Being promoted by major search engines such as Google,
Yahoo!, Bing, and Yandex, Microdata embedded in web pages, especially using schema.org, has become one of the most important markup languages for the Web. However, deployed Microdata is most often not free from errors, which limits its practical use. In this paper, we use
the WebDataCommons corpus of Microdata extracted from more than 250 million web pages for a quantitative analysis of common mistakes in Microdata provision. Since it is unrealistic that data providers will provide clean and correct data, we discuss a set of heuristics that can be applied on the data consumer side to fix many of those mistakes in a
post-processing step. We apply those heuristics to provide an improved knowledge base constructed from the raw Microdata extraction.
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.
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.
The Graph Structure of the Web - Aggregated by Pay-Level Domainoli-unima
The document summarizes research on analyzing the structure of the 2012 web graph when aggregated by pay-level domain (PLD) rather than by individual pages. Some key findings include: the indegree distribution follows a power law but the outdegree distribution does not; the bow-tie structure is unbalanced with a large OUT component compared to previous studies; approximately 42% of domains are connected by paths and the average path length is 4.27 hops; and high connectivity depends more on links to hubs than on hubs themselves. Analysis of topic-specific subgraphs and the public suffix graph show varying patterns of internal and external links.
Mining the Web of Linked Data with RapidMinerHeiko Paulheim
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.
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/.
Data.dcs: Converting Legacy Data into Linked DataMatthew Rowe
This document discusses converting legacy data from the Department of Computer Science (DCS) at the University of Sheffield into linked data. It describes extracting data from websites and publications databases, converting it to RDF triples, resolving duplicate entities, and linking the data to external datasets like DBLP. The goal is to make DCS data about people, publications, and research groups machine-readable and queryable while integrating it into the larger web of linked open data.
The Semantic Web – A Vision Come True, or Giving Up the Great Plan?Martin Hepp
The document discusses the current state and future of the Semantic Web and linked data initiatives. It notes several successes such as the Linked Open Data cloud and schemas like Schema.org and GoodRelations. However, it argues that the original vision of the Semantic Web, which aimed to allow computers to help process information by applying structured data standards at web scale, has not fully been realized. Schemas like Schema.org focus more on information extraction than direct data consumption. The document calls for challenging assumptions through empirical analysis rather than ideological debates.
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.
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.
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.
The Graph Structure of the Web - Aggregated by Pay-Level Domainoli-unima
The document summarizes research on analyzing the structure of the 2012 web graph when aggregated by pay-level domain (PLD) rather than by individual pages. Some key findings include: the indegree distribution follows a power law but the outdegree distribution does not; the bow-tie structure is unbalanced with a large OUT component compared to previous studies; approximately 42% of domains are connected by paths and the average path length is 4.27 hops; and high connectivity depends more on links to hubs than on hubs themselves. Analysis of topic-specific subgraphs and the public suffix graph show varying patterns of internal and external links.
Mining the Web of Linked Data with RapidMinerHeiko Paulheim
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.
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/.
Data.dcs: Converting Legacy Data into Linked DataMatthew Rowe
This document discusses converting legacy data from the Department of Computer Science (DCS) at the University of Sheffield into linked data. It describes extracting data from websites and publications databases, converting it to RDF triples, resolving duplicate entities, and linking the data to external datasets like DBLP. The goal is to make DCS data about people, publications, and research groups machine-readable and queryable while integrating it into the larger web of linked open data.
The Semantic Web – A Vision Come True, or Giving Up the Great Plan?Martin Hepp
The document discusses the current state and future of the Semantic Web and linked data initiatives. It notes several successes such as the Linked Open Data cloud and schemas like Schema.org and GoodRelations. However, it argues that the original vision of the Semantic Web, which aimed to allow computers to help process information by applying structured data standards at web scale, has not fully been realized. Schemas like Schema.org focus more on information extraction than direct data consumption. The document calls for challenging assumptions through empirical analysis rather than ideological debates.
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.
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.
Graph Structure in the Web - Revisited. WWW2014 Web Science TrackChris Bizer
The document discusses research that revisits the graph structure of the web using a new large crawl from Common Crawl. It finds that the web has become more dense and connected over time, with the largest strongly connected component growing significantly. While previous research found power laws for in- and out-degrees, this data does not fit power laws and instead has heavy-tailed distributions. The shape of the bow-tie structure also depends on the specific crawl used. The authors provide the new crawl data and analysis to enable further research on the evolving structure of the web graph.
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.
The document describes a method for focused crawling to retrieve structured data from web pages. It involves using an online classifier trained on URL features to identify pages containing structured data. A bandit-based selection strategy is used to balance exploration and exploitation. Experiments show the adaptive approach retrieves 26% more relevant pages than static classification, and 66% more when focused on a specific objective. Decaying the bandit randomness over time improved results further. The method was able to retrieve hundreds of millions of structured data pages from billions of web pages.
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.
This document provides an introduction to REST (Representational State Transfer), including definitions of REST and its key principles and components. It discusses that REST uses HTTP methods to perform CRUD operations on resources, focuses on resources over methods, and is stateless. The four basic design principles of REST are also summarized: use HTTP methods explicitly, be stateless, expose directory structure-like URLs, and transfer XML or JSON.
This document discusses applications of linked data and semantic web technologies. It describes the linked open data cloud and prominent datasets like DBpedia. It provides statistics about the size and connectivity of linked open data. It also discusses ontologies, browsers, and search engines that facilitate working with linked data. Finally, it outlines the components needed to build linked data driven web applications and access linked data through SPARQL endpoints and libraries.
Context clues are hints in a text that help identify the meaning of unknown words. To use context clues, examine the sentence before, the sentence with, and the sentence after the unknown word. There are three steps to determine a word's meaning from context clues: 1) Check for synonyms or definitions in the sentence, 2) Check for contrast clues and consider the opposite meaning, 3) Reread and substitute a fitting word. Examples are provided to demonstrate using context clues to determine the correct word for each sentence.
Cloud Foundry Summit 2015: 10 common errors when pushing apps to cloud foundryJack-Junjie Cai
You may experience some errors when you push your application to CloudFoundry. Some of them are easier to figure out, while others may be mysterious and harder to diagnose. This session will examine 10 common errors that may happen during application push, including their symptom, the tools and techniques to diagnose them, and the possible solutions. The session will mostly focus on Java and node.js applications, but some of the tips applies to all runtimes.
Context clues are facts or ideas in the text that help suggest the meaning of an unknown word. There are different types of context clues including definition, restatement, examples, comparison/contrast, cause and effect, punctuation, and modifiers. Exercises are provided to help identify context clues and determine the meanings of unknown words.
Ryan wrote in his journal about his day using words from a new language he is learning. The reader is tasked with using context clues from Ryan's writing to determine the meanings of underlined words. These underlined words include "poof-poofs" meaning cereal, "tramzam" meaning school bus, "zilgping" meaning homework, and others. The reader is able to determine the meanings of each underlined word based on how it is used in context.
The document discusses context clues, which are hints in the text surrounding an unfamiliar word that help the reader understand its meaning. It defines different types of context clues like definition, synonyms, restatement, contrast, explanation, examples, and inference. It provides examples of context clues and has exercises for readers to identify unfamiliar words and the context clues that help define them.
The document discusses how context clues can help readers understand unfamiliar words. It defines context as the ideas surrounding a word or situation. Readers can look at context clues like definitions, examples, and related words to infer the meaning of an unknown word. The document provides examples of using context clues to determine the meanings of words like "profusely", "elaborate", and "prehensile". It emphasizes that context clues are important for understanding vocabulary on tests.
This document discusses the different types of context clues that can help readers understand the meaning of unfamiliar words: [1] Definition/explanation clues provide a direct definition within the text; [2] Restatement/synonym clues restate the word's meaning; [3] Contrast/antonym clues contrast an unfamiliar word with a familiar antonym; [4] Inference/general clues allow readers to infer meaning based on the context. The document provides examples of each type of context clue and guidance on using context to determine a word's definition.
The document discusses different types of context clues that can help determine the meaning of unfamiliar words in a text. It provides examples of definition, synonym, restatement, contrast, explanation, and inference context clues. It also notes some limitations of relying solely on context clues to define a word and provides examples of applying different context clue types to define unfamiliar words.
The document discusses different types of context clues that can help readers determine the meaning of unfamiliar words. It identifies four main types of context clues: synonyms, antonyms, explanations, and examples. Readers can use other words or phrases in the same sentence or nearby sentences that are similar or opposite in meaning to the unfamiliar word to infer its definition based on context. The document provides multiple examples for each type of context clue to illustrate how authors provide hints about word meanings.
Industry Ontologies: Case Studies in Creating and Extending Schema.org for In...MakoLab SA
The presentation introduces listeners into the details of the most important global semantic vocabulary build jointly by Google, Yahoo, Microsoft and Yandex: schema.org. It then discusses the experiences related to the creation of “hosted” extensions for the automotive industries (existing: auto.schema.org) and for the financial industries (in making: fibo.schema.org). The two extensions, built by an international team of specialists managed by MakoLab with full respect to the community processes, have two different creation strategies which will be presented and discussed.
The use cases for both vocabularies will be demonstrated. They are related to both “external” business effects (better visibility of the websites using them on the web) and “internal” effects (new kind of analytics and search capacities).
The presentation will also invite to participate to two W3C Community Groups responsible for the open communication activities around the two extensions.
Watch full webinar here: https://bit.ly/3mdj9i7
You will often hear that "data is the new gold"? In this context, data management is one of the areas that has received more attention from the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
In this webinar, we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company.
Watch this on-demand webinar as we cover:
- The most interesting trends in data management
- How to build a data fabric architecture?
- How to manage your data integration strategy in the new hybrid world
- Our predictions on how those trends will change the data management world
- How can companies monetize the data through data-as-a-service infrastructure?
- What is the role of voice computing in future data analytic
The Web Data Commons Microdata, RDFa, and Microformat Dataset Series @ ISWC2014Robert Meusel
The document describes a series of datasets created by parsing HTML pages to extract structured data in the form of Microdata, RDFa, and Microformats. It provides an overview of the datasets created in 2010, 2012, and 2013, which contain over 30 billion RDF quads extracted from over 1.7 million domains. The datasets are hosted online and provide insights into the usage of different vocabularies and markup languages as well as opportunities for applying and analyzing the large-scale structured web data.
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.
Graph Structure in the Web - Revisited. WWW2014 Web Science TrackChris Bizer
The document discusses research that revisits the graph structure of the web using a new large crawl from Common Crawl. It finds that the web has become more dense and connected over time, with the largest strongly connected component growing significantly. While previous research found power laws for in- and out-degrees, this data does not fit power laws and instead has heavy-tailed distributions. The shape of the bow-tie structure also depends on the specific crawl used. The authors provide the new crawl data and analysis to enable further research on the evolving structure of the web graph.
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.
The document describes a method for focused crawling to retrieve structured data from web pages. It involves using an online classifier trained on URL features to identify pages containing structured data. A bandit-based selection strategy is used to balance exploration and exploitation. Experiments show the adaptive approach retrieves 26% more relevant pages than static classification, and 66% more when focused on a specific objective. Decaying the bandit randomness over time improved results further. The method was able to retrieve hundreds of millions of structured data pages from billions of web pages.
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.
This document provides an introduction to REST (Representational State Transfer), including definitions of REST and its key principles and components. It discusses that REST uses HTTP methods to perform CRUD operations on resources, focuses on resources over methods, and is stateless. The four basic design principles of REST are also summarized: use HTTP methods explicitly, be stateless, expose directory structure-like URLs, and transfer XML or JSON.
This document discusses applications of linked data and semantic web technologies. It describes the linked open data cloud and prominent datasets like DBpedia. It provides statistics about the size and connectivity of linked open data. It also discusses ontologies, browsers, and search engines that facilitate working with linked data. Finally, it outlines the components needed to build linked data driven web applications and access linked data through SPARQL endpoints and libraries.
Context clues are hints in a text that help identify the meaning of unknown words. To use context clues, examine the sentence before, the sentence with, and the sentence after the unknown word. There are three steps to determine a word's meaning from context clues: 1) Check for synonyms or definitions in the sentence, 2) Check for contrast clues and consider the opposite meaning, 3) Reread and substitute a fitting word. Examples are provided to demonstrate using context clues to determine the correct word for each sentence.
Cloud Foundry Summit 2015: 10 common errors when pushing apps to cloud foundryJack-Junjie Cai
You may experience some errors when you push your application to CloudFoundry. Some of them are easier to figure out, while others may be mysterious and harder to diagnose. This session will examine 10 common errors that may happen during application push, including their symptom, the tools and techniques to diagnose them, and the possible solutions. The session will mostly focus on Java and node.js applications, but some of the tips applies to all runtimes.
Context clues are facts or ideas in the text that help suggest the meaning of an unknown word. There are different types of context clues including definition, restatement, examples, comparison/contrast, cause and effect, punctuation, and modifiers. Exercises are provided to help identify context clues and determine the meanings of unknown words.
Ryan wrote in his journal about his day using words from a new language he is learning. The reader is tasked with using context clues from Ryan's writing to determine the meanings of underlined words. These underlined words include "poof-poofs" meaning cereal, "tramzam" meaning school bus, "zilgping" meaning homework, and others. The reader is able to determine the meanings of each underlined word based on how it is used in context.
The document discusses context clues, which are hints in the text surrounding an unfamiliar word that help the reader understand its meaning. It defines different types of context clues like definition, synonyms, restatement, contrast, explanation, examples, and inference. It provides examples of context clues and has exercises for readers to identify unfamiliar words and the context clues that help define them.
The document discusses how context clues can help readers understand unfamiliar words. It defines context as the ideas surrounding a word or situation. Readers can look at context clues like definitions, examples, and related words to infer the meaning of an unknown word. The document provides examples of using context clues to determine the meanings of words like "profusely", "elaborate", and "prehensile". It emphasizes that context clues are important for understanding vocabulary on tests.
This document discusses the different types of context clues that can help readers understand the meaning of unfamiliar words: [1] Definition/explanation clues provide a direct definition within the text; [2] Restatement/synonym clues restate the word's meaning; [3] Contrast/antonym clues contrast an unfamiliar word with a familiar antonym; [4] Inference/general clues allow readers to infer meaning based on the context. The document provides examples of each type of context clue and guidance on using context to determine a word's definition.
The document discusses different types of context clues that can help determine the meaning of unfamiliar words in a text. It provides examples of definition, synonym, restatement, contrast, explanation, and inference context clues. It also notes some limitations of relying solely on context clues to define a word and provides examples of applying different context clue types to define unfamiliar words.
The document discusses different types of context clues that can help readers determine the meaning of unfamiliar words. It identifies four main types of context clues: synonyms, antonyms, explanations, and examples. Readers can use other words or phrases in the same sentence or nearby sentences that are similar or opposite in meaning to the unfamiliar word to infer its definition based on context. The document provides multiple examples for each type of context clue to illustrate how authors provide hints about word meanings.
Industry Ontologies: Case Studies in Creating and Extending Schema.org for In...MakoLab SA
The presentation introduces listeners into the details of the most important global semantic vocabulary build jointly by Google, Yahoo, Microsoft and Yandex: schema.org. It then discusses the experiences related to the creation of “hosted” extensions for the automotive industries (existing: auto.schema.org) and for the financial industries (in making: fibo.schema.org). The two extensions, built by an international team of specialists managed by MakoLab with full respect to the community processes, have two different creation strategies which will be presented and discussed.
The use cases for both vocabularies will be demonstrated. They are related to both “external” business effects (better visibility of the websites using them on the web) and “internal” effects (new kind of analytics and search capacities).
The presentation will also invite to participate to two W3C Community Groups responsible for the open communication activities around the two extensions.
Watch full webinar here: https://bit.ly/3mdj9i7
You will often hear that "data is the new gold"? In this context, data management is one of the areas that has received more attention from the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
In this webinar, we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company.
Watch this on-demand webinar as we cover:
- The most interesting trends in data management
- How to build a data fabric architecture?
- How to manage your data integration strategy in the new hybrid world
- Our predictions on how those trends will change the data management world
- How can companies monetize the data through data-as-a-service infrastructure?
- What is the role of voice computing in future data analytic
The Web Data Commons Microdata, RDFa, and Microformat Dataset Series @ ISWC2014Robert Meusel
The document describes a series of datasets created by parsing HTML pages to extract structured data in the form of Microdata, RDFa, and Microformats. It provides an overview of the datasets created in 2010, 2012, and 2013, which contain over 30 billion RDF quads extracted from over 1.7 million domains. The datasets are hosted online and provide insights into the usage of different vocabularies and markup languages as well as opportunities for applying and analyzing the large-scale structured web data.
Crossing the Analytics Chasm and Getting the Models You Developed DeployedRobert Grossman
There are two cultures in data science and analytics - those that develop analytic models and those that deploy analytic models into operational systems. In this talk, we review the life cycle of analytic models and provide an overview of some of the approaches that have been developed for managing analytic models and workflows and for deploying them, including using analytic engines and analytic containers . We give a quick overview of languages for analytic models (PMML) and analytic workflows (PFA). We also describe the emerging discipline of AnalyticOps that has borrowed some of the techniques of DevOps.
L'architettura di classe enterprise di nuova generazione - Massimo BrignoliData Driven Innovation
La nascita dei data lake - La aziende, ormai, sono sommerse dai dati e il classico datawarehouse fa fatica a macinare questi dati per numerosità e varietà. In molti hanno iniziato a guardare a delle architetture chiamate Data Lakes con Hadoop come tecnologia di riferimento. Ma questa soluzione va bene per tutto? Vieni a capire come operazionalizzare i data lakes per creare delle moderne architetture di gestione dati.
A possible future role of schema.org for business reportingsopekmir
The presentation demonstrates a vision for the “reporting extension” that could enhance the processes related to business reporting and the role it could have for the SBR vision.
20131111 - Santa Monica - BigDataCamp - Big Data Design PatternsAllen Day, PhD
This document discusses design patterns for big data applications. It begins by defining what a design pattern is, then provides examples of patterns for different types of data volumes and query speeds. Common patterns like percolation and recommendation systems are explained. The document also discusses how to analyze big data applications to determine which patterns may apply. Specific examples like personalized search, medicine, and market segmentation are used to illustrate how patterns can be implemented. The key lessons are to take a high-level view of recurring problems and design reusable pattern-based solutions.
GoodRelations & RDFa for Deep Comparison Shopping on a Web ScaleMartin Hepp
GoodRelations & RDFa for Deep Comparison Shopping on a Web Scale: Can the Web of Data Reduce Price Competition and Increase Customer Satisfaction?
See http://purl.org/goodrelations/ for the official page.
These are my slides from the Zurich and Chicago Semantic Web Meet-up presentation.
USING FACTORY DESIGN PATTERNS IN MAP REDUCE DESIGN FOR BIG DATA ANALYTICSHCL Technologies
Though insights from Big Data gives a breakthrough to make better business decision, it poses its own set of challenges. This paper addresses the gap of Variety problem and suggest a way to seamlessly handle data processing even if there is change in data type/processing algorithm. It explores the various map reduce design patterns and comes out with a unified working solution (library). The library has the potential to ‘adapt’ itself to any data processing need which can be achieved by Map Reduce saving lot of man hours and enforce good practices in code.
Insights into Real World Data Management ChallengesDataWorks Summit
Data is your most valuable business asset and it's also your biggest challenge. This challenge and opportunity means we continually face significant road blocks toward becoming a data driven organisation. From the management of data, to the bubbling open source frameworks, the limited industry skills to surmounting time and cost pressures, our challenge in data is big.
We all want and need a “fit for purpose” approach to management of data, especially Big Data, and overcoming the ongoing challenges around the ‘3Vs’ means we get to focus on the most important V - ‘Value’.Come along and join the discussion on how Oracle Big Data Cloud provides Value in the management of data and supports your move toward becoming a data driven organisation.
Speaker
Noble Raveendran, Principal Consultant, Oracle
Five Critical Success Factors for Big Data and Traditional BIInside Analysis
VelociData offers big data operations appliances that combine FPGAs, GPUs, and CPUs to enable high-speed parallel data processing. This allows VelociData to accelerate common data transformation and quality tasks by several orders of magnitude compared to conventional approaches. Examples included accelerating lookups from 3000 to 600,000 records per second. VelociData appliances can offload bottlenecks from ETL servers, mainframes, Hadoop, and data warehouses to improve overall performance. The presentation demonstrated how VelociData solutions provide 100x or greater acceleration through massively parallel hardware architectures.
Oracle BI Hybrid BI : Mode 1 + Mode 2, Cloud + On-Premise Business AnalyticsMark Rittman
Mark Rittman, founder of Rittman Mead, discusses Oracle's approach to hybrid BI deployments and how it aligns with Gartner's vision of a modern BI platform. He explains how Oracle BI 12c supports both traditional top-down modeling and bottom-up data discovery. It also enables deploying components on-premises or in the cloud for flexibility. Rittman believes the future is bi-modal, with IT enabling self-service analytics alongside centralized governance.
XML Publisher (www.aboutoracleapps.com)Chris Martin
This document provides an overview of Oracle XML Publisher and how to set it up and use it. It discusses the issues with classic reporting tools and how XML Publisher separates data, layout, and translation for more flexibility. It then covers how to set up the environment, generate XML output, design and map templates, register definitions and templates, and run reports. The goal is to demonstrate how to quickly generate reports from XML data using XML Publisher.
Watch here: https://bit.ly/3i2iJbu
You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
Join us for an exciting session that will cover:
- The most interesting trends in data management.
- Our predictions on how those trends will change the data management world.
- How these trends are shaping the future of data virtualization and our own software.
L’architettura di Classe Enterprise di Nuova GenerazioneMongoDB
This document discusses using MongoDB as part of an enterprise data management architecture. It begins by describing the rise of data lakes to manage growing and diverse data volumes. Traditional EDWs struggle with this new data variety and volume. The document then provides an overview of MongoDB's features like flexible schemas, secondary indexes, and aggregation capabilities that make it suitable for building different layers of an EDM pipeline for tasks like raw data storage, transformation, analysis, and serving data to downstream systems. Example use cases are presented for building a single customer view and for replacing Oracle with MongoDB.
Webinar: Enterprise Data Management in the Era of MongoDB and Data LakesMongoDB
1. The document discusses using MongoDB and data lakes for enterprise data management. It outlines the current issues with relational databases and how MongoDB addresses challenges like flexibility, scalability and performance.
2. Various architectures for enterprise data management with MongoDB are presented, including using it for raw, transformed and aggregated data stores.
3. The benefits of combining MongoDB and Hadoop in a data lake are greater agility, insight from handling different data structures, scalability and low latency for real-time decisions.
What is the current status quo of the Semantic Web as first mentioned by Tim Berners Lee in 2001?
Not only 10 blue links can drive you traffic anymore, Google has added many so called Knowlegde cards and panels to answer the specific informational need of their users. Sounds complicated, but it isn’t. If you ask for information, Google will try to answer it within the result pages.
I'll share my research from a theoretical point of view through exploring patents and papers, and actual testing cases in the live indices of Google. Getting your site listed as the source of an Answer Card can result in an increase of CTR as much as 16%. How to get listed? Come join my session and I'll shine some light on the factors that come into play when optimizing for Google's Knowledge graph.
Similar to Heuristics for Fixing Common Errors in Deployed schema.org Microdata (20)
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
3. 3
Microdata in a Nutshell
Adding structured information to web pages
• By marking up contents and entities
Arbitrary vocabularies are possible
• Practically, only schema.org is deployed on a large scale
• Plus its historical predecessor: data-vocabulary.org
Similar to RDFa
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
<div itemscope itemtype="http://schema.org/PostalAddress">
<span itemprop="name">Data and Web Science Group</span>
<span itemprop="addressLocality">Mannheim</span>,
<span itemprop="postalCode">68131</span>
<span itemprop="addressCountry">Germany</span>
</div>
4. 4
Schema.org in a Nutshell
Vocabulary for marking up entities on web pages
• 675 classes and 965 properties (as of May 2015, release 2.0)
Promoted and consumes by major search engine companies
• Google, Bing, Yahoo!, and Yandex
• Google Rich Snippets
Community-driven evolution and development
Can be used with Microdata and RDFa
• Hardly used together with RDFa (<0.1% of RDFa-using websites [1])
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
[1] http://webdatacommons.org/structureddata/2014-12/stats/stats.html
5. 5
Schema.org in a Nutshell – Coverage
Schema.org has incorporated some popular vocabularies, like:
• Good Relations (2012)
• W3C BibExtend (2014)
• MusicBrainz vocabulary (2015)
• Automotive Ontology (2015)
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
6. 6
Microdata with Schema.org in HTML Pages
<html>
…
<body>
…
<div id="main-section" class="performance left" data-
sku="M17242_580“>
<h1> Predator Instinct FG Fußballschuh
</h1>
<div>
<meta content="EUR">
<span
data-sale-price="219.95">219,95</span>
…
</body>
</html>
HTML pages embed directly
markup languages to annotate
items using different vocabularies
<html>
…
<body>
…
<div id="main-section" class="performance left" data-
sku="M17242_580" itemscope
itemtype="http://schema.org/Product">
<h1 itemprop="name"> Predator Instinct FG Fußballschuh
</h1>
<div itemscope itemtype="http://schema.org/Offer"
itemprop="offers">
<meta itemprop="priceCurrency" content="EUR">
<span itemprop="price" data-sale-
price="219.95">219,95</span>
…
</body>
</html>
1._:node1 <http://www.w3.org/1999/02/22-rdf-syntax-
ns#type> <http://schema.org/Product> .
2._:node1 <http://schema.org/Product/name> "Predator
Instinct FG Fußballschuh"@de .
3._:node1 <http://www.w3.org/1999/02/22-rdf-syntax-
ns#type> <http://schema.org/Offer> .
4._:node1 <http://schema.org/Offer/price>
"219,95"@de .
5._:node1 <http://schema.org/Offer/priceCurrency>
"EUR" .
6.…
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
7. 7
So Far, So Good …
Schema is well explained on the schema.org websites
Data providers are supported by validation tools
(e.g. Yandex structured data validator) when deploying
Win-Win for both sides
Plus: Data is (mostly) free accessible in the Web
…. but:
>100.000s of data providers, which are mostly no schema.org
experts or evangelists
Validators & schema might help but there is no need to use
them
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
8. 8
So What Could Possibly Go Wrong?
Usage of wrong namespaces
• http./schema.org
Usage of undefined types
• http://schema.org/Breadcrumb
Usage of undefined properties
• http://schema.org/postID
Confusion of datatype properties and object properties
• _:n1 s:address “Jump Street 21”
Property domain and range violations
• _:n1 a s:Product
_:n1 s:price “for free”
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
9. 9
Compiling a Schema.org Dataset
Starting point: all pages in the CommonCrawl that contain
Microdata
What could be (meant to be) schema.org?
• Everything that contains “schema.org” as substring in a namespace
• Everything that contains URIs where the protocol and authority is similar
to “http://schema.org/” (with an EditDistance of 1)
• Filter noise: removing all namespaces that occur only on one website
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
Final corpus consists of:
6.4 billion triples
extracted from over 217 billion pages
belonging to 398,542 data providers
which is 86% of all Microdata in the corpus.
10. 10
Namespace Violations
More than 98% of the preselected pages use a correct
namespace
Frequent namespace variations:
• http://www.schema.org/
• https://schema.org
• http:/schema.org
• http://SChema.org
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
Debated!
11. 11
Undefined Types
Used by around 6% of all data providers
Typical causes:
• Misspellings: http://schema.org/Stores
• Miscapitalization: http://schema.org/localbusiness
Comparison to LOD Compliance
• 5.8% of all Microdata documents
• 38.8% of all LOD documents (Hogan et al., 2010)
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
…/Store
…/LocalBusiness
12. 12
Undefined Properties
Used by around 4% of all data providers
Typical Causes:
• Miscapitalization: http://schema.org/contentURL
• Close but miss: http://schema.org/currency
http://schema.org/fax
• Made up: http://schema.org/blogId
http://schema.org/postId
Comparison to LOD Compliance
• 9.7% of all Microdata documents
• 72.4% of all LOD documents (Hogan et al., 2010)
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
…/contentUrl
…/priceCurrency
13. 13
Confusion of Object Properties with Data Properties
i.e. using an object property with a string values
Used by over 56.6% of all data providers
Typical properties:
• http://schema.org/addresscountry
• http://schema.org/manufacturer
• http://schema.org/author
• http://schema.org/brand
Comparison to LOD Compliance
• 24.35% of all Microdata documents
• 8% of all LOD documents (Hogan et al., 2010)
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
14. 14
Confusion of Data Properties with Object Properties
i.e. using a data property with a complex object
Used by less than 0.2% of all data providers
Comparison to LOD Compliance
• 0.6% of all Microdata documents
• 2.2% of all LOD documents (Hogan et al., 2010)
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
15. 15
Property Domain Violations
i.e. using a property with a subject not included in its domain
Used by 4% of all data providers
Typical violations are mainly shortcuts
• s:price used on s:Product
• s:streetAddress used on s:LocalBusiness
Comparison to LOD Compliance:
• Difficult to compare as semantics are different
• List of schema.org domains is exhaustive
• LOD: open world assumption
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
s:Product s:Offer s:price
s:LocalBusiness s:PostalAddress
s:streetAddress
16. 16
Data Property Range Violations
i.e. using a data property with an incompatible literal
Used by 9.6% of all data providers
20 most common violations:
• 13 dates
• 3 Urls
• 2 numbers
• 2 times
Comparison to LOD Compliance:
• 12.06% of all Microdata documents
• 4.6% of all LOD documents (Hogan et al., 2010)
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
“a month ago”
“2 pieces”
“last week”
17. 17
Object Property Range Violations
i.e. using an object property with a type outside its range
Used by 8.6% of all data providers
Typical violations:
• s:mainContentOfPage with s:Blog instead of
s:WebPageElement
Comparison to LOD Compliance
• 3.2% of all Microdata documents
• 2.4% of all LOD documents (Hogan et al., 2010)
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
Maybe a hint at a missing
hierarchy relation?
18. 18
Schema.org Compliance Summary
Surprisingly high level of compliance
Providers are often not technology evangelists (unlike in LOD)
• Anybody can start publishing Microdata annotated HTML
Most often higher than for LOD
• Except for the confusion of data and object properties
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
But still the number of erroneous pages could prevent data
consumers to make use of the annotated data and understand
the semantics.
19. 19
Identifying and Fixing Wrong Namespaces
Main errors due to missing slashes, wrong protocol and
capitalization
Simple rules to handle wrong namespaces
• Removal of www
• Replacement of https by http
• Conversion to lower case
• Adding of missing slashes and removal of prefixes before schema.org
Impact:
• 147 of 148 wrongly spelled namespaces could be fixed
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
20. 20
Handling Undefined Types and Properties
Main errors due to wrong capitalization
Heuristic: Ignore capitalization when parsing entities from web
pages, and replace the schema element with the properly
capitalized version
Impact (together with namespace fixes):
• Correct type replacement within 71% of all data providers
• Correct property replacement within 65% of all data providers
• Remaining data providers account for over 70% of all
undefined types and properties and
are hard-to-detect typos
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
21. 21
Handling Object Properties with Literal Values
Main objects modeled as literals are s:Organization,
s:Person and s:PostalAddress
Manually inspecting those values for the object
properties s:author, s:creator and s:address
Impact
• The heuristic could replace all misused
object properties on 92,449 data providers
• Might lead to changes in the type distribution
• E.g. 14 million new entities of type
s:PostalAddress
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
_:1 s:author “Robert” .
_:1 s:author _:2 .
_:2 a s:Person .
_:2 s:name “Robert” .
22. 22
Handling Property Domain Violations
Main cause are shortcuts
Heuristic to find the
property R and type T
for a domain violation
of property s:r:
One unique solution for only one of
the two patterns:
Impact:
• 31% of erroneous data providers could be fixed
• No solution or multiple solutions for the rest
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
_:1 “5”
s:aggregatedRating
s:aggregatedRating
is not defined for
type of _:1
_:2
s:aggregatedRating
Type?
Property?
R s:domainIncludes s:t .
R s:rangeIncludes T .
s:r s:domainIncludes T .
R s:rangeIncludes s:t .
R s:domainIncludes T .
s:r s:domainIncludes T .
23. 23
Heuristics Summary
Over 410 million wrong triples could be corrected
Over 700 million missing triples could be added
Corrections affected in total over 115.000 data providers
• ~ 28% of all data providers in the data set
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
24. 24
LD4IE Challenge @ ISWC 2015
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
Learn to annotate entities on HTML pages using already
annotated pages as training set.
Deadline: 2015-07-15
Challenge Page: goo.gl/laF6yl
Contact: Heiko Paulheim
(heiko@dwslab.de)
Good Luck!
25. 25
Thank you! Questions? Feedback?
Data and more insights can be found at:
http://webdatacommons.org/structureddata/2013-
11/stats/fixing_common_errors.html
More interesting datasets and analysis can be found at the
website of WebDataCommons:
http://webdatacommons.org/index.html
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015
Acknowledgement
The extraction and analysis of the datasets was supported
by AWS in Education Grant.