Lots of data from different domains is published as Linked Open Data. While there are quite a few browsers for that data, as well as intelligent tools for particular purposes, a versatile tool for deriving additional knowledge by mining the Web of Linked Data is still missing. In this challenge entry, we introduce the RapidMiner Linked Open Data extension. The extension hooks into the powerful data mining platform RapidMiner, and offers operators for accessing Linked Open Data in RapidMiner, allowing for using it in sophisticated data analysis workflows without the need to know SPARQL or RDF. As an example, we show how statistical data on scientific publications, published as an RDF data cube, can be linked to further datasets and analyzed using additional background knowledge from various LOD datasets.
Adoption of the Linked Data Best Practices in Different Topical DomainsChris Bizer
Slides from the presentation of the following paper:
Max Schmachtenberg, Christian Bizer, Heiko Paulheim: Adoption of the Linked Data Best Practices in Different Topical Domains. 13th International Semantic Web Conference (ISWC2014) - RDB Track, pp. 245-260, Riva del Garda, Italy, October 2014.
Paper URL:
http://dws.informatik.uni-mannheim.de/fileadmin/lehrstuehle/ki/pub/SchmachtenbergBizerPaulheim-AdoptionOfLinkedDataBestPractices.pdf
Abstract:
The central idea of Linked Data is that data publishers support applications in discovering and integrating data by complying to a set of best practices in the areas of linking, vocabulary usage, and metadata provision. In 2011, the State of the LOD Cloud report analyzed the adoption of these best practices by linked datasets within different topical domains. The report was based on information that was provided by the dataset publishers themselves via the datahub.io Linked Data catalog. In this paper, we revisit and update the findings of the 2011 State of the LOD Cloud report based on a crawl of the Web of Linked Data conducted in April 2014. We analyze how the adoption of the different best practices has changed and present an overview of the linkage relationships between datasets in the form of an updated LOD cloud diagram, this time not based on information from dataset providers, but on data that can actually be retrieved by a Linked Data crawler. Among others, we find that the number of linked datasets has approximately doubled between 2011 and 2014, that there is increased agreement on common vocabularies for describing certain types of entities, and that provenance and license metadata is still rarely provided by the data sources.
Search Joins with the Web - ICDT2014 Invited LectureChris Bizer
The talk will discuss the concept of Search Joins. A Search Join is a join operation which extends a local table with additional attributes based on the large corpus of structured data that is published on the Web in various formats. The challenge for Search Joins is to decide which Web tables to join with the local table in order to deliver high-quality results. Search joins are useful in various application scenarios. They allow for example a local table about cities to be extended with an attribute containing the average temperature of each city for manual inspection. They also allow tables to be extended with large sets of additional attributes as a basis for data mining, for instance to identify factors that might explain why the inhabitants of one city claim to be happier than the inhabitants of another.
In the talk, Christian Bizer will draw a theoretical framework for Search Joins and will highlight how recent developments in the context of Linked Data, RDFa and Microdata publishing, public data repositories as well as crowd-sourcing integration knowledge contribute to the feasibility of Search Joins in an increasing number of topical domains.
DBpedia - An Interlinking Hub in the Web of DataChris Bizer
Given and overview about the DBpedia project and the role of DBpedia in the Web of Data and outlines the next steps from the Dbpedia project as well as ideas for using DBpedia data within the BBC.
Evolving the Web into a Global Dataspace – Advances and ApplicationsChris Bizer
Keynote talk at the 18th International Conference on Business Information Systems, 24-26 June 2015, Poznań, Poland
URL:
http://bis.kie.ue.poznan.pl/bis2015/keynote-speakers/
Abstract:
Motivated by Google, Yahoo!, Microsoft, and Facebook, hundreds of thousands of websites have started to annotate structured data within their pages using markup formats such as Microdata, RDFa, and Microformats. In parallel, the adoption of Linked Data technologies by government agencies, libraries, and scientific institutions has risen considerably. In his talk, Christian Bizer will give an overview of the content profile of the resulting Web of Data. He will showcase applications that exploit the Web of Data and will discuss the challenges of integrating and cleansing data from thousands of independent Web 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/.
Adoption of the Linked Data Best Practices in Different Topical DomainsChris Bizer
Slides from the presentation of the following paper:
Max Schmachtenberg, Christian Bizer, Heiko Paulheim: Adoption of the Linked Data Best Practices in Different Topical Domains. 13th International Semantic Web Conference (ISWC2014) - RDB Track, pp. 245-260, Riva del Garda, Italy, October 2014.
Paper URL:
http://dws.informatik.uni-mannheim.de/fileadmin/lehrstuehle/ki/pub/SchmachtenbergBizerPaulheim-AdoptionOfLinkedDataBestPractices.pdf
Abstract:
The central idea of Linked Data is that data publishers support applications in discovering and integrating data by complying to a set of best practices in the areas of linking, vocabulary usage, and metadata provision. In 2011, the State of the LOD Cloud report analyzed the adoption of these best practices by linked datasets within different topical domains. The report was based on information that was provided by the dataset publishers themselves via the datahub.io Linked Data catalog. In this paper, we revisit and update the findings of the 2011 State of the LOD Cloud report based on a crawl of the Web of Linked Data conducted in April 2014. We analyze how the adoption of the different best practices has changed and present an overview of the linkage relationships between datasets in the form of an updated LOD cloud diagram, this time not based on information from dataset providers, but on data that can actually be retrieved by a Linked Data crawler. Among others, we find that the number of linked datasets has approximately doubled between 2011 and 2014, that there is increased agreement on common vocabularies for describing certain types of entities, and that provenance and license metadata is still rarely provided by the data sources.
Search Joins with the Web - ICDT2014 Invited LectureChris Bizer
The talk will discuss the concept of Search Joins. A Search Join is a join operation which extends a local table with additional attributes based on the large corpus of structured data that is published on the Web in various formats. The challenge for Search Joins is to decide which Web tables to join with the local table in order to deliver high-quality results. Search joins are useful in various application scenarios. They allow for example a local table about cities to be extended with an attribute containing the average temperature of each city for manual inspection. They also allow tables to be extended with large sets of additional attributes as a basis for data mining, for instance to identify factors that might explain why the inhabitants of one city claim to be happier than the inhabitants of another.
In the talk, Christian Bizer will draw a theoretical framework for Search Joins and will highlight how recent developments in the context of Linked Data, RDFa and Microdata publishing, public data repositories as well as crowd-sourcing integration knowledge contribute to the feasibility of Search Joins in an increasing number of topical domains.
DBpedia - An Interlinking Hub in the Web of DataChris Bizer
Given and overview about the DBpedia project and the role of DBpedia in the Web of Data and outlines the next steps from the Dbpedia project as well as ideas for using DBpedia data within the BBC.
Evolving the Web into a Global Dataspace – Advances and ApplicationsChris Bizer
Keynote talk at the 18th International Conference on Business Information Systems, 24-26 June 2015, Poznań, Poland
URL:
http://bis.kie.ue.poznan.pl/bis2015/keynote-speakers/
Abstract:
Motivated by Google, Yahoo!, Microsoft, and Facebook, hundreds of thousands of websites have started to annotate structured data within their pages using markup formats such as Microdata, RDFa, and Microformats. In parallel, the adoption of Linked Data technologies by government agencies, libraries, and scientific institutions has risen considerably. In his talk, Christian Bizer will give an overview of the content profile of the resulting Web of Data. He will showcase applications that exploit the Web of Data and will discuss the challenges of integrating and cleansing data from thousands of independent Web 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/.
A talk given at the Ingenta Publisher Forum, in November 2008.
See: http://www.ldodds.com/blog/archives/000264.html
For a detailed description of the talk.
Extending Tables with Data from over a Million WebsitesChris Bizer
The slideset describes the Mannheim Search Join Engine and was used to present our submission to the Semantic Web Challenge 2014.
More information about the Semantic Web Challenge:
http://challenge.semanticweb.org/
Paper about the Mannheim Search Join Engine:
http://dws.informatik.uni-mannheim.de/fileadmin/lehrstuehle/ki/pub/Lehmberg-Ritze-Ristoski-Eckert-Paulheim-Bizer-TableExtension-SemanticWebChallenge-ISWC2014-Paper.pdf
Abstract:
This Big Data Track submission demonstrates how the BTC 2014 dataset, Microdata annotations from thousands of websites, as well as millions of HTML tables are used to extend local tables with additional columns. Table extension is a useful operation within a wide range of application scenarios: Image you are an analyst having a local table describing companies and you want to extend this table with the headquarter of each company. Or imagine you are a film lover and want to extend a table describing films with attributes like director, genre, and release date of each film. The Mannheim SearchJoin Engine automatically performs such table extension operations based on a large data corpus gathered from over a million websites that publish structured data in various formats. Given a local table, the SearchJoin Engine searches the corpus for additional data describing the entities of the input table. The discovered data are then joined with the local table and their content is consolidated using schema matching and data fusion methods. As result, the user is presented with an extended table and given the opportunity to examine the provenance of the added data. Our experiments show that the Mannheim SearchJoin Engine achieves a coverage close to 100% and a precision of around 90% within different application scenarios.
[Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry ...Ontotext
This webinar continues series are demonstrating how linked open data and semantic tagging of news can be used for comprehensive media monitoring, market and business intelligence. The platform for the demonstrations is FactForge: a hub for news and data about people, organizations, and locations (POL). FactForge embodies a big knowledge graph (BKG) of more than 1 billion facts that allows various analytical queries, including tracing suspicious patterns of company control; media monitoring of people, including companies owned by them, their subsidiaries, etc.
The slideset used to conduct an introduction/tutorial
on DBpedia use cases, concepts and implementation
aspects held during the DBpedia community meeting
in Dublin on the 9th of February 2015.
(slide creators: M. Ackermann, M. Freudenberg
additional presenter: Ali Ismayilov)
A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary ...Robert Meusel
Promoted by major search engines, schema.org has become a widely adopted standard for marking up structured data in HTML web pages. In this paper, we use a series of largescale Web crawls to analyze the evolution and adoption of schema.org over time. The availability of data from dierent points in time for both the schema and the websites deploying data allows for a new kind of empirical analysis of standards adoption, which has not been possible before. To conduct our analysis, we compare dierent versions of the schema.org vocabulary to the data that was deployed on hundreds of thousands of Web pages at dierent points in time. We measure both top-down adoption (i.e., the extent to which changes in the schema are adopted by data providers) as well as bottom-up evolution (i.e., the extent to which the actually deployed data drives changes in the schema). Our empirical analysis shows that both processes can be observed.
Maintaining scholarly standards in the digital age: Publishing historical gaz...Humphrey Southall
This presentation: (1( Discusses why providing detailed attributions of individual contributions is essential to large scale sharing of historical research data; (2) Provides a short introduction to Open Linked Data; (3) Introduces the PastPlace Gazetteer API (Applications Programming Interface), explaining components of the RDF it generates using the example of Oxford, UK; (4) Notes that most open data projects use the Creative Commons -- Must Ackowledge license (CC-BY) while not actually acknowledging contributors within their RDF, then shows how we do it; (5) Introduces the separate PastPlace Datafeed API, which implements the W3C Datacube Vocabulary.
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.
The original Semantic Web vision foresees to describe entities in a way that the meaning can be interpreted both by machines and humans. Following that idea, large-scale knowledge graphs capturing a significant portion of knowledge have been developed. In the recent past, vector space embeddings of semantic web knowledge graphs - i.e., projections of a knowledge graph into a lower-dimensional, numerical feature
space (a.k.a. latent feature space) - have been shown to yield superior performance in many tasks, including relation prediction, recommender systems, or the enrichment of predictive data mining tasks. At the same time, those projections describe an entity as a numerical vector, without
any semantics attached to the dimensions. Thus, embeddings are as far from the original Semantic Web vision as can be. As a consequence, the results achieved with embeddings - as impressive as they are in terms of quantitative performance - are most often not interpretable, and it is hard to obtain a justification for a prediction, e.g., an explanation why an item has been suggested by a recommender system. In this paper, we make a claim for semantic embeddings and discuss possible ideas towards their construction.
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.
A talk given at the Ingenta Publisher Forum, in November 2008.
See: http://www.ldodds.com/blog/archives/000264.html
For a detailed description of the talk.
Extending Tables with Data from over a Million WebsitesChris Bizer
The slideset describes the Mannheim Search Join Engine and was used to present our submission to the Semantic Web Challenge 2014.
More information about the Semantic Web Challenge:
http://challenge.semanticweb.org/
Paper about the Mannheim Search Join Engine:
http://dws.informatik.uni-mannheim.de/fileadmin/lehrstuehle/ki/pub/Lehmberg-Ritze-Ristoski-Eckert-Paulheim-Bizer-TableExtension-SemanticWebChallenge-ISWC2014-Paper.pdf
Abstract:
This Big Data Track submission demonstrates how the BTC 2014 dataset, Microdata annotations from thousands of websites, as well as millions of HTML tables are used to extend local tables with additional columns. Table extension is a useful operation within a wide range of application scenarios: Image you are an analyst having a local table describing companies and you want to extend this table with the headquarter of each company. Or imagine you are a film lover and want to extend a table describing films with attributes like director, genre, and release date of each film. The Mannheim SearchJoin Engine automatically performs such table extension operations based on a large data corpus gathered from over a million websites that publish structured data in various formats. Given a local table, the SearchJoin Engine searches the corpus for additional data describing the entities of the input table. The discovered data are then joined with the local table and their content is consolidated using schema matching and data fusion methods. As result, the user is presented with an extended table and given the opportunity to examine the provenance of the added data. Our experiments show that the Mannheim SearchJoin Engine achieves a coverage close to 100% and a precision of around 90% within different application scenarios.
[Webinar] FactForge Debuts: Trump World Data and Instant Ranking of Industry ...Ontotext
This webinar continues series are demonstrating how linked open data and semantic tagging of news can be used for comprehensive media monitoring, market and business intelligence. The platform for the demonstrations is FactForge: a hub for news and data about people, organizations, and locations (POL). FactForge embodies a big knowledge graph (BKG) of more than 1 billion facts that allows various analytical queries, including tracing suspicious patterns of company control; media monitoring of people, including companies owned by them, their subsidiaries, etc.
The slideset used to conduct an introduction/tutorial
on DBpedia use cases, concepts and implementation
aspects held during the DBpedia community meeting
in Dublin on the 9th of February 2015.
(slide creators: M. Ackermann, M. Freudenberg
additional presenter: Ali Ismayilov)
A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary ...Robert Meusel
Promoted by major search engines, schema.org has become a widely adopted standard for marking up structured data in HTML web pages. In this paper, we use a series of largescale Web crawls to analyze the evolution and adoption of schema.org over time. The availability of data from dierent points in time for both the schema and the websites deploying data allows for a new kind of empirical analysis of standards adoption, which has not been possible before. To conduct our analysis, we compare dierent versions of the schema.org vocabulary to the data that was deployed on hundreds of thousands of Web pages at dierent points in time. We measure both top-down adoption (i.e., the extent to which changes in the schema are adopted by data providers) as well as bottom-up evolution (i.e., the extent to which the actually deployed data drives changes in the schema). Our empirical analysis shows that both processes can be observed.
Maintaining scholarly standards in the digital age: Publishing historical gaz...Humphrey Southall
This presentation: (1( Discusses why providing detailed attributions of individual contributions is essential to large scale sharing of historical research data; (2) Provides a short introduction to Open Linked Data; (3) Introduces the PastPlace Gazetteer API (Applications Programming Interface), explaining components of the RDF it generates using the example of Oxford, UK; (4) Notes that most open data projects use the Creative Commons -- Must Ackowledge license (CC-BY) while not actually acknowledging contributors within their RDF, then shows how we do it; (5) Introduces the separate PastPlace Datafeed API, which implements the W3C Datacube Vocabulary.
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.
The original Semantic Web vision foresees to describe entities in a way that the meaning can be interpreted both by machines and humans. Following that idea, large-scale knowledge graphs capturing a significant portion of knowledge have been developed. In the recent past, vector space embeddings of semantic web knowledge graphs - i.e., projections of a knowledge graph into a lower-dimensional, numerical feature
space (a.k.a. latent feature space) - have been shown to yield superior performance in many tasks, including relation prediction, recommender systems, or the enrichment of predictive data mining tasks. At the same time, those projections describe an entity as a numerical vector, without
any semantics attached to the dimensions. Thus, embeddings are as far from the original Semantic Web vision as can be. As a consequence, the results achieved with embeddings - as impressive as they are in terms of quantitative performance - are most often not interpretable, and it is hard to obtain a justification for a prediction, e.g., an explanation why an item has been suggested by a recommender system. In this paper, we make a claim for semantic embeddings and discuss possible ideas towards their construction.
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.
घरेलु बालश्रम परिचय
विश्वमा २४ करोडभन्दा बढी बालबालिकाहरू श्रममा संलग्न रहेको तितो यथार्थभित्र बाँचिरहेका हाम्रा बालबालिकाहरूको भविष्य र त्यसले झल्काउने हाम्रो समाजको वर्तमान तथा भविष्य दुवै अत्यन्तै गम्भीर र चिन्ताजनक देखिन्छ । हाम्रो देशमा पनि बीस लाखभन्दा बढी बालबालिकाहरू श्रममा संलग्न रहेका छन् जसमध्ये १ लाख २७ हजार बालबालिकाहरू निकृष्ट प्रकारको बालश्रममा संलग्न रही आफ्नो जीवन यापन गरिरहेका छन् । यसरी निकृष्ट बाल श्रममा संलग्न हुन बाध्य भएका बालबालिकाहरूमा सर्वाधिक संख्या घरेलु बालश्रमिकहरूको रहेको छ । अन्तर्राष्ट्रिय श्रम संगठनको तथ्याङ्कअनुसार नेपालका सहरी क्षेत्रमा मात्र ५५ हजारभन्दा बढी बालबालिकाहरू घरेलु श्रममा संलग्न रहेका छन् । घरेलु श्रममा संलग्न बालबालिकाहरू विभिन्नखाले हिंसा, शोषण तथा दुव्र्यवहारहरू खप्न बाध्य भइरहेका छन् र पनि यस्तो अवस्थामा थोरै मात्र संघसंस्था तथा सरकारी निकायहरूले यस क्षेत्रलाई आवश्यक मात्रामा ध्यान पुर्याउन सकिरहेको देखिँदैन भने भइरहेका प्रयासहरूमा पनि कमी कमजोरीहरू देखिन्छन् ।
यही करु ालार्इ मध्यनजर गर्दै सिविसले घरेलु बालश्रम र हस्तक्षेपका उपायहरू विषयभित्र विभिन्न कार्यि नर्दिेशकाहरू तयार गरकेा छ र त्यसै अन्तगर्त याे ”घरेलु बालश्रम परिचय” पुस्तक यहाँहरूमाझ प्रस्तुत गर्न पाउँदा हामीलाई अत्यन्तै खुसी लागेको छ ।
Early College Academy is Greeley's newest high school. This powerpoint presentation was given to parents and potential students as part of a promotional campaign.
A quick overview on REST : what it is and what it is not. REST has strict contraints and many internet Apis are not so REST. It’s also very popular today because RESTfull services can be consumed easily by any client or device. Soap is also still valid in a few circomstaces. It has never been so easy to create Rest-like services in .net since asp.net Web Api.
RD shared services and research data springJisc RDM
Daniela Duca's presentation at the DataVault workshop on 29 June. An overview of research at risk, research data shared service and research data spring.
Easy SPARQLing for the Building Performance ProfessionalMartin Kaltenböck
Slides of Martin Kaltenböcks (SWC) presentation at SEMANTiCS2014 conference in Leipzig on 5th of September 2014 about the 'Tool for Building Energy Performance Scenarios' of GBPN (Global Buildings Performance Network, http://gbpn.org) that provides a prediction tool for buildings performance worldwide by making use of Linked Open Data (LOD).
Open Data management is still not trivial nor sustainable - COMSODE results are here to bring automation to publication and management of Open Data in public institutions and companies. Presentation includes Open Data Ready standard proposal, three use cases and invitation for Horizon 2020 projects 2016.
In recent years governments and research institutions have emphasized the need for open data as a fundamental component of open science. But we need much more than the data themselves for them to be reusable and useful. We need descriptive and machine-readable metadata, of course, but we also need the software and the algorithms necessary to fully understand the data. We need the standards and protocols that allow us to easily read and analyze the data with the tools of our choice. We need to be able to trust the source and derivation of the data. In short, we need an interoperable data infrastructure, but it must be a flexible infrastructure able to work across myriad cultures, scales, and technologies. This talk will present a concept of infrastructure as a body of human, organisational, and machine relationships built around data. It will illustrate how a new organization, the Research Data Alliance, is working to build those relationships to enable functional data sharing and reuse.
Slides from our tutorial on Linked Data generation in the energy domain, presented at the Sustainable Places 2014 conference on October 2nd in Nice, France
Linked Open Data Principles, benefits of LOD for sustainable developmentMartin Kaltenböck
Presentation held on 18.09.2013 at the OKCon 2013 in Geneva, Switzerland in the course of the workshop: How Linked Open data supports Sustainable Development and Climate Change Development by Martin Kaltenböck (SWC), Florian Bauer (REEEP) and Jens Laustsen (GBPN).
Big Data HPC Convergence and a bunch of other thingsGeoffrey Fox
This talk supports the Ph.D. in Computational & Data Enabled Science & Engineering at Jackson State University. It describes related educational activities at Indiana University, the Big Data phenomena, jobs and HPC and Big Data computations. It then describes how HPC and Big Data can be converged into a single theme.
Researcher data management shared service for the UK – John Kaye, Jisc
Hydra - Tom Cramer, Stanford University and Chris Awre, University of Hull
Addressing the preservation gap at the University of York - Jenny Mitcham, University of York
Emulation developments - David Rosenthal, Stanford University
Jisc and CNI conference, 6 July 2016
Presented at the Workshop for Sustaonable Software for Science: Practice and Experiment (WSSSPE). Part of Supercomputing 2013 (SC13) in denver Colorado.
Knowledge Graph Generation from Wikipedia in the Age of ChatGPT: Knowledge ...Heiko Paulheim
In the past years, sophisticated methods for extracting knowledge graphs from Wikipedia, like DBpedia,YAGO, and CaLiGraph, have been developed. In this talk, I revisit some of these methods and examine if and how they can be replaced by prompting a large language model like ChatGPT.
Knowledge graph embeddings are a mechanism that projects each entity in a knowledge graph to a point in a continuous vector space. It is commonly assumed that those approaches project two entities closely to each other if they are similar and/or related. In this talk, I give a closer look at the roles of similarity and relatedness with respect to knowledge graph embeddings, and discuss how the well-known embedding mechanism RDF2vec can be tailored towards focusing on similarity, relatedness, or both.
RDF2vec is a method for creating embeddings vectors for entities in knowledge graphs. In this talk, I introduce the basic idea of RDF2vec, as well as the latest extensions developments, like the use of different walk strategies, the flavour of order-aware RDF2vec, RDF2vec for dynamic knowledge graphs, and more.
Using knowledge graphs in data mining typically requires a propositional, i.e., vector-shaped representation of entities. RDF2vec is an example for generating such vectors from knowledge graphs, relying on random walks for extracting pseudo-sentences from a graph, and utilizing word2vec for creating embedding vectors from those pseudo-sentences. In this talk, I will give insights into the idea of RDF2vec, possible application areas, and recently developed variants incorporating different walk strategies and training variations.
Knowledge Matters! The Role of Knowledge Graphs in Modern AI SystemsHeiko Paulheim
AI is not just about machine learning, it also requires knowledge about the world. In this talk, I give an introduction on knowledge graphs, how they are built at scale, and how they are used in modern AI systems.
This presentation shows approaches for knowledge graph construction from Wikipedia and other Wikis that go beyond the "one entity per page" paradigm. We see CaLiGraph, which extracts entities from categories and listings, as well as DBkWik, which extracts and integrates information from thousands of Wikis.
Using Knowledge Graphs in Data Science - From Symbolic to Latent Representati...Heiko Paulheim
Knowledge Graphs are often used as a symbolic representation mechanism for representing knowledge in data intensive applications, both for integrating corporate knowledge as well as for providing general, cross-domain knowledge in public knowledge graphs such as Wikidata. As such, they have been identified as a useful way of injecting background knowledge in data analysis processes. To fully harness the potential of knowledge graphs, latent representations of entities in the graphs, so called knowledge graph embeddings, show superior performance, but sacrifice one central advantage of knowledge graphs, i.e., the explicit symbolic knowledge representations. In this talk, I will shed some light on the usage of knowledge graphs and embeddings in data analysis, and give an outlook on research directions which aim at combining the best of both worlds.
Beyond DBpedia and YAGO – The New Kids on the Knowledge Graph BlockHeiko Paulheim
Starting with Cyc in the 1980s, the collection of general
knowledge in machine interpretable form has been considered a valuable ingredient in intelligent and knowledge intensive applications. Notable contributions in the field include the Wikipedia-based datasets DBpedia and YAGO, as well as the collaborative knowledge base Wikidata. Since Google has coined the term in 2012, they are most often referred to as knowledge graphs. Besides such open knowledge graphs, many companies have started using corporate knowledge graphs as a means of information representation.
In this talk, I will look at two ongoing projects related to the extraction of knowledge graphs from Wikipedia and other Wikis. The first new dataset, CaLiGraph, aims at the generation of explicit formal definitions from categories, and the extraction of new instances from list pages. In its current release, CaLiGraph contains 200k axioms defining classes,
and more than 7M typed instances. In the second part, I will look at the transfer of the DBpedia approach to a multitude of arbitrary Wikis. The first such prototype, DBkWik, extracts data from Fandom, a Wiki farm hosting more than 400k different Wikis on various topics. Unlike DBpedia, which relies on a larger user base for crowdsourcing an explicit schema and extraction rules, and the "one-page-per-entity" assumption, DBkWik has to address various challenges in the fields of schema learning and data integration. In its current release, DBkWik contains more than 11M entities, and has been found to be highly complementary to DBpedia.
From Wikipedia to Thousands of Wikis – The DBkWik Knowledge GraphHeiko Paulheim
From a bird eye's view, the DBpedia Extraction Framework takes a MediaWiki dump as input, and turns it into a knowledge graph. In this talk, I discuss the creation of the DBkWik knowledge graph by applying the DBpedia Extraction Framework to thousands of Wikis.
Knowledge graphs are used in various applications and have
been widely analyzed. A question that is not very well researched is: what is the price of their production? In this paper, we propose ways to estimate the cost of those knowledge graphs. We show that the cost of manually curating a triple is between $2 and $6, and that the cost for automatically created knowledge graphs is by a factor of 15 to 150 cheaper (i.e., 1c to 15c per statement). Furthermore, we advocate for taking cost into account as an evaluation metric, showing the correspondence between cost per triple and semantic validity as an example.
Machine Learning with and for Semantic Web Knowledge GraphsHeiko Paulheim
Large-scale cross-domain knowledge graphs, such as DBpedia or Wikidata, are some of the most popular and widely used datasets of the Semantic Web. In this paper, we introduce some of the most popular knowledge graphs on the Semantic Web. We discuss how machine learning is used to improve those knowledge graphs, and how they can be exploited as background knowledge in popular machine learning tasks, such as recommender systems.
Weakly Supervised Learning for Fake News Detection on TwitterHeiko Paulheim
The problem of automatic detection of fake news insocial media, e.g., on Twitter, has recently drawn some attention. Although, from a technical perspective, it can be regarded
as a straight-forward, binary classification problem, the major
challenge is the collection of large enough training corpora, since manual annotation of tweets as fake or non-fake news is an expensive and tedious endeavor. In this paper, we discuss a weakly supervised approach, which automatically collects a large-scale, but very noisy training dataset comprising hundreds of thousands of tweets. During collection, we automatically label tweets by their source, i.e., trustworthy or untrustworthy source, and train a classifier on this dataset. We then use that classifier for a different classification target, i.e., the classification of fake and non-fake tweets. Although the labels are not accurate according to the new classification target (not all tweets by an untrustworthy source need to be fake news, and vice versa), we show that despite this unclean inaccurate dataset, it is possible to detect fake news with an F1 score of up to 0.9.
Knowledge Graphs, such as DBpedia, YAGO, or Wikidata, are valuable resources for building intelligent applications like data analytics tools or recommender systems. Understanding what is in those knowledge graphs is a crucial prerequisite for selecing a Knowledge Graph for a task at hand. Hence, Knowledge Graph profiling - i.e., quantifying the structure and contents of knowledge graphs, as well as their differences - is essential for fully utilizing the power of Knowledge Graphs. In this paper, I will discuss methods for Knowledge Graph profiling, depict crucial differences of the big, well-known Knowledge Graphs, like DBpedia, YAGO, and Wikidata, and throw a glance at current developments of new, complementary Knowledge Graphs such as DBkWik and WebIsALOD.
How are Knowledge Graphs created?
What is inside public Knowledge Graphs?
Addressing typical problems in Knowledge Graphs (errors, incompleteness)
New Knowledge Graphs: WebIsALOD, DBkWik
Data-driven Joint Debugging of the DBpedia Mappings and OntologyHeiko Paulheim
DBpedia is a large-scale, cross-domain knowledge graph extracted from Wikipedia. For the extraction, crowd-sourced mappings from Wikipedia infoboxes to the DBpedia ontology are utilized. In this process, different problems may arise: users may create wrong and/or inconsistent mappings, use the ontology in an unforeseen way, or change the ontology without considering all possible consequences. In this paper, we present a data-driven approach to discover problems in mappings as well as in the ontology and its usage in a joint, data-driven process. We show both quantitative and qualitative results about the problems identified, and derive proposals for altering mappings and refactoring the DBpedia ontology.
Fast Approximate A-box Consistency Checking using Machine LearningHeiko Paulheim
Ontology reasoning is typically a computationally intensive operation. While soundness and completeness of results is required in some use cases, for many others, a sensible trade-off between computation efforts and correctness of results makes more sense. In this paper, we show that it is possible to approximate a central task in reasoning, i.e., A-box consistency checking, by training a machine learning model which approximates the behavior of that reasoner for a specific ontology. On four different datasets, we show that such learned models constantly achieve an accuracy above 95% at less than 2% of the runtime of a reasoner, using a decision tree with no more than 20 inner nodes. For example, this allows for validating 293M Microdata documents against the schema.org ontology in less than 90 minutes, compared to 18 days required by a state of the art ontology reasoner.
Serving DBpedia with DOLCE - More Than Just Adding a Cherry on TopHeiko Paulheim
Large knowledge bases, such as DBpedia, are most often created heuristically due to scalability issues. In the building process, both random as well as systematic errors may occur. In this paper, we focus on finding systematic errors, or anti-patterns, in DBpedia. We show that by aligning the DBpedia ontology to the foundational ontology DOLCE-Zero, and by combining reasoning and clustering of the reasoning results, errors affecting millions of statements can be identified at a minimal workload for the knowledge base designer.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
1. Mining the Web of Linked Data
with RapidMiner
Introducing the RapidMiner
Linked Open Data Extension
Petar Ristoski, Christian Bizer, Heiko Paulheim
2. Motivation
Which factors lead to a high corruption rate?
How to improve the quality of living?
How to find good books to read?
How to publish more scientific articles?
How to prevent inflation?
What makes cars to consume less fuel?
How to decrease the electricity consumption?
10/27/14 Ristoski, Bizer, Paulheim 2
4. Motivation
Local LOD
Data
link combine cleanse transform analyze
10/27/14 Ristoski, Bizer, Paulheim 4
5. RapidMiner Linked Open Data Extension
Introducing RapidMiner:
● An open source platform for data mining and predictive analytics
● Processes are designed by wiring operators in a GUI
(no programming)
● Operators for data loading, transformation, modeling, visualization, …
● Scalable, distributed, parallel processing in a cloud environment
● 200,000 active users
● Developers can write their own extensions
10/27/14 Ristoski, Bizer, Paulheim 5
6. RapidMiner Linked Open Data Extension
• The extension adds operators for
– accessing local and remote semantic web data (RDF, SPARQL, …)
– linking local to remote data (e.g., DBpedia Lookup)
– enriching local data (e.g., with data properties from LOD sources)
– automatically following links to other datasets
– exploiting semantic schemata for optimizing attribute subset selection
(DiscoveryScience'14)
– matching and fusing data from different sources
• Data analysts can use it without knowing SPARQL etc.
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7. Example Use Case
• Which factors correlate with the increase of published scientific and
technical journal articles?
• RapidMiner workflow:
– Import data from WorldBank RDF data cube
– Link countries to DBpedia
– Explore additional datasets
– Generate attributes
– Analyze the results
• now live!
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8. Example Use Case
• Starting from links to DBpedia, we follow links and collect data from
– DBpedia
– Linked GeoData
– Eurostat
– GeoNames
– WHO’s Global Health
Observatory
– Linked Energy Data
– OpenCyc
– World Factbook
– YAGO
• Related data is fused
– e.g., population figures from different sources
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9. Example Use Case
• Factors that correlate with large number of publications
– The fragile state index – FSI (positive)
– Human development index – HDI (positive)
– GDP (positive)
• wealthier countries being able to invest
more federal money into science funding?
– For EU countries, the number of EU seats (positive)
• an increasing fraction of EU funding for science
being attributed to those countries?
– Many climate indicators (precipitation, hours of sun, temperature)
• unequal distribution of wealth across different climate zones?
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10. Other Use Cases
• Improving performance of predictive models (RMWorld'14)
– UCI car dataset: predicting fuel consumption
• Reducing the prediction error of M5' by half
– on average, we are wrong by 1.6 instead of 2.9 MPG
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11. Other Use Cases
• Building Semantic Recommeder Systems (ESWC'14)
• Combines two extensions:
– Linked Open Data extension
– Recommender system extension
• Use data about books
for content-based recommender
– best system (out of 24)
on two out of three tasks
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12. Other Use Cases
• Debugging Linked Open Data
– loading a subset of statements
– augment with additional features
– run outlier detection
• again: a special
extension
• Example: identify wrong
dataset interlinks
(WoDOOM'14)
– AUC up to 85%
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13. Summary
• This challenge entry
– brings data analysis to the web of data
– can be used by data analysts without learning SPARQL
• Availability
– on the RapidMiner
marketplace
– installable from
inside RapidMiner
– >4,000 installations
and counting
10/27/14 Ristoski, Bizer, Paulheim 14
14. Mining the Web of Linked Data
with RapidMiner
Introducing the RapidMiner
Linked Open Data Extension
Petar Ristoski, Christian Bizer, Heiko Paulheim