http://iswc2016.semanticweb.org/pages/program/keynote-bizer.html
Semantic Web technologies, such as Linked Data and Schema.org, are used by a significant number of websites to support the automated processing of their content. In the talk, I will contrast the original vision of the Semantic Web with empirical findings about the adoption of Semantic Web technologies on the Web. The analysis will show areas in which data providers behave as envisioned by the Semantic Web community but will also reveal areas in which real-world adoption patterns strongly deviate. Afterwards, I will discuss the challenges that result from the current adoption situation. To address these challenges, I will exemplify entity reconciliation, vocabulary matching, and data quality assessment techniques which exploit all semantic clues that are provided while being tolerant to noise and lazy data providers.
Algumas das principais características do NoSQLEric Silva
Este trabalho tem como objetivo apresentar
algumas das principais características do NoSQL,
um banco de dados que possui como diferencial um
grande poder de escalabilidade, proporcionando
uma maior capacidade de armazenamento e
velocidade.
MongoDB Atlas makes it easy to set up, operate, and scale your MongoDB deployments in the cloud. From high availability to scalability, security to disaster recovery - we've got you covered.
Automated: With MongoDB Atlas, you no longer need to worry about operational tasks such as provisioning, configuration, patching, upgrades, backups, and failure recovery. MongoDB Atlas provides the functionality and reliability you need, at the click of a button.
Flexible: Only MongoDB Atlas combines the critical capabilities of relational databases with the innovations of NoSQL. Radically simplify development and operations by delivering a diverse range of capabilities in a single, managed database platform.
Secure: MongoDB Atlas provides multiple levels of security for your database. These include robust access control, network isolation using Amazon VPC, IP whitelists, encryption of data in-flight using TLS/SSL, and optional encryption of the underlying filesystem.
Scalable: MongoDB Atlas grows with you, all with the click of a button. You can scale up across a range of instance sizes, and scale-out with automatic sharding. And you can do it with zero application downtime.
Highly Available: MongoDB Atlas is designed to offer exceptional uptime. Recovery from instance failures is transparent and fully automated. A minimum of three copies of your data are replicated across availability zones and continuously backed up.
High Performance: MongoDB Atlas provides high throughput and low latency for the most demanding workloads. Consistent, predictable performance eliminates the need for separate caching tiers, and delivers a far better price-performance ratio compared to traditional database software.
Collibra Data Citizen '19 - Bridging Data Privacy with Data Governance BigID Inc
This presentation was shown at the 2019 Collibra Data Citizen Event in New York City.
Presented by Nimrod Vax, Chief Product Officer & Co-Founder & Joaquin Sufuentes, Lead Architect, Metadata Managment and Personal Infomation Protection, enterprise Data Managment, Intel IT
Using AWS WAF to protect against bots and scrapers - SDD311 - AWS re:Inforce ...Amazon Web Services
"In this workshop, you learn how to deploy AWS WAF in front of your application, how to set up AWS WAF full logging for compliance and monitoring purposes, and how to increase your security posture by creating custom rules using Amazon Elasticsearch Service with Kibana. You also learn how to protect your application against bad bots, web scrapers, and scanners by configuring bad and benign bot signatures and then automating your AWS WAF rules by parsing AWS WAF full logs using an AWS Lambda function.
All attendees need a laptop, an active AWS Account, an AWS IAM Administrator, and a familiarity with core AWS services."
Algumas das principais características do NoSQLEric Silva
Este trabalho tem como objetivo apresentar
algumas das principais características do NoSQL,
um banco de dados que possui como diferencial um
grande poder de escalabilidade, proporcionando
uma maior capacidade de armazenamento e
velocidade.
MongoDB Atlas makes it easy to set up, operate, and scale your MongoDB deployments in the cloud. From high availability to scalability, security to disaster recovery - we've got you covered.
Automated: With MongoDB Atlas, you no longer need to worry about operational tasks such as provisioning, configuration, patching, upgrades, backups, and failure recovery. MongoDB Atlas provides the functionality and reliability you need, at the click of a button.
Flexible: Only MongoDB Atlas combines the critical capabilities of relational databases with the innovations of NoSQL. Radically simplify development and operations by delivering a diverse range of capabilities in a single, managed database platform.
Secure: MongoDB Atlas provides multiple levels of security for your database. These include robust access control, network isolation using Amazon VPC, IP whitelists, encryption of data in-flight using TLS/SSL, and optional encryption of the underlying filesystem.
Scalable: MongoDB Atlas grows with you, all with the click of a button. You can scale up across a range of instance sizes, and scale-out with automatic sharding. And you can do it with zero application downtime.
Highly Available: MongoDB Atlas is designed to offer exceptional uptime. Recovery from instance failures is transparent and fully automated. A minimum of three copies of your data are replicated across availability zones and continuously backed up.
High Performance: MongoDB Atlas provides high throughput and low latency for the most demanding workloads. Consistent, predictable performance eliminates the need for separate caching tiers, and delivers a far better price-performance ratio compared to traditional database software.
Collibra Data Citizen '19 - Bridging Data Privacy with Data Governance BigID Inc
This presentation was shown at the 2019 Collibra Data Citizen Event in New York City.
Presented by Nimrod Vax, Chief Product Officer & Co-Founder & Joaquin Sufuentes, Lead Architect, Metadata Managment and Personal Infomation Protection, enterprise Data Managment, Intel IT
Using AWS WAF to protect against bots and scrapers - SDD311 - AWS re:Inforce ...Amazon Web Services
"In this workshop, you learn how to deploy AWS WAF in front of your application, how to set up AWS WAF full logging for compliance and monitoring purposes, and how to increase your security posture by creating custom rules using Amazon Elasticsearch Service with Kibana. You also learn how to protect your application against bad bots, web scrapers, and scanners by configuring bad and benign bot signatures and then automating your AWS WAF rules by parsing AWS WAF full logs using an AWS Lambda function.
All attendees need a laptop, an active AWS Account, an AWS IAM Administrator, and a familiarity with core AWS services."
엔터프라이즈의 인공지능(AI)과 머신러닝(ML) 적용은 왜 어려울까요?
베스핀글로벌의 웨비나 자료를 통해서 성공적인 AI와 ML 적용 방법을 확인하세요.
[목차]
1. 디지털 트랜스포메이션의 큰 흐름
- Gartner 선정 미래를 이끌어 갈 기업
- 글로벌 금융 기업의 디지털 트랜스포메이션, 데이터를 바라보는 시각
- 빅데이터 & AI 활용 사례
2. 빅데이터 분석 시스템 도입하기
- 빅데이터 분석 시스템 미도입 이유
- 빅데이터 분석 시스템 도입 사례
3. 데이터 분석을 위한 Data Lake & Data Governance
- 데이터 분석의 한계와 Data Lake
- 클라우드 Migration
- Data Governance의 중요성
4. AI 적용하기
- Amazon AI 서비스
- 적용 사례
Big Data Analytics Architectural Patterns and Best Practices (ANT201-R1) - AW...Amazon Web Services
In this session, we discuss architectural principles that helps simplify big data analytics.
We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll disucss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on.
Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
[Bespin Global 파트너 세션] 분산 데이터 통합 (Data Lake) 기반의 데이터 분석 환경 구축 사례 - 베스핀 글로벌 장익...Amazon Web Services Korea
기업 환경에 따라 차이는 있겠지만, 최근 대부분의 기업은 데이터 분석 환경이 구축되어 있고, 이를 기반으로 데이터를 분석하고 있습니다. 그럼에도 불구하고 현업에서는 분석하고자 하는 데이터가 없거나 변화하는 비즈니스 요건을 반영하지 못한다는 불만을 제기하고, 분석 환경을 제공하는 IT운영팀은 변화하는 비즈니스 요건에 따라 분석 환경을 적시에 제공하기 쉽지 않다는 어려움을 토로하고 있습니다. 이 해결책으로 운영시스템에 데이터베이스 형태로 존재하고 있거나, 현업의 PC에서 수작업으로 작성한 정형, 비정형 파일을 통합 관리할 수 있고, 또한 인프라 환경의 확장 및 변경을 보다 유연하게 할 수 있는 AWS Cloud 기반의 분석 환경 구축 사례를 소개하고자 합니다.
다시보기 링크: https://youtu.be/YvYfNZHMJkI
As organizations pursue Big Data initiatives to capture new opportunities for data-driven insights, data governance has become table stakes both from the perspective of external regulatory compliance as well as business value extraction internally within an enterprise. This session will introduce Apache Atlas, a project that was incubated by Hortonworks along with a group of industry leaders across several verticals including financial services, healthcare, pharma, oil and gas, retail and insurance to help address data governance and metadata needs with an open extensible platform governed under the aegis of Apache Software Foundation. Apache Atlas empowers organizations to harvest metadata across the data ecosystem, govern and curate data lakes by applying consistent data classification with a centralized metadata catalog.
In this talk, we will present the underpinnings of the architecture of Apache Atlas and conclude with a tour of governance capabilities within Apache Atlas as we showcase various features for open metadata modeling, data classification, visualizing cross-component lineage and impact. We will also demo how Apache Atlas delivers a complete view of data movement across several analytic engines such as Apache Hive, Apache Storm, Apache Kafka and capabilities to effectively classify, discover datasets.
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT), APIs, clickstreams, unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. Additionally, Merck will share how they built an end-to-end ETL pipeline for their application release management system, and launched it in production in less than a week using AWS Glue.
(BDT208) A Technical Introduction to Amazon Elastic MapReduceAmazon Web Services
"Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. Additionally, you hear from AOL’s Senior Software Engineer on how they used these strategies to migrate their Hadoop workloads to the AWS cloud and lessons learned along the way.
In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; dDeployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively."
Building with AWS Databases: Match Your Workload to the Right Database (DAT30...Amazon Web Services
We have recently seen some convergence of different database technologies. Many customers are evaluating heterogeneous migrations as their database needs have evolved or changed. Evaluating the best database to use for a job isn't as clear as it was ten years ago. We'll discuss the ideal use cases for relational and nonrelational data services, including Amazon ElastiCache for Redis, Amazon DynamoDB, Amazon Aurora, Amazon Neptune, and Amazon Redshift. This session digs into how to evaluate a new workload for the best managed database option. Please join us for a speaker meet-and-greet following this session at the Speaker Lounge (ARIA East, Level 1, Willow Lounge). The meet-and-greet starts 15 minutes after the session and runs for half an hour.
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.
엔터프라이즈의 인공지능(AI)과 머신러닝(ML) 적용은 왜 어려울까요?
베스핀글로벌의 웨비나 자료를 통해서 성공적인 AI와 ML 적용 방법을 확인하세요.
[목차]
1. 디지털 트랜스포메이션의 큰 흐름
- Gartner 선정 미래를 이끌어 갈 기업
- 글로벌 금융 기업의 디지털 트랜스포메이션, 데이터를 바라보는 시각
- 빅데이터 & AI 활용 사례
2. 빅데이터 분석 시스템 도입하기
- 빅데이터 분석 시스템 미도입 이유
- 빅데이터 분석 시스템 도입 사례
3. 데이터 분석을 위한 Data Lake & Data Governance
- 데이터 분석의 한계와 Data Lake
- 클라우드 Migration
- Data Governance의 중요성
4. AI 적용하기
- Amazon AI 서비스
- 적용 사례
Big Data Analytics Architectural Patterns and Best Practices (ANT201-R1) - AW...Amazon Web Services
In this session, we discuss architectural principles that helps simplify big data analytics.
We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll disucss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on.
Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
[Bespin Global 파트너 세션] 분산 데이터 통합 (Data Lake) 기반의 데이터 분석 환경 구축 사례 - 베스핀 글로벌 장익...Amazon Web Services Korea
기업 환경에 따라 차이는 있겠지만, 최근 대부분의 기업은 데이터 분석 환경이 구축되어 있고, 이를 기반으로 데이터를 분석하고 있습니다. 그럼에도 불구하고 현업에서는 분석하고자 하는 데이터가 없거나 변화하는 비즈니스 요건을 반영하지 못한다는 불만을 제기하고, 분석 환경을 제공하는 IT운영팀은 변화하는 비즈니스 요건에 따라 분석 환경을 적시에 제공하기 쉽지 않다는 어려움을 토로하고 있습니다. 이 해결책으로 운영시스템에 데이터베이스 형태로 존재하고 있거나, 현업의 PC에서 수작업으로 작성한 정형, 비정형 파일을 통합 관리할 수 있고, 또한 인프라 환경의 확장 및 변경을 보다 유연하게 할 수 있는 AWS Cloud 기반의 분석 환경 구축 사례를 소개하고자 합니다.
다시보기 링크: https://youtu.be/YvYfNZHMJkI
As organizations pursue Big Data initiatives to capture new opportunities for data-driven insights, data governance has become table stakes both from the perspective of external regulatory compliance as well as business value extraction internally within an enterprise. This session will introduce Apache Atlas, a project that was incubated by Hortonworks along with a group of industry leaders across several verticals including financial services, healthcare, pharma, oil and gas, retail and insurance to help address data governance and metadata needs with an open extensible platform governed under the aegis of Apache Software Foundation. Apache Atlas empowers organizations to harvest metadata across the data ecosystem, govern and curate data lakes by applying consistent data classification with a centralized metadata catalog.
In this talk, we will present the underpinnings of the architecture of Apache Atlas and conclude with a tour of governance capabilities within Apache Atlas as we showcase various features for open metadata modeling, data classification, visualizing cross-component lineage and impact. We will also demo how Apache Atlas delivers a complete view of data movement across several analytic engines such as Apache Hive, Apache Storm, Apache Kafka and capabilities to effectively classify, discover datasets.
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT), APIs, clickstreams, unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. Additionally, Merck will share how they built an end-to-end ETL pipeline for their application release management system, and launched it in production in less than a week using AWS Glue.
(BDT208) A Technical Introduction to Amazon Elastic MapReduceAmazon Web Services
"Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. Additionally, you hear from AOL’s Senior Software Engineer on how they used these strategies to migrate their Hadoop workloads to the AWS cloud and lessons learned along the way.
In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; dDeployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively."
Building with AWS Databases: Match Your Workload to the Right Database (DAT30...Amazon Web Services
We have recently seen some convergence of different database technologies. Many customers are evaluating heterogeneous migrations as their database needs have evolved or changed. Evaluating the best database to use for a job isn't as clear as it was ten years ago. We'll discuss the ideal use cases for relational and nonrelational data services, including Amazon ElastiCache for Redis, Amazon DynamoDB, Amazon Aurora, Amazon Neptune, and Amazon Redshift. This session digs into how to evaluate a new workload for the best managed database option. Please join us for a speaker meet-and-greet following this session at the Speaker Lounge (ARIA East, Level 1, Willow Lounge). The meet-and-greet starts 15 minutes after the session and runs for half an hour.
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.
SAP Technology Services Conference 2013: Big Data and The Cloud at Yahoo! Sumeet Singh
The Hadoop project is an integral part of Yahoo!'s cloud infrastructure and is at the heart of many of Yahoo!'s important business processes. Sumeet Singh, the Head of Products for Cloud Services and Hadoop at Yahoo!, explains how Yahoo! leverages Hadoop and Cloud Platforms to process and serve Internet- scale data.
Yahoo! operates one of the world's largest private cloud infrastructures. Learn how technologies scale out for building enterprise-wide trusted platforms with tight SLAs.
URL: http://www.saptechnologyservice.com/track1.html
The attention economy and the internetRoss Garrett
Today we’re going to take a look at how traditional or even de facto standards for integration aren’t always the best choice for web mobile and IoT applications. The standard I’m talking about is of course HTTP and ever popular REST APIs.
While I won’t be so bold as to disregard this integration pattern entirely, I do want us to take a critical look at how and where integration can be improved – by understanding the limitations of today’s app integration technologies and considering the business factors that impact success in the attention economy.
Schema.org Annotations and Web Tables: Underexploited Semantic Nuggets on the...Chris Bizer
Slideset of the keynote talk given by Christian Bizer at the Language Data and Knowledge (LDK 2019) conference in Leipzig, Germany
Abstract of the talk:
Millions of websites have started to annotate data describing products, local business, events, jobs, places, recipes, and reviews within their HTML pages using the schema.org vocabulary. These annotations are widely used by search engines to render rich snippets within search results. Surprisingly, the annotations are hardly used by the research community. In the talk, Christian Bizer investigates the potential of schema.org annotations for being used as training data for tasks such as entity matching, information extraction, and sentiment analysis. Web pages that offer semantic annotations often also contain additional structured data in the form of HTML tables. In the second part of the talk, Christian Bizer discusses the interplay of semantic annotations and web tables for information extraction as well as the general potential of relational HTML tables for complementing knowledge bases such as DBpedia, focusing on the discovery of formerly unknown long tail entities as well as the extraction of n-ary relations.
Link to the conference website:
http://2019.ldk-conf.org/invited-speakers/
No Code Platforms How Can They Address Supply Chain Challenges.pptxArpitGautam20
Here are a few interesting ways through which intuitive No Code Platforms can help minimize existing Supply Chain Challenges across the World. https://natifi.ai/no-code-platforms-how-can-they-address-supply-chain-challenges/
Evolution Towards Web 3.0: The Semantic WebLeeFeigenbaum
This was a lecture I presented at Professor Stuart Madnick's class, "Evolution Towards Web 3.0" at the MIT Sloan School of Management on April 21, 2011. Please follow along with the speaker notes which add significant commentary to the slides.
TFF2015, Christian Bizer, Uni Mannheim "Schema.org-Annotationen in Webseiten"TourismFastForward
Professor Christian Bizer erforscht technische und empirische Fragen bezüglich der Entwicklung des World Wide Webs hin zu einem globalen Datenraum. Im Rahmen des WebDataCommons-Projekts untersucht er die globale Adaption von Webseiten-Annotations-Standards wie Schema.org. Im Rahmen des DBpedia-Projekts beschäftigt er sich mit der Ableitung umfassender Multi-Domänen-Wissensbasen aus Wikipedia und dem World Wide Web.
GPT4 versus BERT: Which Foundation Model is better for Web Data Integration?Chris Bizer
The Web contains vast amounts of structured data in the form of HTML tables, schema.org annotations, as well as datasets accessible via data repositories. The automated integration of data from larger numbers of Web data sources is a long-standing research challenge as the integration requires dealing with several tricky tasks such as schema matching, entity matching, and data indexing for retrieval. Most state-of-the-art methods for these tasks rely on variants of the BERT transformer model fine-tuned using significant amounts of task-specific training data. In the talk, Christian Bizer will critically review BERT-based data integration methods and question their robustness concerning out-of-distribution entities. He will compare the performance of BERT-based methods with results of GPT-4-based data integration methods and will argue that GPT-4-based methods are more training data efficient and more robust concerning unseen entities.
Integrating Product Data from the Semantic Web using Deep Learning TechniquesChris Bizer
The adoption of schema.org annotations on the Web has sharply increased over the last years with hundreds of thousands of websites annotating information about products, events, local businesses, reviews, and job postings within their pages. In the talk, Christian Bizer will discuss the integration of schema.org product data from large numbers of websites for the use cases of building product knowledge graphs as well as comparing product prizes across e-shops. The key challenge for this integration is to determine which webpages describe the same product. Christian Bizer will demonstrate how this challenge can be handled by deriving a large pool of training data from schema.org annotations and using this data to train transformer-based product matchers. He will discuss how the matchers exploit the richness of the training data available for widely sold head products using multi-task learning but can also excel on matching long-tail products using contrastive pre-training as well as cross-language learning.
Using the Semantic Web as Training Data for Product MatchingChris Bizer
Talk at the OpenKG Forum co-located with JIST2019 about using schema.org annotations from the Web for training product matchers.
See also:
http://webdatacommons.org/largescaleproductcorpus/v2/
http://jist2019.openkg.cn/index.php/openkgasia-forum/
JIST2019 Keynote: Completing Knowledge Graphs using Data from the Open WebChris Bizer
Current research on knowledge graph completion focuses on employing graph embeddings for the task of link prediction. But knowledge graph completion is more than link prediction and tasks such as adding formerly unknown long-tail entities to the graph, extending the schema of the graph with additional properties, and completing and updating numeric values are equally important tasks. In the talk, Christian Bizer will review recent results on using data from large numbers of independent websites to accomplish these tasks. He will focus on two types of web content – relational HTML tables and semantic annotations within HTML pages – and will discuss the potential of these types of content for set completion, schema extension, and fact checking, as well as their utility as training data for matching textual entity descriptions.
Data Search and Search Joins (Universität Heidelberg 2015)Chris Bizer
The amount of structured data that is published on the Web has increased sharply over the last years. The deluge of available data calls for new search techniques which support users in finding and integrating data from large numbers of data sources. In his talk, Christian Bizer will give an overview of the different types of data search that have been proposed so far: Entity search, table search, constraint and unconstraint search joins. As an example of a system from the last category, he will introduce the Mannheim Search Join Engine which provides for executing unconstraint search joins over different types of Web data including Linked Data, Microdata, Web tables and Wikipedia tables.
Exploring the Application Potential of Relational Web TablesChris Bizer
The Web contains large amounts of HTML tables. Most of these tables are used for layout purposes, but a small subset of the tables is relational, meaning that they contain structured data describing a set of entities. Relational web tables cover a wide range of topics and there is a growing body of research investigating the utility of web table data for applications such as complementing cross-domain knowledge bases, extending arbitrary tables with additional attributes, and translating data values.
Until recently, most of the research around web tables originated from the large search engine companies as they were the only ones having access to large web crawls and thus were able to extract web table corpora from the crawls. This situation has changed in 2012 with the University of Mannheim and in 2014 with the Dresden University of Technology starting to extract web table corpora from the CommonCrawl, a large public web corpus.
In the talk, I will introduce the 2015 version of the Web Data Commons - Web Table Corpus. Afterward, I will give an overview of the different efforts that are currently conducted by my group on exploring the application potential of relational web tables. These efforts include profiling the content of web tables by matching them to cross-domain knowledge bases such as DBpedia, fusing web table data in order to complement cross-domain knowledge bases, and performing SearchJoins between a local table and a web table corpus in order to extend the local table with additional attributes.
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.
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.
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
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1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
Understanding User Behavior with Google Analytics.pdfSEO Article Boost
Unlocking the full potential of Google Analytics is crucial for understanding and optimizing your website’s performance. This guide dives deep into the essential aspects of Google Analytics, from analyzing traffic sources to understanding user demographics and tracking user engagement.
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Discover where your website traffic originates. By examining the Acquisition section, you can identify whether visitors come from organic search, paid campaigns, direct visits, social media, or referral links. This knowledge helps in refining marketing strategies and optimizing resource allocation.
User Demographics Insights:
Gain a comprehensive view of your audience by exploring demographic data in the Audience section. Understand age, gender, and interests to tailor your marketing strategies effectively. Leverage this information to create personalized content and improve user engagement and conversion rates.
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Learn how to measure user interaction with your site through key metrics like bounce rate, average session duration, and pages per session. Enhance user experience by analyzing engagement metrics and implementing strategies to keep visitors engaged.
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Understand the importance of conversion rates and how to track them using Google Analytics. Set up Goals, analyze conversion funnels, segment your audience, and employ A/B testing to optimize your website for higher conversions. Utilize ecommerce tracking and multi-channel funnels for a detailed view of your sales performance and marketing channel contributions.
Custom Reports and Dashboards:
Create custom reports and dashboards to visualize and interpret data relevant to your business goals. Use advanced filters, segments, and visualization options to gain deeper insights. Incorporate custom dimensions and metrics for tailored data analysis. Integrate external data sources to enrich your analytics and make well-informed decisions.
This guide is designed to help you harness the power of Google Analytics for making data-driven decisions that enhance website performance and achieve your digital marketing objectives. Whether you are looking to improve SEO, refine your social media strategy, or boost conversion rates, understanding and utilizing Google Analytics is essential for your success.
Italy Agriculture Equipment Market Outlook to 2027harveenkaur52
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Ken Research has an expertise in Agriculture and Animal Care sector and offer vast collection of information related to all major aspects such as Agriculture equipment, Crop Protection, Seed, Agriculture Chemical, Fertilizers, Protected Cultivators, Palm Oil, Hybrid Seed, Animal Feed additives and many more.
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Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
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Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdfFlorence Consulting
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
Is the Semantic Web what we expected? Adoption Patterns and Content-driven Challenges (ISWC 2016 Keynote)
1. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 1
15th International Semantic Web Conference (ISWC 2016)
Kobe, Japan, 10/20/2016
Is the Semantic Web what we Expected?
Deployment Patterns and Data-driven Challenges
Prof. Dr. Christian Bizer
2. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 2
Ian‘s Keynote Last Year in Bethlehem
http://videolectures.net/
iswc2015_horrocks_semantic_technology/
3. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 3
This Year
Use cases
on the public Web
many data sources
no central control
4. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 4
Outline
1. What did we expect the Semantic Web to be?
2. What does the Semantic Web actually look like?
1. Linked Data
2. HTML-embedded Data
3. Why is this the case?
4. What does this mean for Semantic Web applications?
5. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 5
1. What did we expect the Semantic Web to be?
6. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 6
2001 Article: The Semantic Web
Envisions three things to happen:
people publish structured data
on the Web
ontologies are used to enable
shared understanding
people implement cool applications that
do smart things with the available data
Tim Berners-Lee, James Hendler, Ora Lassila:
The Semantic Web. Scientific American, May 2001.
7. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 7
Expectation: Hyperlinks are Set on Data Level
https://www.w3.org/History/1989/proposal.html
https://www.w3.org/DesignIssues/LinkedData.html
8. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 8
Expectation: High Quality Content / Provenance Metadata
Publishers provide high quality content
Publishers support applications in determining trustworthiness
• by providing provenance metadata
• using digital signatures
Layer Cake, 2001
9. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 9
Check List: Our Expectations about the Semantic Web
1. People publish structured data on the Web
2. Ontologies are used to enable shared understanding
3. Hyperlinks are set on data level
4. People publish high quality content / metadata
5. Cool applications do smart things with the data
10. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 10
2. What does the Semantic Web actually look like?
Linked Data HTML-embedded Data
11. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 11
2.1 Linked Data Deployment
Schmachtenberg, Bizer, Paulheim: Adoption of the Linked Data Best Practices. ISWC2014.
Ermilov, Lehmann, Martin, Auer: LODStats: The Data Web Census Dataset. ISWC 2016.
Topics # of Datasets Percentage
Media 24 2 %
Government 199 18 %
Publications/Library 138 13 %
Geographic 27 2 %
Life Sciences 85 8 %
Cross‐domain 47 4 %
Unser‐generated Content 51 5 %
Social Networking 520 48 %
LODStats 2016: 2740 datasets
LOD Cloud 2014: 1091 datasets
12. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 12
Ontological Agreement
Strong agreement on some vocabularies
• base terminology, people, publications
Proprietary vocabularies are used in
addition to common ones,
as data is often very specific
http://linkeddatacatalog.dws.informatik.uni-mannheim.de/state/
https://lov.okfn.org/dataset/lov/
13. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 13
Some datasets put a lot of effort into linking
Many datasets only link to a small number of
other datasets or do not set RDF links at all
RDF Links
Link to # of Datasets Percentage
more than 10 datasets 79 8 %
6 to 10 datasets 81 8 %
5 datasets 31 3 %
4 datasets 42 4 %
3 datasets 54 5 %
2 datasets 106 10 %
1 datasets 176 17 %
0 datasets 445 44 %
http://linkeddatacatalog.dws.informatik.uni-mannheim.de/state/
71 %
16 %
14. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 14
Cool Applications
Prototypes of Semantic Web browsers and search engines.
15. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 15
2.2 HTML-embedded Data
Microformats
Microdata
RDFa
JSON-LD
16. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 16
Overall Adoption 2015
Web Data Commons, 2015:
2.72 million pay-level-domains (PLDs) out of the 14.41
million PLDs provide HTML-embedded data (19%)
540 million HTML pages out of the 1.7 billion pages
provide HTML-embedded data (30%)
Guha/Brickley/Macbeth, 2015:
12 million websites provide schema.org data
Guha/Brickley/Macbeth: Schema.org. ACM queue, 2015
http://webdatacommons.org/structureddata/2015-11/
17. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 17
Number of PLDs providing HTML-embedded Data
http://webdatacommons.org/structureddata/
18. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 18
Widely-used Classes
Class # of Domains
WDC 2015
# of Domains
schema.org 2015
schema:PostalAddress 124,000 > 1,000,000
schema:Product 108,000 > 1,000,000
schema:Offer 82,000 > 1,000,000
schema:LocalBusiness 77,000 500,000 ‐ 1,000,000
schema:Person 74,000 > 1,000,000
schema:Review 28,000 250,000 ‐ 500,000
schema:GeoCoordinates 17,000 100,000 ‐ 250,000
schema:Event 12,000 100,000 ‐ 250,000
schema:Hotel 5,300 10,000 ‐ 50,000
schema:Restaurant 3,800 10,000 ‐ 50,000
schema:JobPosting 3,600 10,000 ‐ 50,000
http://webdatacommons.org/structureddata/2015-11/
19. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 19
Adoption by E-Commerce Websites
Alexa Top‐15 Website schema:Product
Amazon.com
Ebay.com
NetFlix.com
Amazon.co.uk
Walmart.com
etsy.com
Ikea.com
Bestbuy.com
Homedepot.com
Target.com
Groupon.com
Newegg.com
Lowes.com
Macys.com
Nordstrom.com
Adoption:
60 %
21. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 21
Challenge: Small Amount of Identifiers and Data Links
1. Small amount of product identifiers
2. Hardly any schema:sameAs links
Definition: URL of a reference Web page that unambiguously
indicates the item's identity.
Properties PLDs
# %
schema:Product/sameAs 85 0.07 %
schema:LocalBusiness/sameAs 655 0.8 %
schema:Organization/sameAs 3,900 5 %
Properties PLDs
# %
schema:Product/productID 9,221 10 %
schema:Product/gtin13 935 1 %
http://webdatacommons.org/structureddata/2015-11/
22. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 22
Challenge: Flat Data Structures
Websites do not explicitly annotate product features
but mention them in product names and descriptions.
<div itemtype="http://schema.org/Product">
<span itemprop="name">Apple MacBook Air A1370 Intel Core i5
1.60GHz 64GB SSD 11.6 Laptop
</span>
<span itemprop=“description"> Catch up on work, school, or socializing
on the Apple MacBook Air A1370 11.6-inch laptop. This handy
computer features 2GB DDR3 RAM, an Intel Core i5 560UM
processor, 64GB hard drive, and the Mac OS …
</span>
</div>
Petrovski, Bryl, Bizer: Integrating Product Data from Websites Offering Microdata Markup.
DEOS 2014.
23. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 23
Challenge: Product Categorization
1. Small amount of websites publishing categorization information
2. Heterogeneity of the product taxonomies
Properties PLDs
# %
schema:Offer/category 2200 2 %
schema:WebPage/breadcrumb 460 0.4 %
Home > Shop > Outdoor & Garden > Barbecues & Outdoor
Living > Garden Furniture > Tables > Dining Tables
Philadelphia Eagles > Philadelphia Eagles Mens > Philadelphia
Eagles Mens Jerseys > over $60
Meusel, Primpeli, Meilicke, Paulheim, Bizer: Exploiting Microdata Annotations to
Consistently Categorize Product Offers at Web Scale. EC-Web 2015.
24. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 24
Adoption by Travel Websites
Top 15 Travel Websites schema:Hotel
Booking.com
TripAdvisor
Expedia
Agoda
Hotels.com (uses OGP)
Kayak
Priceline
Travelocity
Orbitz
ChoiceHotels
HolidayCheck
ChoiceHotels
InterContinental Hotels Group
Marriott International
Global Hyatt Corp.
Adoption:
86 %
Kärle, Fensel, Toma, Fensel: Why Are There More Hotels in Tyrol than in Austria?
Analyzing Schema. org Usage in the Hotel Domain. ICTT 2016.
25. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 25
Properties used to Describe Hotels
Top 10 Properties PLDs
# %
schema:Hotel/name 4173 88,35 %
schema:Hotel/address 3311 70,10 %
schema:Hotel/telephone 2488 52,68 %
schema:PostalAddress/streetAddress 2362 50,01 %
schema:PostalAddress/addressLocality 2231 47,24 %
schema:Hotel/url 2102 44,51 %
schema:PostalAddress/postalCode 2096 44,38 %
schema:AggregateRating/ratingValue 1952 41,33 %
schema:Hotel/aggregateRating 1866 39,51 %
schema:AggregateRating/bestRating 1697 35,93 %
Might improve in the future as new schema.org accommodation
vocabulary was released August 2016.
26. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 26
Adoption by Job Portals
Adoption:
70 %
Top‐10 Employment Websites schema:JobPosting
Indeed.com
Monster.com
Careerbuilder.com
Snagajob.com
Jobsdb.com
Jobsearch.about.com
Jobs.net
Internships.com
Jobs.aol.com
Quintcareers.com
28. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 28
Cool Applications
1. Rich snippets within search results
2. Knowledge graph
panels
https://developers.google.com/
structured-data/
29. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 29
Cool Applications
Open Graph Protocol allows site owners to
determine how entities are displayed in Facebook
uses RDFa for marking up data in HTML pages
used by over 200,000 websites (WDC 2015)
30. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 30
Our Expectations Revisited
Expectation Linked Data HTML‐embedded Data
1. People publish
structured data
> 1000 sources,
wide range of
specific topics
Millions of sources,
focused on search
engines and Facebook
2. Ontologies enable
understanding
Partial agreement,
complex data structures
Strong agreement,
flat data structures
3. Hyperlinks on data
level
Some data links Hardly any data links
4. High quality content
Web quality,
partly outdated
Web quality,
some SPAM
5. Cool applications
various application
prototypes
strong application pull
by search engines
31. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 31
3. Why is this the Case?
Expectation Linked Data HTML‐embedded Data
1. People publish
structured data
> 1000 sources,
wide range of
specific topics
Millions of sources,
focused on search
engines and Facebook
2. Ontologies enable
understanding
Partial agreement,
complex data structures
Strong agreement,
flat data structures
3. Hyperlinks on data
level
Some data links Hardly any data links
4. High quality content
Web quality,
partly outdated
Web quality,
some SPAM
5. Cool applications
various application
prototypes
strong application pull
by search engines
32. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 32
Benefits and Costs for Data Providers
Making the Web a better place isn’t enough
motivation for most data providers.
33. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 33
Benefits of Publishing HTML-Embedded Data
Get richer visibility in search results and
potentially more clicks.
34. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 34
Effort for Publishing HTML-Embedded Data
Most providers just change their HTML templates
If more effort is required, most providers just do not do it
1. Annotate specific product features
• given free text descriptions in the backend database
2. Map to common product taxonomy like GS1 GPC
• given local categorizations
3. Annotate skills and occupational categories
• given free text descriptions in the backend database
<div itemtype="http://schema.org/Hotel">
<span itemprop="name">Vienna Marriott Hotel</span>
<span itemprop="address" itemscope="" itemtype="http://schema.org/PostalAddress">
<span itemprop="streetAddress">Parkring 12a</span>
<span itemprop="addressLocality">Vienna</span>
</span></div>
35. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 35
Effort for Setting Data Links
Effort:
1. Decide which data sources to link to
2. Compare schemata and develop a matching rule for each class
3. Run link generation algorithm
4. Publish resulting link set on the Web
Benefits:
• You increase the value of your data as it becomes
easier to use it together with data from other sources
• You reduce the integration costs for the data consumer
<http://dbpedia.org/resource/Berlin> owl:sameAs
<http://sws.geonames.org/2950159> .
Data publisher
provides links
Effort
Distribution
Data consumer
generates links
36. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 36
For Whom does the Linking Effort pay off?
Scientists
• Innovation becomes possible by
connecting datasets
• My impact / prestige grows if
my data is used for cool things
Librarians
• Have the mission to catalog artefacts
• Traditionally use shared identifiers
E-Commerce Vendors
• Benefits of setting data links are unclear
• Just want to look nice on Google
• Might not want to be comparable on
price portals
37. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 37
Effort of Maintaining Links
We want to be nice!
• we want to link to everybody
We set instance- and schema-level links!
• created and collected 37 link sets
• over 20 million RDF links
• http://wiki.dbpedia.org/services-resources/interlinking
https://github.com/dbpedia/links
We would likely need a full-time volunteer to maintain all these links
Result: Many dead links
1. because target data source has changed
2. because we used bad linkage rules due to insufficient domain knowledge
38. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 38
Hypothesis
Missing links and shared identifiers
Flat data structures
Heterogeneity of taxonomies
Mixed data quality
We will keep on seeing similar adoption patterns,
as we need to be realistic about the effort
spent by data publishers
39. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 39
4. What does this mean for Semantic Web Applications?
40. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 40
Be happy about all semantic clues
(integration hints) provided
But do not expect the clues to be perfect
4. What does this mean for Semantic Web Applications?
41. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 41
Applications should be happy about …
… all effort that data providers put into setting data links
• but treat links with caution as they might be wrong / outdated
… all effort that data providers put into using common vocabularies
• but still try to understand proprietary vocabularies / taxonomies
… all effort that data providers put into structuring their data
• but still try to understand flat free-text descriptions
Treat all statements on the Web as a claims
• whose trustworthiness needs to be verified
Data Publisher’s
Effort
Data Consumer’s
Effort
Effort
Distribution
42. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 42
Semantic Web Clients need to be FAT Clients
There are no shortcuts!
1. Crawl data
2. Normalize vocabularies
6. Resolve data conflicts
3. Parse flat descriptions
4. Verify existing data links
5. Create missing data links
43. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 43
Parsing Flat Descriptions
Ristoski, Mika: Enriching Product Ads with Metadata from HTML Annotations. ESWC 2016.
Foley, et al.: Learning to Extract Local Events from the Web. SIGIR 2015.
Dictionary
Parser
schema.org
schema.org data suitable as
distant supervision:
schema:Product/brand
schema:Product/manufacturer
schema:JobPosting/industry
Schema:JobPosting/skills
schema:Event/name
44. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 44
Create and Verify Data Links
Supervised learning of detailed
matching rules leads to F1>95%
(e.g. Silk and LIMES frameworks)
Sources of supervision
1. Data links and shared identifiers
• owl:sameAs
• schema:Product/productID
• schema:Product/gtin13
2. Human guidance via active learning
How to generalize matching rules to data
from multiple sources?
Isele, Bizer: Active Learning of Expressive Linkage Rules using Genetic Programming. JWS 2013.
Stonebraker, et al.: Data Curation at Scale: The Data Tamer System. CIDR 2013.
45. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 45
Resolve Data Conflicts
Do you have some data that you already trust?
Knowledge-based Trust
• determine trustworthiness of a data source by comparing its content
with trusted data (ground truth)
• outperforms PageRank and voting
Dong, et al.: Knowledge-based Trust: Estimating the Trustworthiness of Web Sources. VLDB 2015.
Web Data Source
Country City
Germany Berlin
France Paris
United Kingdom London
Canada Ottawa
USA Washington D.C.
Mexico Ecatepec
?
?
Trusted Data
Country Capital
Germany Berlin
France
United Kingdom London
Canada
USA Washington D.C.
Mexico Mexico City
46. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 46
Google Knowledge Vault
Extends Freebase with data from
one billion web pages
1. Web text (TXT): Entity linking,
relationship extraction
2. HTML trees (DOM): Wrapper induction
3. HTML tables (TBL): Relational tables
4. Semantic Annotations (ANO): schema.org, OGP
Employs knowledge-based trust for ranking
Results:
• 271 million facts with confidence >90%
• 90 million facts not in Freebase before
Dong, et al.: Knowledge Vault: A Web-scale Approach to Probabilistic Knowledge Fusion.
SIGKDD 2014.
47. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 47
The Structural Continuum
Be open to different forms of
“structured” Web content.
HTML tables
DOM Trees
CSV Tables
HTML-embedded Data
Linked Data Upper
Ontologies
48. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 48
Exploit Schema.org and HTML Tables Together
Qui, et al.: DEXTER: Large-Scale Discovery and Extraction of Product Specifications on the Web. VLDB 2015.
Petrovski, et al: The WDC Gold Standards for Product Feature Extraction and Product Matching. ECWeb 2016.
s:name
HTML table
s:breadcrumb
49. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 49
Conclusions
50. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 50
The Semantic Web contains more data than most people like
exciting test-bed for research on data profiling, cleansing
and integration
endless data pool for commercial applications (product
comparison, business listings, job search, …)
The Semantic Web is Huge
Billions of product offers
A description of every
hotel in the world
Lots of data about local businesses
51. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 51
We will keep on seeing Similar Adoption Patterns
as we need to be realistic about the effort spent by data publishers
be happy about any semantic clues (integration hints) provided
design algorithms to work despite the scarcity and noisiness of clues
Flat Data Structures
Missing Links and Identifiers
Mixed Data Quality
Heterogeneity of Taxonomies
52. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 52
Semantic Web Clients need to be FAT Clients
There are no shortcuts!
1. Crawl data
2. Normalize vocabularies
6. Resolve data conflicts
3. Parse flat descriptions
4. Verify existing data links
5. Create missing data links
53. Bizer: Is the Semantic Web what we Expected? ISWC 2016, 10/20/2016 Slide 53
Thank you!