Italy
Tel: +39-055-4796567, fax: +39-055-4796363
DISIT Lab
http://www.disit.dinfo.unifi.it alias http://www.disit.org
paolo.nesi@unifi.it
Proc. of the 20th International Conference on Distributed Multimedia Systems,
Pittsburgh, USA, August 2014
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 1
2. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Context and motivations
• Open Data vs Linked Data / Linked Open Data
– OD hundreds of formats
– Linked Data URI as a large network of definitions: triples,
not quereable
• Linked Open Data towards RDF Stores + SPARQL
entry point
– RDF Stores as Knowledge base storing, quereable
– Huge number of OD, limited of LD, a few RDF‐SPARQL
entry point services
– SPARQL entry points services present many dialects and
maturity (versions)
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 2
3. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Grow‐up Knowledge base
• Developing knowledge base, distributed
knowledge base
– Reusing: Definitions, Ontologies, SKOS, vocabularies,..
– Reusing / linking: LD triples, RDF Stores + SPARQL
– A unique storage by copying linking:
– Distributing RDF segments SPARQL queries
• Exploiting the KB
– Integrating multiple RDF Stores & LD
– Understanding and browsing: RDF Stores, LD
– Enriching KB with other triples, LD / URI
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 3
4. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 4
LOG.disit.org
GRUFF
LodLive
5. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Major Features categories
• Access and Query
– Access to multiple distributed LD, browsing, searching,
etc.
• Relationships vs Entities
– Establishing links, showing, discovering, etc.
• General Manipulation of the elements
– Manipulating the graph elements and the graph
• URI Details
– Showing and exploiting attributes and values
• Non Functional
– Scalability, removing duplicates, working via WEB
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 5
6. LOG LodLive Gruff
DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 6
Access and Query
Access and rendering of LD Y Y N
Access and rendering URI from SPARQL entry point Y Y Y
Managing Entry Points with different URL in URI. Y N Y
Multiple SPARQL entry points Y(10) N N
Making keyword based query Y Y Y
Inspecting entry point for searching classes Y Y Y
Relationships vs entities
Showing relationships, turning on/off, singularly or globally Y(3) Y(2) Y(2)
Representing relationships (managing complexity) Y Y(4) Y(4)
Discovering inbound/outbound relationships, URI and queries Y Y Y(7)
Discovering /searching single element from 1:N relation , or samples Y N N
Discover paths between URI N N Y
Creating triples/relationships N N Y
Expand all relationships Y Y N
Close all relationships Y N N
Counting number of elements Y Y Y
“sameAs” management Y Y Y
Blank nodes rendering Y Y Y
General Manipulation
Undo actions performed, “back” Y N Y
Save and Load LOD graphs Y N (Y)
Share and collaborative LOD graphs Y N N
Export of RDF graph triples N N N
Re‐layouting the graph Y(6) N Y
Focusing on an URI Y Y N
Zooming the graph Y N Y(8)
Centering the graph Y N N
Panning the graph with mouse/finger Y Y Y
URI Details
URI attributes (showing info or an URI) Y Y Y(1)
Map allocation of URI Y(9) Y(9) N
URL to resources Y Y N
Open play resources Y Y Y
Representing entities Y Y(5) Y(5)
Non Functional
Web based tool Y Y N
Embed in web pages of third party service: ECLAP Y N N
7. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Access and rendering
• Several kinds of
relationships,
same direction,
etc.: type,
sameAs, blank
nodes, subject,
• Access and
rendering of LD
• Access and
rendering URI
from a SPARQL
entry point
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 7
8. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
http://log.disit.org/service/?graph=cfd084d874318c96205f2f8770ef3b1b
From SPARQL RDF
store to LD access
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 8
9. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Access and Rendering
• Managing Entry
Points with different
URLs in URI
– Multiple ontologies,
entities, sources…
• Inspecting entry
point for searching
classes
• Making keyword
based query
• Multiple SPARQL
entry points
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 9
10. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Relationships vs entities
• Showing relationships, turning
on/off, singularly or globally
– Expand all relationships
– Close all relationships
– “sameAs” management
– Blank nodes rendering
• Counting number of elements
• Discovering inbound/outbound
relationships, URI and queries
• Discovering /searching single
element from relation
• Representing relationships
(managing complexity)
From local stores
• Discover paths between URI
• Creating triples/relationships DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 10
11. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Discovering /searching single element
from relation (RDF store ‐vs‐ LD URI)
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 11
12. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
General Manipulation
• Undo actions performed, “back”
• Save and Load LOD graphs
• Share and collaborative LOD
graphs
Classical features
• Re‐layouting the graph
• Focusing on an URI
• Zooming the graph
• Centering the graph
• Panning the graph with
mouse/finger
Not yet
• Export of RDF graph triples
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 12
13. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
URI Details
• URI attributes (showing
info or an URI)
• Map allocation of URI
• URL to resources
• Open play resources
– Images in local
– Video in remote
– etc.
• Learning how to
compose queries
• Representing entities
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 13
14. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
LOG.disit.org computing
• A) LOG case with two roots: N0 and N8, share node
N5 that holds a double multiplicity (belonging to two
graphs).
– user closes R0 (double click on it): 2 relationships
related arcs dotted are deleted.
– According to that action, a graph analysis is needed.
• B) performing a labeling process from both roots N0
and N8.
– identifying all nodes that are connected from some
root (all except N2, N3) in the graph.
• elements which are not connected have to be removed
(see B): N2, N3, R3 and R2.
• shared nodes, such as node N5 lose their multiplicity.
• C) final results after the application of the above
described “closure” algorithm
– some elements passed from one root to the other.
• complementary operation is needed when an
inbound link of a node is opened
– Example: N3 request the opening of R3, then a
situation similar to B can be reached.
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 14
15. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
A LOG RDF graph analysing connection and structures of
the same user on ECLAP and OSIM RDF stores
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 15
16. DISIT Lab, Distributed Data Intelligence and Technologies
Applications http://www.disit.dinfo.unifi.it
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
• With the aim of exploiting available knowledge sources
– Integrating multiple sources for KB building
– Via: SPARQL entry points, ontologies/LD, LD, vocabularies/LD, etc.
• Understanding, browsing, simulating: RDF Stores, LD
– Discovering connections among RDF Stores and LD
– Comparing Ontologies and representation
• Building and Exploiting merged KB!!
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 16
• Applications:
– ECLAP: CH representation, multiple ontologies,
links with dbPedia, Geonames
– Europeana: Ch representation, multiple
ontologies, links with ECLAP
– Sii‐Mobility: as a support for defining rules
about smart city conditions and for developers
to identify viable query for advanced smart
applications
– OSIM: for Cloud model browsing and
understanding.
– Add yours!!!
17. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Conclusions
• LOG.DISIT, a new Model and Tool
– as a support for KB development in the advanced
semantic web era.
– advanced and more complete features with respect to
the state of the art tools, solving and enabling
• collaborative work, and sharing
• progressive browsing of the graphs
• graph composition: multiple SPARQL entry, plus LD, ..
• support to pose specific queries
• progressive discovering/selection of instances
• currently used in a number of projects and activities in the
area of semantic web. Add yours!!!
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 17
18. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
References
• T. Berners‐Lee, “Linked Data”, http://www.w3.org/DesignIssues/LinkedData.html, 2006.
• C. Bizer, T. Heath and T. Berners‐Lee (2009) Linked Data ‐ the story so far. Int. Journal on Semantic Web and Information Systems, 5, (3), 1‐22.
• G. Klyne, J. Carroll, “Resource Description Framework (RDF): Concepts and Abstract Syntax ‐ W3C Recommendation”, 2004
• FOAF, http://www.foaf‐project.org/
• G. Tummarello, R. Delbru, and E. Oren. 2007. Sindice.com: weaving the open linked data. In Proc. of ISWC'07/ASWC'07, Springer, Berlin,
Heidelberg, pp.552‐565.
• O. Hartig, C. Bizer, J.‐C. Freytag. 2009. Executing SPARQL Queries over the Web of Linked Data. In Proc. of ISWC '09, Springer, pp.293‐309.
• S. Ramakrishnan and A. Vijayan. 2014. A study on development of cognitive support features in recent ontology visualization tools. Artif. Intell.
Rev. 41, 4 (April 2014), pp.595‐623.
• Protégé http://protege.stanford.edu/
• iSPARQL, http://oat.openlinksw.com/isparql/index.html,
• O. Ambrus, K. Moller, S. Handschuh, “Konduit VQB: a Visual Query Builder for SPARQL on the Social Semantic Desktop”, proc of VISSW2010,
IUI2010, 2010, Hong Kong, China.
• A. Russell, P.R. Smart, D. Braines, Dave, N.R. Shadbolt, “NITELIGHT: A Graphical Tool for Semantic Query Construction”, In, SWUI 2008, Florence,
Italy,
• Gfacet, http://www.visualdataweb.org/gfacet.php
• D. V. Camarda, S. Mazzini, A. Antonuccio. 2012. LodLive, exploring the web of data. In Proc. of the I‐SEMANTICS '12, ACM, pp.197‐200.
http://lodlive.it
• P. Bellini, P. Nesi, "Modeling Performing Arts Metadata and Relationships in Content Service for Institutions", Multimedia Systems Journal,
Springer, 2014. http://www.eclap.eu
• D3, Data‐Driven Documents, http://d3js.org/
• P. Bellini, P. Nesi, N. Rauch, “Smart City data via LOD/LOG Service”, Workshop Linked Open Data: where are we?, LOD2014, org. by W3C.
• Prud'hommeaux, E., Seaborne, A., SPARQL Query Language for RDF, http://www.w3.org/TR/2004/WD‐rdf‐sparql‐query‐20041012/
• OTN, Ontology of Transportation Networks, Deliverable A1‐D4, Project REWERSE, 2005 http://rewerse.net/deliverables/m18/a1‐d4.pdf
• http://dublincore.org, http://dublincore.org/documents/dcmi‐terms/
• VCARD, http://www.w3.org/TR/vcard‐rdf/
• wgs84, http://www.w3.org/2003/01/geo/wgs84_pos
• dbPedia, http://dbpedia.org/resource/ DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 18
19. DISIT Lab, Distributed Data Intelligence and Technologies
Distributed Systems and Internet Technologies
Department of Information Engineering (DINFO)
http://www.disit.dinfo.unifi.it
Thank you!
http://www.disit.dinfo.unifi.it
Paolo Nesi
Dipartimento di Ingegneria dell’Informazione, DINFO
Università degli Studi di Firenze
Via S. Marta 3, 50139, Firenze, Italy
Tel: +39-055-4796567, fax: +39-055-4796363
DISIT Lab
http://www.disit.dinfo.unifi.it alias http://www.disit.org
paolo.nesi@unifi.it
DISIT Lab (DINFO UNIFI), DMS 2014, USA, August 2014 19