This document describes a presentation on data collection, integration, and linked data management. It discusses web mining techniques for data extraction, strategies for data integration including conceptual models, and challenges of linked data management in enterprises. It also introduces the FactForge knowledge base, which provides integrated access to billions of facts across multiple datasets and enables complex queries over the semantic data.
On demand access to Big Data through Semantic TechnologiesPeter Haase
The document discusses enabling on-demand access to big data through semantic technologies. It describes how semantic technologies like Linked Data and ontologies can be used to virtually integrate and provide access to large, heterogeneous datasets across different data silos. The key points are that semantic technologies allow for big data to be accessed and analyzed on-demand in a self-service manner through a "Linked Data as a Service" approach, providing scalable end user access to big data.
Linked Data for the Masses: The approach and the SoftwareIMC Technologies
Title: Linked Data for the Masses: The approach and the Software
@ EELLAK (GFOSS) Conference 2010
Athens, Greece
15/05/2010
Creator: George Anadiotis (R&D Director)
Triplestores and inference, applications in Finance, text-mining. Projects and solutions for financial media and publishers.
Keystone Industrial Panel, ISWC 2014, Riva del Garda, 18 Oct 2014.
Thanks to Atanas Kiryakov for this presentation, I just cut it to size.
This document provides an overview of relevant approaches for accessing open data programmatically and data-as-a-service (DaaS) solutions. It discusses common data access methods like web APIs, OData, and SPARQL and describes several DaaS platforms that simplify publishing and consuming open data. It also outlines requirements for a proposed open DaaS platform called DaPaaS that aims to address challenges in open data management and application development.
Overview of Open Data, Linked Data and Web ScienceHaklae Kim
This document provides an overview of open data, linked data, and web science through conceptual discussions, case studies, and proposed next steps. It begins with definitions of key concepts like open data and the semantic web. Case studies demonstrate current applications of open data through government initiatives and technologies like Google's Knowledge Graph and Apple's Siri. The document concludes by acknowledging challenges with open data strategies and advocating for interdisciplinary collaboration to realize the potential of linked open government data.
The document summarizes the Helsinki Region Infoshare project, which aims to develop an open data network and catalogue for the Helsinki metropolitan region. The project will work with data owners to open and publish public sector data following open data principles. It will also support utilization of the data through pilots and an online portal. The project is being carried out in phases from preparation to implementation to establishing a long-term operational model for open data in the region.
This document summarizes a presentation about semantic technologies for big data. It discusses how semantic technologies can help address challenges related to the volume, velocity, and variety of big data. Specific examples are provided of large semantic datasets containing billions of triples and semantic applications that have integrated and analyzed disparate data sources. Semantic technologies are presented as a good fit for addressing big data's variety, and research is making progress in applying them to velocity and volume as well.
Within the course, we will present Linked Data as a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the past years, leading to the creation of a global data space that contains many billions of assertions – the Web of Linked Data.
On demand access to Big Data through Semantic TechnologiesPeter Haase
The document discusses enabling on-demand access to big data through semantic technologies. It describes how semantic technologies like Linked Data and ontologies can be used to virtually integrate and provide access to large, heterogeneous datasets across different data silos. The key points are that semantic technologies allow for big data to be accessed and analyzed on-demand in a self-service manner through a "Linked Data as a Service" approach, providing scalable end user access to big data.
Linked Data for the Masses: The approach and the SoftwareIMC Technologies
Title: Linked Data for the Masses: The approach and the Software
@ EELLAK (GFOSS) Conference 2010
Athens, Greece
15/05/2010
Creator: George Anadiotis (R&D Director)
Triplestores and inference, applications in Finance, text-mining. Projects and solutions for financial media and publishers.
Keystone Industrial Panel, ISWC 2014, Riva del Garda, 18 Oct 2014.
Thanks to Atanas Kiryakov for this presentation, I just cut it to size.
This document provides an overview of relevant approaches for accessing open data programmatically and data-as-a-service (DaaS) solutions. It discusses common data access methods like web APIs, OData, and SPARQL and describes several DaaS platforms that simplify publishing and consuming open data. It also outlines requirements for a proposed open DaaS platform called DaPaaS that aims to address challenges in open data management and application development.
Overview of Open Data, Linked Data and Web ScienceHaklae Kim
This document provides an overview of open data, linked data, and web science through conceptual discussions, case studies, and proposed next steps. It begins with definitions of key concepts like open data and the semantic web. Case studies demonstrate current applications of open data through government initiatives and technologies like Google's Knowledge Graph and Apple's Siri. The document concludes by acknowledging challenges with open data strategies and advocating for interdisciplinary collaboration to realize the potential of linked open government data.
The document summarizes the Helsinki Region Infoshare project, which aims to develop an open data network and catalogue for the Helsinki metropolitan region. The project will work with data owners to open and publish public sector data following open data principles. It will also support utilization of the data through pilots and an online portal. The project is being carried out in phases from preparation to implementation to establishing a long-term operational model for open data in the region.
This document summarizes a presentation about semantic technologies for big data. It discusses how semantic technologies can help address challenges related to the volume, velocity, and variety of big data. Specific examples are provided of large semantic datasets containing billions of triples and semantic applications that have integrated and analyzed disparate data sources. Semantic technologies are presented as a good fit for addressing big data's variety, and research is making progress in applying them to velocity and volume as well.
Within the course, we will present Linked Data as a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the past years, leading to the creation of a global data space that contains many billions of assertions – the Web of Linked Data.
This presentation addresses the main issues of Linked Data and scalability. In particular, it provides gives details on approaches and technologies for clustering, distributing, sharing, and caching data. Furthermore, it addresses the means for publishing data trough could deployment and the relationship between Big Data and Linked Data, exploring how some of the solutions can be transferred in the context of Linked Data.
This document discusses interaction with linked data, focusing on visualization techniques. It begins with an overview of the linked data visualization process, including extracting data analytically, applying visualization transformations, and generating views. It then covers challenges like scalability, handling heterogeneous data, and enabling user interaction. Various visualization techniques are classified and examples are provided, including bar charts, graphs, timelines, and maps. Finally, linked data visualization tools and examples using tools like Sigma, Sindice, and Information Workbench are described.
Linked Open Data Principles, Technologies and ExamplesOpen Data Support
Theoretical and practical introducton to linked data, focusing both on the value proposition, the theory/foundations, and on practical examples. The material is tailored to the context of the EU institutions.
Slides for talk given at IWMW 1998 held at the University of Newcastle on 15-17 September 1998.
See http://www.ukoln.ac.uk/web-focus/events/workshops/webmaster-sep1998/materials/
Big Linked Data - Creating Training CurriculaEUCLID project
This presentation includes an overview of the basic rules to follow when developing training and education curricula for Linked Data and Big Linked Data
Build Narratives, Connect Artifacts: Linked Open Data for Cultural HeritageOntotext
Many issues are faced by scholars, book researchers, museum directors who try to find the underlying connection between resources. Scholars in particular continuously emphasizes the role of digital humanities and the value of linked data in cultural heritage information systems.
This document discusses Linked Open Data and how to publish open government data. It explains that publishing data in open, machine-readable formats and linking it to other external data sources increases its value. It provides examples of published open government data and outlines best practices for making data open through licensing, standard formats like CSV and XML, using URIs as identifiers, and linking to related external data. The key benefits outlined are empowering others to build upon the data and improving transparency, competition and innovation.
ESWC SS 2012 - Wednesday Tutorial Barry Norton: Building (Production) Semanti...eswcsummerschool
Ontotext is a leading semantic technology company that has developed OWLIM, a family of semantic repositories for storing and querying RDF and OWL data. OWLIM can handle large datasets, perform reasoning, and supports features like full text search, notifications, and geo-spatial querying. It has been used successfully in large-scale production systems like the BBC's World Cup website to power semantic search and dynamic content delivery using semantic web technologies.
Microtask Crowdsourcing Applications for Linked DataEUCLID project
This document discusses using microtask crowdsourcing to enhance linked data applications. It describes how crowdsourcing can be used in various components of the linked data integration process, including data cleansing, vocabulary mapping, and entity interlinking. Specific crowdsourcing applications and systems are discussed that address tasks like assessing the quality of DBpedia triples, entity linking with ZenCrowd, and understanding natural language queries with CrowdQ. The results show that crowdsourcing can often improve the results of automated techniques for various linked data tasks and help integrate and enhance large linked data sources.
The Power of Semantic Technologies to Explore Linked Open DataOntotext
Atanas Kiryakov's, Ontotext’s CEO, presentation at the first edition of Graphorum (http://graphorum2017.dataversity.net/) – a new forum that taps into the growing interest in Graph Databases and Technologies. Graphorum is co-located with the Smart Data Conference, organized by the digital publishing platform Dataversity.
The presentation demonstrates the capabilities of Ontotext’s own approach to contributing to the discipline of more intelligent information gathering and analysis by:
- graphically explorinh the connectivity patterns in big datasets;
- building new links between identical entities residing in different data silos;
- getting insights of what type of queries can be run against various linked data sets;
- reliably filtering information based on relationships, e.g., between people and organizations, in the news;
- demonstrating the conversion of tabular data into RDF.
Learn more at http://ontotext.com/.
This presentation covers the whole spectrum of Linked Data production and exposure. After a grounding in the Linked Data principles and best practices, with special emphasis on the VoID vocabulary, we cover R2RML, operating on relational databases, Open Refine, operating on spreadsheets, and GATECloud, operating on natural language. Finally we describe the means to increase interlinkage between datasets, especially the use of tools like Silk.
Linked Open Data combines open data and linked data by making open data available on the web in a way that is machine-readable and semantically interlinked. It uses URIs and RDF to identify things and their properties and relationships, and links data from different sources to enable discovery of related data. Publishing and consuming Linked Open Data allows data sharing and integration to create new knowledge and applications. Key steps involve identifying, cleaning, and publishing data as RDF while linking it to other datasets, then consuming and combining it with other sources. Major Linked Open Data sources include data from governments, Wikipedia, and other organizations.
First Steps in Semantic Data Modelling and Search & Analytics in the CloudOntotext
This webinar will break the roadblocks that prevent many from reaping the benefits of heavyweight Semantic Technology in small scale projects. We will show you how to build Semantic Search & Analytics proof of concepts by using managed services in the Cloud.
Open Data, by definition, provides the chance to re-shape and publish heterogeneous pieces and fragments of information which are open, namely anyone is free to use, reuse, and redistribute it. In order for users to fully benefit this idea, Open Data Systems of tomorrow must provide high quality data, relying on real time and ubiquitous services, along with a deep integration with mobile and smart devices and infrastructures.
In this session, we present a syntheses of Whitehall proposal addressed a this vision: is addressed at building Open Data in a fully-fledged Big Data infrastructure, realized using graph based and NoSQL technologies. This idea is shaped in a cultural heritage scenario, where data in envisaged at valorizing one of the main assets of Italy: cultural heritage.
Denodo: Enabling a Data Mesh Architecture and Data Sharing Culture at Landsba...Denodo
This document discusses Landsbankinn's implementation of a logical data warehouse and data mesh architecture using Denodo over five years. It summarizes that:
1) Landsbankinn initially implemented a logical data warehouse with Denodo to create a single point of access, business logic, and control for reporting across heterogeneous data sources.
2) Over the next two years, they expanded this to include additional data consumers and sources without requiring ETL.
3) They then realized a data mesh approach where domains owned and published their data was better, reducing complexity and meetings for mapping changes.
4) The current approach has domains developing and sharing views via a data mesh while the logical data warehouse combines them, improving governance and flexibility
Cloud-based Linked Data Management for Self-service Application DevelopmentPeter Haase
Peter Haase and Michael Schmidt of fluid Operations AG presented on developing applications using linked open data. They discussed the increasing amount of linked open data available and challenges in building applications that integrate data from different sources and domains. Their Information Workbench platform aims to address these challenges by allowing users to discover, integrate, and customize applications using linked data in a no-code environment. Key components of the platform include virtualized integration of data sources and the vision of accessing linked data as a cloud-based data service.
Unlock Your Data for ML & AI using Data VirtualizationDenodo
How Denodo Complement’s Logical Data Lake in Cloud
● Denodo does not substitute data warehouses, data lakes,
ETLs...
● Denodo enables the use of all together plus other data
sources
○ In a logical data warehouse
○ In a logical data lake
○ They are very similar, the only difference is in the main
objective
● There are also use cases where Denodo can be used as data
source in a ETL flow
The document describes the Semantic Scout, a framework developed by CNR Semantic Technology Lab for searching, presenting, and analyzing entities from CNR data sources using semantic web, linked open data, natural language processing, and information retrieval techniques. It summarizes the goals and architecture of the Semantic Scout, including how it converts CNR data into ontologies and triples, publishes and links the data, and allows users to search and explore the data through a SPARQL endpoint and other interfaces. The document also provides an example of how the Semantic Scout can be used to identify experts on a topic by searching the integrated CNR data cloud.
SemTechBiz 2012 Panel on Linking Enterprise Data3 Round Stones
The document discusses a panel on linked enterprise data patterns featuring Arnaud Le Hors from IBM, Ashok Malhotra from Oracle, and David Wood from 3 Round Stones. It provides details on recent linked data activities from the W3C including a new working group. It also summarizes IBM, Oracle, and 3 Round Stones' involvement with linked data and semantic technologies including products, projects, and standards.
This presentation addresses the main issues of Linked Data and scalability. In particular, it provides gives details on approaches and technologies for clustering, distributing, sharing, and caching data. Furthermore, it addresses the means for publishing data trough could deployment and the relationship between Big Data and Linked Data, exploring how some of the solutions can be transferred in the context of Linked Data.
This document discusses interaction with linked data, focusing on visualization techniques. It begins with an overview of the linked data visualization process, including extracting data analytically, applying visualization transformations, and generating views. It then covers challenges like scalability, handling heterogeneous data, and enabling user interaction. Various visualization techniques are classified and examples are provided, including bar charts, graphs, timelines, and maps. Finally, linked data visualization tools and examples using tools like Sigma, Sindice, and Information Workbench are described.
Linked Open Data Principles, Technologies and ExamplesOpen Data Support
Theoretical and practical introducton to linked data, focusing both on the value proposition, the theory/foundations, and on practical examples. The material is tailored to the context of the EU institutions.
Slides for talk given at IWMW 1998 held at the University of Newcastle on 15-17 September 1998.
See http://www.ukoln.ac.uk/web-focus/events/workshops/webmaster-sep1998/materials/
Big Linked Data - Creating Training CurriculaEUCLID project
This presentation includes an overview of the basic rules to follow when developing training and education curricula for Linked Data and Big Linked Data
Build Narratives, Connect Artifacts: Linked Open Data for Cultural HeritageOntotext
Many issues are faced by scholars, book researchers, museum directors who try to find the underlying connection between resources. Scholars in particular continuously emphasizes the role of digital humanities and the value of linked data in cultural heritage information systems.
This document discusses Linked Open Data and how to publish open government data. It explains that publishing data in open, machine-readable formats and linking it to other external data sources increases its value. It provides examples of published open government data and outlines best practices for making data open through licensing, standard formats like CSV and XML, using URIs as identifiers, and linking to related external data. The key benefits outlined are empowering others to build upon the data and improving transparency, competition and innovation.
ESWC SS 2012 - Wednesday Tutorial Barry Norton: Building (Production) Semanti...eswcsummerschool
Ontotext is a leading semantic technology company that has developed OWLIM, a family of semantic repositories for storing and querying RDF and OWL data. OWLIM can handle large datasets, perform reasoning, and supports features like full text search, notifications, and geo-spatial querying. It has been used successfully in large-scale production systems like the BBC's World Cup website to power semantic search and dynamic content delivery using semantic web technologies.
Microtask Crowdsourcing Applications for Linked DataEUCLID project
This document discusses using microtask crowdsourcing to enhance linked data applications. It describes how crowdsourcing can be used in various components of the linked data integration process, including data cleansing, vocabulary mapping, and entity interlinking. Specific crowdsourcing applications and systems are discussed that address tasks like assessing the quality of DBpedia triples, entity linking with ZenCrowd, and understanding natural language queries with CrowdQ. The results show that crowdsourcing can often improve the results of automated techniques for various linked data tasks and help integrate and enhance large linked data sources.
The Power of Semantic Technologies to Explore Linked Open DataOntotext
Atanas Kiryakov's, Ontotext’s CEO, presentation at the first edition of Graphorum (http://graphorum2017.dataversity.net/) – a new forum that taps into the growing interest in Graph Databases and Technologies. Graphorum is co-located with the Smart Data Conference, organized by the digital publishing platform Dataversity.
The presentation demonstrates the capabilities of Ontotext’s own approach to contributing to the discipline of more intelligent information gathering and analysis by:
- graphically explorinh the connectivity patterns in big datasets;
- building new links between identical entities residing in different data silos;
- getting insights of what type of queries can be run against various linked data sets;
- reliably filtering information based on relationships, e.g., between people and organizations, in the news;
- demonstrating the conversion of tabular data into RDF.
Learn more at http://ontotext.com/.
This presentation covers the whole spectrum of Linked Data production and exposure. After a grounding in the Linked Data principles and best practices, with special emphasis on the VoID vocabulary, we cover R2RML, operating on relational databases, Open Refine, operating on spreadsheets, and GATECloud, operating on natural language. Finally we describe the means to increase interlinkage between datasets, especially the use of tools like Silk.
Linked Open Data combines open data and linked data by making open data available on the web in a way that is machine-readable and semantically interlinked. It uses URIs and RDF to identify things and their properties and relationships, and links data from different sources to enable discovery of related data. Publishing and consuming Linked Open Data allows data sharing and integration to create new knowledge and applications. Key steps involve identifying, cleaning, and publishing data as RDF while linking it to other datasets, then consuming and combining it with other sources. Major Linked Open Data sources include data from governments, Wikipedia, and other organizations.
First Steps in Semantic Data Modelling and Search & Analytics in the CloudOntotext
This webinar will break the roadblocks that prevent many from reaping the benefits of heavyweight Semantic Technology in small scale projects. We will show you how to build Semantic Search & Analytics proof of concepts by using managed services in the Cloud.
Open Data, by definition, provides the chance to re-shape and publish heterogeneous pieces and fragments of information which are open, namely anyone is free to use, reuse, and redistribute it. In order for users to fully benefit this idea, Open Data Systems of tomorrow must provide high quality data, relying on real time and ubiquitous services, along with a deep integration with mobile and smart devices and infrastructures.
In this session, we present a syntheses of Whitehall proposal addressed a this vision: is addressed at building Open Data in a fully-fledged Big Data infrastructure, realized using graph based and NoSQL technologies. This idea is shaped in a cultural heritage scenario, where data in envisaged at valorizing one of the main assets of Italy: cultural heritage.
Denodo: Enabling a Data Mesh Architecture and Data Sharing Culture at Landsba...Denodo
This document discusses Landsbankinn's implementation of a logical data warehouse and data mesh architecture using Denodo over five years. It summarizes that:
1) Landsbankinn initially implemented a logical data warehouse with Denodo to create a single point of access, business logic, and control for reporting across heterogeneous data sources.
2) Over the next two years, they expanded this to include additional data consumers and sources without requiring ETL.
3) They then realized a data mesh approach where domains owned and published their data was better, reducing complexity and meetings for mapping changes.
4) The current approach has domains developing and sharing views via a data mesh while the logical data warehouse combines them, improving governance and flexibility
Cloud-based Linked Data Management for Self-service Application DevelopmentPeter Haase
Peter Haase and Michael Schmidt of fluid Operations AG presented on developing applications using linked open data. They discussed the increasing amount of linked open data available and challenges in building applications that integrate data from different sources and domains. Their Information Workbench platform aims to address these challenges by allowing users to discover, integrate, and customize applications using linked data in a no-code environment. Key components of the platform include virtualized integration of data sources and the vision of accessing linked data as a cloud-based data service.
Unlock Your Data for ML & AI using Data VirtualizationDenodo
How Denodo Complement’s Logical Data Lake in Cloud
● Denodo does not substitute data warehouses, data lakes,
ETLs...
● Denodo enables the use of all together plus other data
sources
○ In a logical data warehouse
○ In a logical data lake
○ They are very similar, the only difference is in the main
objective
● There are also use cases where Denodo can be used as data
source in a ETL flow
The document describes the Semantic Scout, a framework developed by CNR Semantic Technology Lab for searching, presenting, and analyzing entities from CNR data sources using semantic web, linked open data, natural language processing, and information retrieval techniques. It summarizes the goals and architecture of the Semantic Scout, including how it converts CNR data into ontologies and triples, publishes and links the data, and allows users to search and explore the data through a SPARQL endpoint and other interfaces. The document also provides an example of how the Semantic Scout can be used to identify experts on a topic by searching the integrated CNR data cloud.
SemTechBiz 2012 Panel on Linking Enterprise Data3 Round Stones
The document discusses a panel on linked enterprise data patterns featuring Arnaud Le Hors from IBM, Ashok Malhotra from Oracle, and David Wood from 3 Round Stones. It provides details on recent linked data activities from the W3C including a new working group. It also summarizes IBM, Oracle, and 3 Round Stones' involvement with linked data and semantic technologies including products, projects, and standards.
The document provides an overview of the Dublinked Technology Workshop held on December 15th, 2011. It includes presentations on transportation data, spatial web services, linked data, and semantic data description. Breakout sessions covered topics like data publishing, discovery, web services, and advanced functions. The workshop aimed to address challenges around sharing digital data between organizations and discussed technical requirements and tools to support open government data platforms.
Linked Data for the Enterprise: Opportunities and ChallengesMarin Dimitrov
1) Semantic technologies and linked data can help address challenges of integrating disparate data sources and providing unified access to enterprise information.
2) Case studies demonstrate successes in areas like semantic search, knowledge discovery, and dynamic publishing by linking and enriching content.
3) Adoption challenges include developing domain ontologies, query performance, data quality, and getting enterprise IT teams familiar with semantic technologies.
[Databeers] 06/05/2014 - Boris Villazon: “Data Integration - A Linked Data ap...Data Beers
This document discusses using linked data approaches for data integration. It introduces linked data as a way to publish and connect disparate data sources using common identifiers and semantic web standards like URIs and RDF. This allows data to be queried and exploited as a single global database. Examples are given of applying linked data for integrating enterprise data sources and for publishing geospatial data from Ecuador using semantic representations. The benefits of linked data for data integration are that it enables querying across data silos and consuming data without complex transformations by using the graph-based RDF data model.
Myth Busters III: I’m Building a Data Lake, So I Don’t Need Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/2XXAzU3
So you’re building a data lake to solve your big data challenges. A data lake will allow you to keep all of your raw, detailed data in a single, consolidated repository; therefore, your problem is solved. Or is it? Is it really that easy?
Data lakes have their use and purpose, and we’re not here to argue that. However, data lakes on their own are constrained by factors such as duplication of data and therefore higher costs, governance limitations, and the risk of becoming another data silo.
With the addition of data virtualization, a physical data lake, can turn into a virtual or logical data like through an abstraction layer. Data virtualization can facilitate and expedite accessing and exploring critical data in a cost-effective manner and assist in deriving a greater return on the data lake investment.
You might still not be convinced. Give us an opportunity and join us as we try to bust this myth!
Watch this webinar as we explore the promises of a data lake as well as its downfalls to draw a final conclusion.
Modern Data Management for Federal ModernizationDenodo
Watch full webinar here: https://bit.ly/2QaVfE7
Faster, more agile data management is at the heart of government modernization. However, Traditional data delivery systems are limited in realizing a modernized and future-proof data architecture.
This webinar will address how data virtualization can modernize existing systems and enable new data strategies. Join this session to learn how government agencies can use data virtualization to:
- Enable governed, inter-agency data sharing
- Simplify data acquisition, search and tagging
- Streamline data delivery for transition to cloud, data science initiatives, and more
Myth Busters: I’m Building a Data Lake, So I Don’t Need Data Virtualization (...Denodo
Watch full webinar here: https://bit.ly/3kr0oq4
So you’re building a data lake to solve your big data challenges. A data lake will allow you to keep all of your raw, detailed data in a single, consolidated repository; therefore, your problem is solved. Or is it? Is it really that easy?
Data lakes have their use and purpose, and we’re not here to argue that. However, data lakes on their own are constrained by factors such as duplication of data and therefore higher costs, governance limitations, and the risk of becoming another data silo.
With the addition of data virtualization, a physical data lake, can turn into a virtual or logical data like through an abstraction layer. Data virtualization can facilitate and expedite accessing and exploring critical data in a cost-effective manner and assist in deriving a greater return on the data lake investment.
You might still not be convinced. Give us an opportunity and join us as we try to bust this myth!
Watch this webinar as we explore the promises of a data lake as well as its downfalls to draw a final conclusion.
This document summarizes Marin Dimitrov's presentation on linked data management at the 3rd GATE training course in Montreal in August 2010. The presentation covered linked data principles, key vocabularies and datasets, open government data initiatives, and tools for working with linked data. Some open issues discussed were the diversity of linked data schemas, data quality issues, reliability of endpoints, licensing concerns, and challenges of querying distributed data.
A Survey of Exploratory Search Systems Based on LOD ResourcesKarwan Jacksi
The document summarizes Karwan Jacksi's presentation on exploratory search systems based on Linked Open Data (LOD) resources at the International Conference on Computing and Informatics in Istanbul, 2015. The presentation discusses search strategies, the semantic web, linked data, existing linked data browsers and recommenders. It then summarizes several existing exploratory search systems that utilize LOD resources, including Yovisto, Semantic Wonder Cloud, Lookup Explore Discover, Aemoo, Seevl, Linked Jazz, Discovery Hub, and inWalk. The presentation also covers computing semantic similarity, linked data techniques, and references.
Ben Gardner | Delivering a Linked Data warehouse and integrating across the w...semanticsconference
- The document discusses building a linked data warehouse at a law firm to integrate siloed information from diverse sources and allow for flexible information discovery.
- They extracted data from various sources like Excel reports, XML files, and databases into RDF triples and loaded them into a triple store with an ETL platform.
- This created a logical data warehouse that connects matters, people, clients, and other entities as "things" with relationships, allowing exploration from different domain views.
- An OData integration allows querying the triple store from tools like Excel and Tableau, while a web interface demonstrates lens-based exploration of entities and composing advanced semantic searches.
An introduction deck for the Web of Data to my team, including basic semantic web, Linked Open Data, primer, and then DBpedia, Linked Data Integration Framework (LDIF), Common Crawl Database, Web Data Commons.
Linked Data (1st Linked Data Meetup Malmö)Anja Jentzsch
This document discusses Linked Data and outlines its key principles and benefits. It describes how Linked Data extends the traditional web by creating a single global data space using RDF to publish structured data on the web and by setting links between data items from different sources. The document outlines the growth of Linked Data on the web, with over 31 billion triples from 295 datasets as of 2011. It provides examples of large Linked Data sources like DBpedia and discusses best practices for publishing, consuming, and working with Linked Data.
The document summarizes the Research Data Family services at the University of Oxford. It discusses the history of research data management at Oxford dating back to 2008. It outlines several key services including DataPlan for creating data management plans, DataStage for lightweight data curation, DataBank as the research data repository, DataFinder as the research data catalogue, and training and support services. Future plans include further integrating these services and making them more sustainable and interoperable with other university and publishing systems.
Conference: 23rd ICE/IEEE ITMC Conference
(ICE2017).
Madeira, Portugal – June 27-30, 2017
Title of the paper: Configuring and Visualizing The
Data Resources in a Cloud-based Data Collection
Framework
Authors: Wael M. Mohammed, David Aleixo, Borja
Ramis Ferrer, Carlos Agostinho, Jose L. Martinez
Lastra
if you would like to receive a reprint of the
original paper, please contact us.
The document discusses Microsoft's approach to implementing a data mesh architecture using their Azure Data Fabric. It describes how the Fabric can provide a unified foundation for data governance, security, and compliance while also enabling business units to independently manage their own domain-specific data products and analytics using automated data services. The Fabric aims to overcome issues with centralized data architectures by empowering lines of business and reducing dependencies on central teams. It also discusses how domains, workspaces, and "shortcuts" can help virtualize and share data across business units and data platforms while maintaining appropriate access controls and governance.
Similar to Data Collection and Integration, Linked Data Management (20)
Text Stream Processing Tutorial @WIMS 2012RENDER project
The document discusses text stream processing, including an overview of topic detection methods like clustering and probabilistic topic modeling applied to text streams, as well as entity extraction, relation extraction, and other natural language processing tasks that can be performed on continuous streams of text data in (near) real-time. Pre-processing steps for text streams are also covered, along with demonstrations and publicly available tools for text stream analysis.
The document summarizes a news aggregation system that (1) monitors over 150,000 RSS and other news feeds, (2) cleans and enriches harvested articles by detecting languages, categorizing topics, geo-tagging locations, and identifying entities, and (3) exposes the aggregated and enriched news articles through an HTTP API and command-line client for users to access the data.
Render Review: Wikipedia Case Study, Year 1RENDER project
The document discusses a project to enhance diversity in content creation on Wikipedia. It aims to support editors in maintaining high quality articles on both prominent and obscure topics. It outlines challenges to diversity like biases in authorship demographics and inconsistencies in article content across languages. It also describes work done in the first year, including defining use case scenarios and developing methods to detect bias-inducing behaviors. Metrics are being used to evaluate how tools can help address the identified problems and improve Wikipedia.
The document discusses a project called RENDER that aims to enhance diversity in Wikipedia content creation. It seeks to support editors, address biases, and improve article quality. The project has defined use case scenarios and metrics to evaluate progress. It has also reused research from other projects to inform the development of tools for bias detection, notifications, and lowering barriers to participation.
The document discusses building a model to predict bias on Wikipedia pages. It outlines identifying patterns in editing behavior that typically lead to bias, like ownership behavior and opinion camps. These patterns will be used to train machine learning algorithms to predict bias. The goal is to help the declining number of editors address bias by developing tools that leverage the prediction model.
Diversiweb2011 02 Opening- Devika P. MadalliRENDER project
The document summarizes a workshop on knowledge diversity that was held on March 28, 2011 in Hyderabad. It discusses the LivingKnowledge project which aims to investigate diverse disciplines to develop a formal knowledge model that represents evolution, diversity and bias over time. It lists the consortium members and describes six research areas and challenges around information extraction, understanding bias and diversity, knowledge evolution, and enhanced search and retrieval technologies.
Diversiweb2011 08 Mining Diverse Views from Related Articles - Ravali Pochamp...RENDER project
The document describes a method for mining diverse views from a set of related articles. It extracts important sentences from the articles and clusters them into views based on semantic similarity. Each view represents a sub-topic and provides an organized summary of the issue. The method scores each sentence based on TF-IDF and clusters the top ranked sentences using hierarchical agglomerative clustering. Cluster cohesion is used to determine the most relevant view. The results show views with better cohesion are obtained by clustering between 20-35 top sentences. The method provides an alternative IR model for summarization with multiple organized views.
The document discusses approximating subgraph matching for detecting variations in topics. It presents a method for identifying a common template from a collection of texts on a topic by aligning the texts to the template. Templates are represented as subgraphs with frequent specializations. The method constructs a semantic graph from news data and then performs subgraph matching and generalization/specialization to mine templates representing event schemas. Preliminary results on 5 test domains showed the approach could extract templates in 10-60 seconds.
Diversiweb2011 06 Faceted Approach To Diverse Query Processing - Devika P. Ma...RENDER project
The document describes a faceted approach to processing diverse queries. It discusses how classical libraries used subject indices to organize information, and how faceted classification systems like Colon Classification provide contextual information. It then presents the DEPA framework which uses facets extracted from queries to refine searches. An algorithm is described that analyzes documents to produce "focused terms" as labels for clusters in a context. These terms are then used to build a faceted ontology represented in description logic.
Diversiweb2011 05 Scalable Detection of Sentiment-Based Contradictions - Mika...RENDER project
The document discusses detecting sentiment-based contradictions in text. It defines contradictions as situations where two sentences are unlikely to be true together based on sentiment. It motivates the need to analyze contradictions due to diversity and change of views expressed on social media and other online forums. The paper proposes a three step approach to detect sentiment-based contradictions: topic identification, sentiment extraction and aggregation, and contradiction extraction.
This document discusses expressing and analyzing opinion diversity. It proposes building a domain-specific opinion vocabulary from a movie review corpus to identify positive and negative sentiment words. This vocabulary is then used to analyze sentiment in tweets about movies, determining sentiment distribution and evolution over time. Future work includes expanding the vocabulary to include other parts of speech and testing the correlation between sentiment and movie ratings.
Diversiweb2011 03 Towards a Knowledge Diversity Model - Denny VrandecicRENDER project
The document proposes a knowledge diversity model to capture notions related to diversity-enabled information management. The core concepts are agents holding opinions on topics, documents containing opinion expressions, bias being the set of opinions expressed, and diversity being the co-existence of biases for a topic. Next steps include representing the model in OWL and grounding it in existing ontologies.
This document summarizes the 1st International Workshop on Knowledge Diversity on the Web (DiversiWeb2011) which was held on March 28, 2011 in Hyderabad. The workshop aimed to provide an interdisciplinary forum to discuss challenges related to diversity on the web. Topics included risks and advantages of diversity, modeling knowledge diversity, detecting biases in web content, and enabling communication across diverse groups. The workshop featured presentations and discussions on modeling knowledge diversity, expressing opinion diversity, detecting sentiment-based contradictions, faceted diverse query processing, detecting topic variations, and mining diverse views from related articles.
This document discusses work package 4 (WP4) of a project which involves developing extensions to existing collaboration tools to support diversity. It outlines two tasks: T4.1 which involves developing extensions for tools like WordPress and MediaWiki over three years, and T4.2 which involves producing best practice documents. It then provides more details on the planned extensions for WordPress and MediaWiki, and brainstorms potential scenarios for applying the extensions, such as adding links to related but different opinions in blogs, and checking biases in sources for wiki articles.
This document provides an overview of a project for Telefónica I+D to develop analytical user modeling tools. It will analyze data from multiple sources, including call centers, web portals, forums, surveys, and Twitter, to understand customer opinions. The data sources vary in language formality, ability to segment users, and difficulty of acquisition. Current opinion mining tools for Twitter data show limited capability to accurately identify opinions on Telefónica brands from tweets and classify sentiment. The project aims to improve on these tools to better discover and analyze insights from large, multilingual data streams.
The document discusses the concept of diversity within the context of RENDER, which deals with modeling communication and usage of documents. It identifies several types of diversity that could be relevant to RENDER, including diversity in topics, knowledge, languages, opinions, geographies, and contexts represented in content and usage data. Diversity in content may come from differences in topics covered, social relationships depicted, languages used, opinions expressed, and reporting biases. Diversity in usage can come from variations in user demographics, interests, locations, access methods, social networks, access times, and historical usage patterns. The document aims to structure the notion of diversity within RENDER scenarios involving content producers, content users, and their data.
This document provides an overview of the RENDER project, which aims to develop techniques and software that leverage information diversity on the web. The 3-year project involves 7 partners from 5 countries with a budget of over 4 million euros. It will create open-source tools to support diversity-aware information management on platforms like Wikipedia and blogs. The goals are to address challenges around accessing and making sense of online information, better reflecting diverse viewpoints, and enabling true cross-community collaboration.
Data Collection and Integration, Linked Data Management
1. Data Collection and Integration,
Linked Data Management
Tutorial at the RENDER Kick-off Meeting
Oct 2010
2. Presentation Outline
• Introduction
• Web Mining
• Data Integration Strategy
• Linked Data Management
– Linked Data: Introduction and Challenges
– FactForge: Fast Track to the Center of the Web of Data
Data Collection and Integration, LD Management Oct 2010 #2
3. Content and Data Management Solutions
Data Collection and Integration, LD Management Oct 2010 #3
4. Content and Data Management Solutions
Data Collection and Integration, LD Management Oct 2010 #4
5. Web Mining
• Web Crawling
• Focused Crawling
• Screen-scrapping
• Data feeds, RSS feeds
Data Collection and Integration, LD Management Oct 2010 #5
6. Web Mining Projects
• Namerimi – a national search engine
• Innovantage – job offers in UK
• Ronsmap – car offers in USA
• ANAM – recepie knowledge base
• Semantic KB of the Government Web Archive
(TNA)
Data Collection and Integration, LD Management Oct 2010 #6
7. TNA SemKB Conceptual Model for Data Integration
Ontology Central Government
PROTON
Ontology
Mapping
Mapping
FactForge Semantic Knowledge Semi-
Base Ontology structured
LOD Data
Linked ETL
Factual Mapping
Data TNA Struct.
knowledge Data
Government
Mapping Mapping
Web
Web Archive DataBase
Extracts
Metadata
Government Web
Archive Metadata
Unstructured
Content Government Web
Archive
Legend:
Reference
8. Presentation Outline
• Introduction
• Web Mining
• Data Integration Strategy
• Linked Data Management
– Linked Data: Introduction and Challenges
– FactForge: Fast Track to the Center of the Web of Data
Data Collection and Integration, LD Management Oct 2010 #8
9. Data Integration Strategy
• Determine data sources
• Determine extraction policies
• Conceptual Model for Data Integration
• Transformation routines
• Identity Resolution
Data Collection and Integration, LD Management Oct 2010 #9
10. Conceptual Model for Data Integration In RENDER
• Build a stack of reference ontologies
– a relatively stable controlled vocabulary
• Use those for:
– Integration of RENDER-specific data to LOD
– Refer to it from various components and resources
• The Stack:
– RENDER schema – very tiny set of primitives
– PROTON – few hundred schema primitives
– UMBEL – several thousand classes and subjects
– FactForge – gateway to the central LOD datasets
Data Collection and Integration, LD Management Oct 2010 #10
11. Presentation Outline
• Introduction
• Web Mining
• Data Integration Strategy
• Linked Data Management
– Linked Data: Introduction and Challenges
– FactForge: Fast Track to the Center of the Web of Data
Data Collection and Integration, LD Management Oct 2010 #11
12. Linking Open Data (LOD)
• Linking Open Data W3C SWEO Community project
http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData
• Initiative for publishing “linked data” which already
includes 50+ interlinked datasets and about 15B facts
Data Collection and Integration, LD Management Oct 2010 #12
13. Why Do People Not Use Linked Data?
• Plenty of people in the IT world have heard about
linked data and like the idea
• However, the impact of linked data in enterprises
is still very limited
• Because:
– There are no well established opinions about what linked data can
“buy” for the enterprise or best practices for using it
• What are the concrete benefits?
– It is not clear what it would cost
• What are the problems?
• What are the associated risks?
Data Collection and Integration, LD Management Oct 2010 #13
14. Linked Data in the Enterprise: Why?
• To facilitate data integration
– One can use LOD as an “interlingua” for enterprise data integration
– Additional public information can help alignment and linking
• To add value to proprietary data
– Public data can allow more analytics on top of proprietary data
– For instance, by linking to spatial data from Geonames
– Better description and access to content, e.g. search for images
• Make enterprise data more open
– To make enterprise data easier to use outside the enterprise
– Public identifiers and vocabularies can be used to access them
Data Collection and Integration, LD Management Oct 2010 #14
15. Linked Data in the Enterprise: Challenges
• LOD is hard to comprehend
– Diversity comes at a price
– One needs to make a query against 200 different schemata and
hundreds of thousands of classes and properties
• LOD is unreliable
– Most of the servers behind LOD today are slow
• High down time
– Dealing with data distributed on the web is slow
• A federated SPARQL query that uses 2-3 servers within several joins
can be *very* slow
– No kind of consistency is guaranteed
• Low commitment to the formal semantics and intended usage of the
ontologies and schemata
Data Collection and Integration, LD Management Oct 2010 #15
16. Reason-able Views to the Web of Data
• Reason-able views represent an approach for
reasoning and management of linked data
• Key ideas:
– Group selected datasets and ontologies in a compound dataset
• Clean up, post-process and enrich the datasets if necessary
• Do this conservatively, in a clearly documented and automated manner, so that:
– the operation can easily be performed each time a new version of one of the datasets is
published
– Users can easily understand the intervention made to the original dataset
– Load the compound dataset in a single semantic repository
– Perform inference with respect to tractable OWL dialects
– Define a set of sample queries against the compound dataset
• These determine the “level of service” or the “scope of consistency” contract
offered by the reason-able view
Data Collection and Integration, LD Management Oct 2010 #16
17. Reason-able Views: Objectives
• Make reasoning and query evaluation feasible
• Guarantee a basic level of consistency
– The sample queries guarantee the consistency of the data in the same
way in which regression tests do for the quality of software
• Guarantee availability
– In the same way in which web search engines are usually more reliable
than most of the web sites; they also to caching
• Easier exploration and querying of unseen data
– Lower the cost of entry through URI auto-complete and RDF search
– Sample queries provide re-usable extraction patterns, which reduce
the time for acquaintance with the datasets and their interconnections
Data Collection and Integration, LD Management Oct 2010 #17
18. Two Reason-able Views to the Web of Linked Data
• FactForge (indicated in red on the next slide)
– Some of the central LOD datasets
– General-purpose information (not specific to a domain)
– 1.2B explicit plus .9M inferred indexed, 10B retrievable statements
– The largest upper-level knowledge base
– http://www.factforge.net/
• Linked Life Data (indicated in yellow)
– 25 of the most popular life-science datasets
– Complemented by gluing ontologies
– 2.7B explicit and 1.4B inferred, total of 4.1B indexed statements
– The largest non-synthetic dataset that was used for reasoning
– http://www.linkedlifedata.com
Data Collection and Integration, LD Management Oct 2010 #18
19. Linking Open Data Datasets and Views (red and yellow)
Data Collection and Integration, LD Management Oct 2010 #19
20. Presentation Outline
• Introduction
• Web Mining
• Data Integration Strategy
• Linked Data Management
– Linked Data: Introduction and Challenges
– FactForge: Fast Track to the Center of the Web of Data
Data Collection and Integration, LD Management Oct 2010 #20
21. FactForge: Fast Track to the Center of the Web of Data
• Datasets: DBPedia, Freebase, Geonames, UMBEL,
MusicBrainz, Wordnet, CIA World Factbook, Lingvoj
• Ontologies: Dublin Core, SKOS, RSS, FOAF
• Inference: materialization with respect to OWL 2 RL
– Seems to completely cover the semantics of the data
– owl:sameAs optimization in BigOWLIM allows reduction of the
indices without loss of semantics, but big gains in performance
• Free public service at http://www.factforge.com,
– Incremental URI auto-suggest
– Query and explore through Forest and Tabulator
– RDF Search: retrieve ranked list of URIs by keywords (see slide 26)
– SPARQL end-point
Data Collection and Integration, LD Management Oct 2010 #21
22. FactForge Loading and Inference Statistics
Explicit Inferred Total # of Entities
Indexed Indexed Stored ('000 of Inferred
Dataset
Triples Triples Triples nodes in closure
('000) ('000) ('000) the graph) ratio
Sechmata and ontologies 11 7 18 6 0.6
DBpedia (categories) 2,877 42,587 45,464 1,144 14.8
DBpedia (sameAs) 5,544 566 6,110 8,464 0.1
UMBEL 5,162 42,212 47,374 500 8.2
Lingvoj 20 863 883 18 43.8
CIA Factbook 76 4 80 25 0.1
Wordnet 2,281 9,296 11,577 830 4.1
Geonames 91,908 125,025 216,933 33,382 1.4
DBpedia core 560,096 198,043 758,139 127,931 0.4
Freebase 463,689 40,840 504,529 94,810 0.1
MusicBrainz 45,536 421,093 466,630 15,595 9.2
Total 1,177,961 881,224 2,058,185 283,253 0.7
Data Collection and Integration, LD Management Oct 2010 #22
23. Post-processing
• Several kinds of post-processing were performed
− Goal: to allow easier navigation and browsing
− Mechanisms: the results are available through system predicates
− For instance: preferred labels, text snippets and RDF Ranks for all
nodes
• Final Statistics
− Number of entities (RDF graph nodes): 405M
− Number of inserted statements (NIS): 1,2B
− Number of stored statements (NSS): 2.2B
− Number of retrievable statements (NRS): 9.8B
− 7.6B statements “compressed” through BigOWLIM’s owl:sameAs
optimisation
Data Collection and Integration, LD Management Oct 2010 #23
24. FactForge Provides Unique Query Capabilities
• Unmatched factual knowledge querying capabilities
– Scope: the largest and most diverse integrated dataset,
including 8 of the central LOD datasets
– Analytical power: the semantics of the data and links
between them is fully accounted for during query evaluation
– RDFRank indicates importance in the integrated dataset
• No other service supports even one of the following:
– Query evaluation against DBPedia, Freebase or Geonames
considering their semantics
– Allows simultaneous querying of multiple datasets,
considering the semantics of the links between them
Data Collection and Integration, LD Management Oct 2010 #24
25. Guess who is the most popular German entertainer?
PREFIX rdf: … (run the query at http://factforge.net/sparql)
SELECT * WHERE {
?Person dbp-ont:birthPlace ?BirthPlace ;
rdf:type opencyc:Entertainer ;
ff:hasRDFRank ?RR .
?BirthPlace geo-ont:parentFeature dbpedia:Germany .
} ORDER BY DESC(?RR) LIMIT 100
• Without FF, answering such queries in real time is impossible:
• Used data from: DBPedia, Geonames, UMBEL and MusicBrainz
• Inference over types, sub-classes, and transitive relationships
• The most popular entertainer born in Germany is:
F. Nietzsche
– Asking factual questions to a global KB can bring unexpected and strange results
– We ask who is the most popular person, who qualifies as an entertainer
– It uses a simple notion of popularity: RDFRank
Data Collection and Integration, LD Management Oct 2010 #25
26. The Modigliani Test for the Semantic Web
• ReadWriteWeb’s founder Richard McManus:
“…the tipping point for the Semantic Web may be
when one can … deliver – using Linked Data – a
comprehensive list of locations of original Modigliani
art works …”
http://www.readwriteweb.com/archives/the_modigliani_test_for_linked_data.php
Data Collection and Integration, LD Management Oct 2010 #26
27. The LDSR Query Passing the Modigliani Test
PREFIX fb: … (check the query at http://factforge.net/sparql)
SELECT DISTINCT
?painting_l ?owner_l ?city_fb_con ?city_db_loc ?city_db_cit
WHERE {
?p fb:visual_art.artwork.artist dbpedia:Amedeo_Modigliani ;
fb:visual_art.artwork.owners [
fb:visual_art.artwork_owner_relationship.owner ?ow ] ;
ff:preferredLabel ?painting_l.
?ow ff:preferredLabel ?owner_l .
OPTIONAL { ?ow fb:location.location.containedby [
ff:preferredLabel ?city_fb_con ] } .
OPTIONAL { ?ow dbp-prop:location ?loc.
?loc rdf:type umbel-sc:City ;
ff:preferredLabel ?city_db_loc }
OPTIONAL { ?ow dbp-ont:city [ ff:preferredLabel ?city_db_cit ]
}
}
Data Collection and Integration, LD Management Oct 2010 #27
28. Thank you!
Questions?
Comments?
Data Collection and Integration, LD Management Oct 2010 #28