DBPedia-past-present-future
•
0 likes•2,818 views
DBPedia past, present and future - Dimitris Kontokostas. Reveals recent developments in the Linked Data and knowledge graphs field and how DBPedia progress with wikipedia data.
Report
Share
Report
Share
Download to read offline
Recommended
The future of Big Data tooling
Alexander Aldev - Co-founder and CTO of MammothDB, currently focused on the architecture of the distributed database engine. Notable achievements in the past include managing the launch of the first triple-play cable service in Bulgaria and designing the architecture and interfaces from legacy systems of DHL Global Forwarding's data warehouse. Has lectured on Hadoop at AUBG and MTel.
"The future of Big Data tooling" will briefly review the architectural concepts of current Big Data tools like Hadoop and Spark. It will make the argument, from the perspective of both technology and economics, that the future of Big Data tools is in optimizing local storage and compute efficiency.
Knowledge Graph Introduction
The document provides an introduction to Prof. Dr. Sören Auer and his background in knowledge graphs. It discusses his current role as a professor and director focusing on organizing research data using knowledge graphs. It also briefly outlines some of his past roles and major scientific contributions in the areas of technology platforms, funding acquisition, and strategic projects related to knowledge graphs.
Das Semantische Daten Web für Unternehmen
This document summarizes the vision, technology, and applications of the Semantic Data Web for businesses. It discusses how the Semantic Web can help solve problems of searching for complex information across different data sources by complementing text on web pages with structured linked open data. It provides overviews of RDF standards, vocabularies, and technologies like SPARQL and OntoWiki that allow creating and managing structured knowledge bases. It also presents examples like DBpedia that extract structured data from Wikipedia and make it available on the web as linked open data.
Big Data: Improving capacity utilization of transport companies
Asparuh Koev is a successful serial entrepreneur. Currently, he is a CEO of Transmetrics, a solution for cargo transport companies that uses Big Data and predictive analytics. Asparuh holds a Bachelor’s Degree in Computer Science from the American University in Bulgaria and an MBA degree from the Vlerick Business School in Belgium.
“Big Data: Improving capacity utilization of transport companies” will explore the practical benefits, IT tools and challenges of implementing a big data solution in a traditional industry (cargo transport), using as a showcase a predictive analytics project done for Speedy.
From Open Linked Data towards an Ecosystem of Interlinked Knowledge
This document discusses the development of linked open data and its potential to create an ecosystem of interlinked knowledge. It outlines achievements in extending the web with structured data and the growth of an open research community. However, it also identifies challenges regarding coherence, quality, performance and usability that must be addressed for linked data to reach its full potential as a global platform for knowledge integration. The document proposes that addressing these issues could ultimately lead to an ecosystem of interlinked knowledge on the semantic web.
Linked Data for Architecture, Engineering and Construction (AEC)
The document discusses the relationship between building information modeling (BIM) and the semantic web. It provides an introduction to linked data and describes how semantic web technologies can be used to add contextual and background knowledge to BIM data, such as geographical, historical, and statistical information. It also addresses challenges around preserving and maintaining the evolution of linked BIM and architecture data on the semantic web.
Enterprise knowledge graphs
Enterprise knowledge graphs use semantic technologies like RDF, RDF Schema, and OWL to represent knowledge as a graph consisting of concepts, classes, properties, relationships, and entity descriptions. They address the "variety" aspect of big data by facilitating integration of heterogeneous data sources using a common data model. Key benefits include providing background knowledge for various applications and enabling intra-organizational data sharing through semantic integration. Challenges include ensuring data quality, coherence, and managing updates across the knowledge graph.
Creating knowledge out of interlinked data
This document discusses creating knowledge from interlinked data. It notes that while reasoning over large datasets does not currently scale well, linked data approaches are more feasible as they allow for incremental improvement. The document outlines the linked data lifecycle including extraction, storage and querying, authoring, linking, and enrichment of semantic data. It provides examples of projects that extract, store, author and link diverse datasets including DBpedia, LinkedGeoData, and statistical data. Challenges discussed include improving query performance, developing standardized interfaces, and increasing the amount of interlinking between datasets.
Recommended
The future of Big Data tooling
Alexander Aldev - Co-founder and CTO of MammothDB, currently focused on the architecture of the distributed database engine. Notable achievements in the past include managing the launch of the first triple-play cable service in Bulgaria and designing the architecture and interfaces from legacy systems of DHL Global Forwarding's data warehouse. Has lectured on Hadoop at AUBG and MTel.
"The future of Big Data tooling" will briefly review the architectural concepts of current Big Data tools like Hadoop and Spark. It will make the argument, from the perspective of both technology and economics, that the future of Big Data tools is in optimizing local storage and compute efficiency.
Knowledge Graph Introduction
The document provides an introduction to Prof. Dr. Sören Auer and his background in knowledge graphs. It discusses his current role as a professor and director focusing on organizing research data using knowledge graphs. It also briefly outlines some of his past roles and major scientific contributions in the areas of technology platforms, funding acquisition, and strategic projects related to knowledge graphs.
Das Semantische Daten Web für Unternehmen
This document summarizes the vision, technology, and applications of the Semantic Data Web for businesses. It discusses how the Semantic Web can help solve problems of searching for complex information across different data sources by complementing text on web pages with structured linked open data. It provides overviews of RDF standards, vocabularies, and technologies like SPARQL and OntoWiki that allow creating and managing structured knowledge bases. It also presents examples like DBpedia that extract structured data from Wikipedia and make it available on the web as linked open data.
Big Data: Improving capacity utilization of transport companies
Asparuh Koev is a successful serial entrepreneur. Currently, he is a CEO of Transmetrics, a solution for cargo transport companies that uses Big Data and predictive analytics. Asparuh holds a Bachelor’s Degree in Computer Science from the American University in Bulgaria and an MBA degree from the Vlerick Business School in Belgium.
“Big Data: Improving capacity utilization of transport companies” will explore the practical benefits, IT tools and challenges of implementing a big data solution in a traditional industry (cargo transport), using as a showcase a predictive analytics project done for Speedy.
From Open Linked Data towards an Ecosystem of Interlinked Knowledge
This document discusses the development of linked open data and its potential to create an ecosystem of interlinked knowledge. It outlines achievements in extending the web with structured data and the growth of an open research community. However, it also identifies challenges regarding coherence, quality, performance and usability that must be addressed for linked data to reach its full potential as a global platform for knowledge integration. The document proposes that addressing these issues could ultimately lead to an ecosystem of interlinked knowledge on the semantic web.
Linked Data for Architecture, Engineering and Construction (AEC)
The document discusses the relationship between building information modeling (BIM) and the semantic web. It provides an introduction to linked data and describes how semantic web technologies can be used to add contextual and background knowledge to BIM data, such as geographical, historical, and statistical information. It also addresses challenges around preserving and maintaining the evolution of linked BIM and architecture data on the semantic web.
Enterprise knowledge graphs
Enterprise knowledge graphs use semantic technologies like RDF, RDF Schema, and OWL to represent knowledge as a graph consisting of concepts, classes, properties, relationships, and entity descriptions. They address the "variety" aspect of big data by facilitating integration of heterogeneous data sources using a common data model. Key benefits include providing background knowledge for various applications and enabling intra-organizational data sharing through semantic integration. Challenges include ensuring data quality, coherence, and managing updates across the knowledge graph.
Creating knowledge out of interlinked data
This document discusses creating knowledge from interlinked data. It notes that while reasoning over large datasets does not currently scale well, linked data approaches are more feasible as they allow for incremental improvement. The document outlines the linked data lifecycle including extraction, storage and querying, authoring, linking, and enrichment of semantic data. It provides examples of projects that extract, store, author and link diverse datasets including DBpedia, LinkedGeoData, and statistical data. Challenges discussed include improving query performance, developing standardized interfaces, and increasing the amount of interlinking between datasets.
Graph Database
This document discusses graph databases and provides an overview of Neo4j. It describes how graph databases are useful for modeling connected data and performing complex queries over relationships. The document outlines the benefits of graph databases like expressing the domain as a graph and using graph traversals for queries. It then provides details on Neo4j, describing it as a widely used open source graph database that is scalable and supports ACID transactions. The document includes examples of creating nodes and relationships in Neo4j and traversing the graph.
Graph database
Graph databases use graph structures to represent and store data, with nodes connected by edges. They are well-suited for interconnected data. Unlike relational databases, graph databases allow for flexible schemas and querying of relationships. Common uses of graph databases include social networks, knowledge graphs, and recommender systems.
d:swarm - A Library Data Management Platform Based on a Linked Open Data Appr...
D:SWARM is a graphical web-based ETL modelling tool that serves to import data from heterogeneous sources with different formats, to map input to output schemata and design transformation workflows, to load transformed data into property graph database. It is developed in a collaborative project by SLUB Dresden (www.slub-dresden.de) and Avantgarde Labs GmbH (www.avantgarde-labs.de) features additional functionalities like exporting of data models as RDF and sharing mappings and transformation workflows.
Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Over the past 4 years, the Semantic Web activity has gained momentum with the widespread publishing of structured data as RDF. The Linked Data paradigm has therefore evolved from a practical research idea into
a very promising candidate for addressing one of the biggest challenges
of computer science: the exploitation of the Web as a platform for data
and information integration. To translate this initial success into a
world-scale reality, a number of research challenges need to be
addressed: the performance gap between relational and RDF data
management has to be closed, coherence and quality of data published on
the Web have to be improved, provenance and trust on the Linked Data Web
must be established and generally the entrance barrier for data
publishers and users has to be lowered. This tutorial will discuss
approaches for tackling these challenges. As an example of a successful
Linked Data project we will present DBpedia, which leverages Wikipedia
by extracting structured information and by making this information
freely accessible on the Web. The tutorial will also outline some recent advances in DBpedia, such as the mappings Wiki, DBpedia Live as well as
the recently launched DBpedia benchmark.
Linked data for Enterprise Data Integration
The Web evolves into a Web of Data. In parallel Intranets of large companies will evolve into Data Intranets based on the Linked Data principles. Linked Data has the potential to complement the SOA paradigm with a light-weight, adaptive data integration approach.
Graph Database and Neo4j
This document provides an overview of graph databases and Neo4j. It discusses how graph databases are better suited than relational databases for interconnected data and have simpler data models. Neo4j is highlighted as a graph database that uses nodes, edges and properties to represent data and uses the Cypher query language. It is fully ACID compliant, open source, and has a large active community.
Data Management and Integration with d:swarm (Lightning talk, ELAG 2014)
d:swarm is a middleware for data integration and management currently developed by the Saxon State and University Library Dresden in cooperation with Avantgarde Labs.
Towards digitizing scholarly communication
Slides of the VIVO 2016 Conference keynote: Despite the availability of ubiquitous connectivity and information technology, scholarly communication has not changed much in the last hundred years: research findings are still encoded in and decoded from linear, static articles and the possibilities of digitization are rarely used. In this talk, we will discuss strategies for digitizing scholarly communication. This comprises in particular: the use of machine-readable, dynamic content; the description and interlinking of research artifacts using Linked Data; the crowd-sourcing of multilingual
educational and learning content. We discuss the relation of these developments to research information systems and how they could become part of an open ecosystem for scholarly communication.
NoSQL Graph Databases - Why, When and Where
NoSQL Graph Databases - Why, When And Where should we use it.
Graph DB - The new era of understanding data
Semantic Graph Databases: The Evolution of Relational Databases
In this webinar, Barry Zane, our Vice President of Engineering, discusses the evolution of databases from Relational to Semantic Graph and the Anzo Graph Query Engine, the key element of scale in the Anzo Smart Data Lake. Based on elastic clustered, in-memory computing, the Anzo Graph Query Engine offers interactive ad hoc query and analytics on datasets with billions of triples. With this powerful layer over their data, end users can effect powerful analytic workflows in a self-service manner.
What can linked data do for digital libraries
The slides for the keynote on Linked Data and Digital Libraries I gave at TPDL2013 on Sep 25 2013 in Valetta, Malta.
Analytics and Access to the UK web archive
The document summarizes the background, purpose, and methods of the UK Web Archive. It discusses how the archive collects, stores, and provides access to snapshots of UK websites over time to preserve digital cultural heritage. It also describes challenges of scale due to the immense size of web content and techniques like full-text search and data analytics that are used to facilitate discovery of information within the archive.
Mongo db
MongoDB is a cross-platform document-oriented database that uses JSON-like documents with dynamic schemas, making it easier and faster to integrate data compared to traditional relational databases. It is developed by MongoDB Inc. and is open-source. MongoDB supports features like ad hoc queries, indexing, replication for high availability, automatic load balancing, and horizontal scalability. It is a popular choice for storing large datasets and powering modern applications.
ROI in Linking Content to CRM by Applying the Linked Data Stack
Today, decision makers in enterprises have to rely more and more on a variety of data sets that are internally but also externally available in heterogeneous formats. Therefore, intelligent processes are required to build an integrated knowledge-base. Unfortunately, the adoption of the Linked Data lifecycle within enterprises, which targets the extraction, interlinking, publishing and analytics of distributed data, lags behind the public domain due to missing frameworks that are efficiently to deploy and ease to use. In this paper, we present our adoption of the lifecycle through our generic, enterprise-ready Linked Data workbench. To judge its benefits, we describe its application within a real-world Customer Relationship Management scenario. It shows (1) that sales employee could significantly reduce their workload and (2) that the integration of sophisticated Linked Data tools come with an obvious positive Return on Investment.
Choosing the Right Graph Database to Succeed in Your Project
The document discusses choosing the right graph database for projects. It describes Ontotext, a provider of graph database and semantic technology products. It outlines use cases for graph databases in areas like knowledge graphs, content management, and recommendations. The document then examines Ontotext's GraphDB semantic graph database product and how it can address key use cases. It provides guidance on choosing a GraphDB option based on project stage from learning to production.
Intro to bigdata on gcp (1)
Big data is characterized by large and complex datasets that are difficult to process using traditional software. These massive volumes of data, known by characteristics like volume, velocity, variety, veracity, value, and volatility, can provide insights to address business problems. Google Cloud Platform offers tools like Cloud Storage, Big Query, PubSub, Dataflow, and Cloud Storage storage classes that can handle big data according to these characteristics and help extract value from large and diverse datasets.
Linked Data Experiences at Springer Nature
An overview of how we're using semantic technologies at Springer Nature, and an introduction to our latest product: www.scigraph.com
(Keynote given at http://2016.semantics.cc/, Leipzig, Sept 2016)
Dataverse opportunities
DANS is a institute of KNAW and NWO that provides open data repository and analysis services through its DataverseNL platform. DataverseNL allows researchers to share, archive, and analyze research data. It provides features such as persistent identifiers, metadata harvesting, and tools for visualization, statistics, and linking datasets. DataverseNL aims to make data Findable, Accessible, Interoperable and Re-usable in accordance with FAIR data principles. It can also integrate with other data archives and repositories to preserve and provide access to research data.
The Power of Semantic Technologies to Explore Linked Open Data
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/.
Hadoop - A big data initiative
This document discusses big data solutions and introduces Hadoop. It defines common big data problems related to volume, velocity, and variety of data. Traditional storage does not work well for this type of unstructured data. Hadoop provides solutions through HDFS for storage, MapReduce for processing, and additional tools like HBase, Pig, Hive, Zookeeper, and Spark to handle different data and analytic needs. Each tool is described briefly in terms of its purpose and how it works with Hadoop.
Real-time information analysis: social networks and open data
Plamen Penev - Co-founder of Yatrus Analytics, graduated from the University of Essex (Political Science and IR). He works In Yatrus Analytics in the fields of NLP(Natural Language Processing), Text mining, Graph analysis.
"Real-time information analysis: social networks and open data" will focus on the problem of real-time multisource data analytics, as well as the variety and the combination of many various data sources, the blending of those in real-time. The information discovery from Twitter and other sources as seen through the state of NLP (Event extraction, Classification of events, Sentiment analysis) in combination with financial and economic data.
Tweeting beyond Facts – The Need for a Linguistic Perspective
The document discusses applying linguistic principles to natural language processing tasks. It argues that a trigger-scope approach to analyzing negation, modality, and speculative language has proven effective. The approach uses general linguistic modules as preprocessing before applying domain-specific models. Underappreciated linguistic elements like numbers, amounts, locations, and modifiers provide useful information for tasks. A suite of language-oriented preprocessing modules could improve downstream specialized processing by adapting general linguistic treatments to specific domains.
More Related Content
What's hot
Graph Database
This document discusses graph databases and provides an overview of Neo4j. It describes how graph databases are useful for modeling connected data and performing complex queries over relationships. The document outlines the benefits of graph databases like expressing the domain as a graph and using graph traversals for queries. It then provides details on Neo4j, describing it as a widely used open source graph database that is scalable and supports ACID transactions. The document includes examples of creating nodes and relationships in Neo4j and traversing the graph.
Graph database
Graph databases use graph structures to represent and store data, with nodes connected by edges. They are well-suited for interconnected data. Unlike relational databases, graph databases allow for flexible schemas and querying of relationships. Common uses of graph databases include social networks, knowledge graphs, and recommender systems.
d:swarm - A Library Data Management Platform Based on a Linked Open Data Appr...
D:SWARM is a graphical web-based ETL modelling tool that serves to import data from heterogeneous sources with different formats, to map input to output schemata and design transformation workflows, to load transformed data into property graph database. It is developed in a collaborative project by SLUB Dresden (www.slub-dresden.de) and Avantgarde Labs GmbH (www.avantgarde-labs.de) features additional functionalities like exporting of data models as RDF and sharing mappings and transformation workflows.
Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Over the past 4 years, the Semantic Web activity has gained momentum with the widespread publishing of structured data as RDF. The Linked Data paradigm has therefore evolved from a practical research idea into
a very promising candidate for addressing one of the biggest challenges
of computer science: the exploitation of the Web as a platform for data
and information integration. To translate this initial success into a
world-scale reality, a number of research challenges need to be
addressed: the performance gap between relational and RDF data
management has to be closed, coherence and quality of data published on
the Web have to be improved, provenance and trust on the Linked Data Web
must be established and generally the entrance barrier for data
publishers and users has to be lowered. This tutorial will discuss
approaches for tackling these challenges. As an example of a successful
Linked Data project we will present DBpedia, which leverages Wikipedia
by extracting structured information and by making this information
freely accessible on the Web. The tutorial will also outline some recent advances in DBpedia, such as the mappings Wiki, DBpedia Live as well as
the recently launched DBpedia benchmark.
Linked data for Enterprise Data Integration
The Web evolves into a Web of Data. In parallel Intranets of large companies will evolve into Data Intranets based on the Linked Data principles. Linked Data has the potential to complement the SOA paradigm with a light-weight, adaptive data integration approach.
Graph Database and Neo4j
This document provides an overview of graph databases and Neo4j. It discusses how graph databases are better suited than relational databases for interconnected data and have simpler data models. Neo4j is highlighted as a graph database that uses nodes, edges and properties to represent data and uses the Cypher query language. It is fully ACID compliant, open source, and has a large active community.
Data Management and Integration with d:swarm (Lightning talk, ELAG 2014)
d:swarm is a middleware for data integration and management currently developed by the Saxon State and University Library Dresden in cooperation with Avantgarde Labs.
Towards digitizing scholarly communication
Slides of the VIVO 2016 Conference keynote: Despite the availability of ubiquitous connectivity and information technology, scholarly communication has not changed much in the last hundred years: research findings are still encoded in and decoded from linear, static articles and the possibilities of digitization are rarely used. In this talk, we will discuss strategies for digitizing scholarly communication. This comprises in particular: the use of machine-readable, dynamic content; the description and interlinking of research artifacts using Linked Data; the crowd-sourcing of multilingual
educational and learning content. We discuss the relation of these developments to research information systems and how they could become part of an open ecosystem for scholarly communication.
NoSQL Graph Databases - Why, When and Where
NoSQL Graph Databases - Why, When And Where should we use it.
Graph DB - The new era of understanding data
Semantic Graph Databases: The Evolution of Relational Databases
In this webinar, Barry Zane, our Vice President of Engineering, discusses the evolution of databases from Relational to Semantic Graph and the Anzo Graph Query Engine, the key element of scale in the Anzo Smart Data Lake. Based on elastic clustered, in-memory computing, the Anzo Graph Query Engine offers interactive ad hoc query and analytics on datasets with billions of triples. With this powerful layer over their data, end users can effect powerful analytic workflows in a self-service manner.
What can linked data do for digital libraries
The slides for the keynote on Linked Data and Digital Libraries I gave at TPDL2013 on Sep 25 2013 in Valetta, Malta.
Analytics and Access to the UK web archive
The document summarizes the background, purpose, and methods of the UK Web Archive. It discusses how the archive collects, stores, and provides access to snapshots of UK websites over time to preserve digital cultural heritage. It also describes challenges of scale due to the immense size of web content and techniques like full-text search and data analytics that are used to facilitate discovery of information within the archive.
Mongo db
MongoDB is a cross-platform document-oriented database that uses JSON-like documents with dynamic schemas, making it easier and faster to integrate data compared to traditional relational databases. It is developed by MongoDB Inc. and is open-source. MongoDB supports features like ad hoc queries, indexing, replication for high availability, automatic load balancing, and horizontal scalability. It is a popular choice for storing large datasets and powering modern applications.
ROI in Linking Content to CRM by Applying the Linked Data Stack
Today, decision makers in enterprises have to rely more and more on a variety of data sets that are internally but also externally available in heterogeneous formats. Therefore, intelligent processes are required to build an integrated knowledge-base. Unfortunately, the adoption of the Linked Data lifecycle within enterprises, which targets the extraction, interlinking, publishing and analytics of distributed data, lags behind the public domain due to missing frameworks that are efficiently to deploy and ease to use. In this paper, we present our adoption of the lifecycle through our generic, enterprise-ready Linked Data workbench. To judge its benefits, we describe its application within a real-world Customer Relationship Management scenario. It shows (1) that sales employee could significantly reduce their workload and (2) that the integration of sophisticated Linked Data tools come with an obvious positive Return on Investment.
Choosing the Right Graph Database to Succeed in Your Project
The document discusses choosing the right graph database for projects. It describes Ontotext, a provider of graph database and semantic technology products. It outlines use cases for graph databases in areas like knowledge graphs, content management, and recommendations. The document then examines Ontotext's GraphDB semantic graph database product and how it can address key use cases. It provides guidance on choosing a GraphDB option based on project stage from learning to production.
Intro to bigdata on gcp (1)
Big data is characterized by large and complex datasets that are difficult to process using traditional software. These massive volumes of data, known by characteristics like volume, velocity, variety, veracity, value, and volatility, can provide insights to address business problems. Google Cloud Platform offers tools like Cloud Storage, Big Query, PubSub, Dataflow, and Cloud Storage storage classes that can handle big data according to these characteristics and help extract value from large and diverse datasets.
Linked Data Experiences at Springer Nature
An overview of how we're using semantic technologies at Springer Nature, and an introduction to our latest product: www.scigraph.com
(Keynote given at http://2016.semantics.cc/, Leipzig, Sept 2016)
Dataverse opportunities
DANS is a institute of KNAW and NWO that provides open data repository and analysis services through its DataverseNL platform. DataverseNL allows researchers to share, archive, and analyze research data. It provides features such as persistent identifiers, metadata harvesting, and tools for visualization, statistics, and linking datasets. DataverseNL aims to make data Findable, Accessible, Interoperable and Re-usable in accordance with FAIR data principles. It can also integrate with other data archives and repositories to preserve and provide access to research data.
The Power of Semantic Technologies to Explore Linked Open Data
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/.
Hadoop - A big data initiative
This document discusses big data solutions and introduces Hadoop. It defines common big data problems related to volume, velocity, and variety of data. Traditional storage does not work well for this type of unstructured data. Hadoop provides solutions through HDFS for storage, MapReduce for processing, and additional tools like HBase, Pig, Hive, Zookeeper, and Spark to handle different data and analytic needs. Each tool is described briefly in terms of its purpose and how it works with Hadoop.
What's hot (20)
d:swarm - A Library Data Management Platform Based on a Linked Open Data Appr...
d:swarm - A Library Data Management Platform Based on a Linked Open Data Appr...
Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Data Management and Integration with d:swarm (Lightning talk, ELAG 2014)
Data Management and Integration with d:swarm (Lightning talk, ELAG 2014)
Semantic Graph Databases: The Evolution of Relational Databases
Semantic Graph Databases: The Evolution of Relational Databases
ROI in Linking Content to CRM by Applying the Linked Data Stack
ROI in Linking Content to CRM by Applying the Linked Data Stack
Choosing the Right Graph Database to Succeed in Your Project
Choosing the Right Graph Database to Succeed in Your Project
The Power of Semantic Technologies to Explore Linked Open Data
The Power of Semantic Technologies to Explore Linked Open Data
Viewers also liked
Real-time information analysis: social networks and open data
Plamen Penev - Co-founder of Yatrus Analytics, graduated from the University of Essex (Political Science and IR). He works In Yatrus Analytics in the fields of NLP(Natural Language Processing), Text mining, Graph analysis.
"Real-time information analysis: social networks and open data" will focus on the problem of real-time multisource data analytics, as well as the variety and the combination of many various data sources, the blending of those in real-time. The information discovery from Twitter and other sources as seen through the state of NLP (Event extraction, Classification of events, Sentiment analysis) in combination with financial and economic data.
Tweeting beyond Facts – The Need for a Linguistic Perspective
The document discusses applying linguistic principles to natural language processing tasks. It argues that a trigger-scope approach to analyzing negation, modality, and speculative language has proven effective. The approach uses general linguistic modules as preprocessing before applying domain-specific models. Underappreciated linguistic elements like numbers, amounts, locations, and modifiers provide useful information for tasks. A suite of language-oriented preprocessing modules could improve downstream specialized processing by adapting general linguistic treatments to specific domains.
Real-time analytics with HBase
Ivo Mitov – Co-founder of Data Fusion Bulgaria, software consultant in the area of EAI and Big Data
"Real-time analytics with HBase" is focused on the usage of coprocessor framework in HBase for event complex processing and simple analytics. The presentation will describe monitoring use case in the context of complex SOA environment.
Data science challenges in flight search
Have you ever searched for a flight online? Do you wonder when and where you can get the best price for your travel plans? And, why are there different flight prices? Now, do you want to know why it is hard to do a meta-search engine for travel, and especially for flights?
Presentation provided by SkyScanner, a leading travel search site offering unbiased, comprehensive and free flight, hotel and car hire search services, used by over 40 million unique visitors every month. Skyscanner opened its office in Sofia in October 2014 and is quickly growing its team here to help solve complex travel problems and continually improve their product.
Computer vision and image processing for dental products
Boyan Yankov presents the real life business case of VisagiSmile - cutting edge dental software for personalized smile design. The case includes both 2D imaging (facial landmarks, face classification) and 3D modelling (3D teeth model generation) based on data mining techniques and algorithms. After the main talk, Boyan would turn to the audience for ideas for solving the main challenge with the new product - Rebel. Dental - automation of mapping of 3D teeth models.
Crowdsourced hedge funds
The focus of this presentation is about the emerging opportunities for any data scientist to benefit from crowd-sourced hedge funds.
Wavelet analysis of financial datasets
The document discusses using wavelet analysis techniques to identify hidden patterns in financial time series data. It presents the wavelet transform approach for filtering time series data and decomposing it into different time scales. Several examples are given showing how wavelet analysis can be used to study structural patterns in datasets like NASDAQ and for momentum-based trading strategies.
Viewers also liked (7)
Real-time information analysis: social networks and open data
Real-time information analysis: social networks and open data
Tweeting beyond Facts – The Need for a Linguistic Perspective
Tweeting beyond Facts – The Need for a Linguistic Perspective
Computer vision and image processing for dental products
Computer vision and image processing for dental products
Similar to DBPedia-past-present-future
DBpedia past, present & future
1) DBpedia started in 2007 when Sören Auer extracted infobox data from Wikipedia pages into RDF triples and collaborated with others to publish the first version.
2) DBpedia has grown significantly since then and the 2014 version contains over 4.5 million entities and 583 million triples extracted from over 100 languages.
3) For DBpedia to continue evolving, areas of focus include fusing data from different sources, validating information, using natural language processing to extract more from Wikipedia text, and developing enterprise solutions to integrate DBpedia knowledge graphs.
The web of interlinked data and knowledge stripped
Linked Data approaches can help solve enterprise information integration (EII) challenges by complementing text on web pages with structured, linked open data from different sources. This allows for intelligently combining, integrating, and joining structured information across heterogeneous systems. A distributed, iterative, bottom-up integration approach using Linked Data may help solve the EII problem in large companies by taking a pay-as-you-go approach.
Decentralized Data Management for the Semantic Web
My HDR Talk about distributed semantic wikis, linked data integration and deep web, federated SPARQL query processing with replicated fragments
Irish Digital Libraries Summit
This is a full stack of slides from the first edition of the Irish Digital Libraries Summit organized by DERI Galway (Apr 20th, 2007)
The Europeana Strategy and Linked Open Data
The document discusses Europeana's strategy for 2015-2020 and how linked open data and linked open data technologies will help realize this strategy. Key points:
- Europeana's strategy is to transition from metadata to graphs and from strings to things by making data and APIs more linked and open.
- Linked open data allows data from different sources to be combined and helps make content more findable on search engines and in knowledge panels.
- Europeana labs provides APIs, tools, documentation and data to help partners publish linked open data that can be reused in the Europeana portal and other applications.
Paul houle resume
I'm an expert on building commerical large scale systems based on Linked Data sources such as Freebase and DBpedia. I'm the creator of :BaseKB, which was the first correct conversion of Freebase to RDF and of Infovore, the open source
I do consulting on the following areas:
* Data processing with Hadoop and the design and construction of systems using Amazon Web Services
* Architecture and construction of systems that consume and produce Linked Data
* Construction and evaluation of intelligent systems that make subjective decisions (text search, text classification, machine learning, etc.)
I'm not at all interested in doing maintenance work on other people's code, but I am interested in helping you align your process, structure, and tools to speed up your development cycle, improve your products, and prevent developer burnout. I am not free to relocate at this time, but I collaborate all of the time with workers around the world and I can travel to your location, understand your needs and transfer skills to your workforce.
IFLA LIDASIG Open Session 2017: Introduction to Linked Data
At the IFLA Linked Data Special Interest Group open session in Wroclaw we briefly introduced the mission of the SIG and then went on to a brief introduction to what linked data is and why that topic is important to libraries.
The presentation was held jointly by Astrid Verheusen (general introduction to the SIG) and Lars G. Svensson (introduction to Linked Data)
Semantic Web in Action
The document provides an overview of the work done at DERI Galway, including developing technologies like SIOC, ActiveRDF, and BrowseRDF to interconnect online communities and enable semantic applications. It also describes JeromeDL, a digital library system that uses semantic metadata and services to allow users to collaboratively browse and share knowledge.
MarcOnt Initiative - Protege meeting
The MarcOnt Initiative aims to:
1) Develop tools for collaborative ontology development, including a portal for editing ontologies and mediation services for translating between formats.
2) Create a bibliographic ontology called MarcOnt that captures concepts from legacy formats to improve interoperability between digital libraries.
3) Enable domain experts to improve the ontology through collaboration and knowledge sharing using the provided tools.
Geo-annotations in Semantic Digital Libraries
The document discusses using geo-annotations and ontologies in digital libraries. It describes JeromeDL, a social semantic digital library that allows users to collaboratively annotate resources with metadata like geotags. It also describes the MarcOnt initiative which aims to develop tools for a collaborative ontology about bibliographic resources to improve interoperability between digital libraries and enable semantic search.
DLF 2017 - Introducing: Wikidata For Digital Preservation
Slides From a Collaborative Research Presentation Given at the Digital Library Federation Forum '17
https://forum2017.diglib.org/
Liferay and Big Data
1. The document discusses big data concepts, architectures, and applications. It provides an overview of common big data storage models like HDFS and NoSQL databases.
2. It also describes computation frameworks like Apache Hadoop MapReduce and shows an example of how MapReduce processing works.
3. Finally, the document discusses popular big data use cases for organizations like recommendation systems, predictive analytics, sentiment analysis, and more to provide insights from large and diverse datasets.
Tech WG report 2011
The report of technical working group given to the DataCite General Assembly 2001 by the tech led Ed Zukowski
Presentation open source library
The document summarizes the Meadville Public Library's transition to using open source software over time. It describes how the library started using open source tools like Linux routers and OpenBSD in the late 1990s and 2000s. It then migrated its integrated library system to Koha in the mid-2000s. The library also developed its own open source kiosk management software called Libki. The document outlines many common open source tools used by libraries and the benefits of using open source software, such as cost savings and community support.
Open Source Library Webinar
This webinar will cover specific open source tools (some of which you may not have heard of before!) that work well for libraries and the benefits and challenges associated with their use. Meadville Public Library uses open source software on 90% of their public access computers.
Cindy Murdock Ames, IT Services Director and Kyle Hall, the library's on-staff developer, will share recommendations for libraries considering open source software and how to get started successfully. Cindy has been using open source software for over 10 years, which has allowed the library to save licensing costs and have more control over its computing environment. The library uses open source tools for their websites, e-mail, Internet firewall, wireless router, proxying, filtering, and productivity software. They use thin clients for Internet access and Koha for the circulation and public catalogs.
TechSoup An Open Source Library Story
This presentation is from a TechSoup webinar. You can view the archive page (https://cc.readytalk.com/cc/schedule/display.do?udc=peinch14k2ix) for a recording and links to all of the many open source tools that were discussed. We have a lively conversation on our community forum (http://bit.ly/oslib) as Cindy and Kyle answered questions we didn't get to during the webinar.
Multi-model database
This document discusses a presentation on multi-model data management. It introduces the topic of multi-model databases that can support multiple data models like document, graph, relational, and key-value models through a single integrated backend. The presentation covers topics like multi-model data storage, query languages, optimization, benchmarking, and open problems. Examples of multi-model databases like ArangoDB and OrientDB are provided.
Overview AG AKSW
The research group Agile Knowledge Engineering & Semantic Web (AKSW) was founded in 2006 and is now part of the Institute for Applied Informatics at the University of Leipzig. The AKSW aims to advance semantic web, knowledge engineering, and software engineering science and also bridges the gap between research results and applications. The AKSW team actively works on several funded projects involving knowledge management, semantic collaboration platforms, and applying semantic web technologies to applications like tourism information and requirements engineering.
The Standards Mosaic Opening the Way to New Technologies
Presents the mosaic of XML and linked data standards that can support the integration of future natural language technology for the localisation industry
ElasticSearch - index server used as a document database
Presentation held on 5.10.2014 on http://2014.webcampzg.org/talks/.
Although ElasticSearch (ES) primary purpose is to be used as index/search server, in its featureset ES overlaps with common NoSql database; better to say, document database.
Why this could be interesting and how this could be used effectively?
Talk overview:
- ES - history, background, philosophy, featureset overview, focus on indexing/search features
- short presentation on how to get started - installation, indexing and search/retrieving
- Database should provide following functions: store, search, retrieve -> differences between relational, document and search databases
- it is not unusual to use ES additionally as an document database (store and retrieve)
- an use-case will be presented where ES can be used as a single database in the system (benefits and drawbacks)
- what if a relational database is introduced in previosly demonstrated system (benefits and drawbacks)
ES is a nice and in reality ready-to-use example that can change perspective of development of some type of software systems.
Similar to DBPedia-past-present-future (20)
The web of interlinked data and knowledge stripped
The web of interlinked data and knowledge stripped
Decentralized Data Management for the Semantic Web
Decentralized Data Management for the Semantic Web
IFLA LIDASIG Open Session 2017: Introduction to Linked Data
IFLA LIDASIG Open Session 2017: Introduction to Linked Data
DLF 2017 - Introducing: Wikidata For Digital Preservation
DLF 2017 - Introducing: Wikidata For Digital Preservation
The Standards Mosaic Opening the Way to New Technologies
The Standards Mosaic Opening the Way to New Technologies
ElasticSearch - index server used as a document database
ElasticSearch - index server used as a document database
More from Data Science Society
[Data Meetup] Data Science in Finance - Factor Models in Finance
In this talk Metodi Nikolov, a Quantitative Researcher, is reviewing, without being exhaustive, the usage of factor models in finance – from the simplest single factor linear regression models, through latent variables and beyond. The focus was not be put solely on stocks but rather, on exploring other data types. The hope is to give the listeners an appreciation for the different ways the models can be applied.
[Data Meetup] Data Science in Finance - Building a Quant ML pipeline
Georgi Kirov shares a common market-neutral statistical arbitrage framework. It will help showcase the many different ways to structure a systematic research project. From data reconciliation and signal backtesting to optimization and execution, what are some principled ways to evaluate and compare ML ideas? This process inevitably depends on the characteristics of a specific strategy, for instance, if it is liquidity-taking or liquidity-making.
[Data Meetup] Data Science in Journalism - Tanbih, QCRI and MIT
Check out our Data Science Meetup devoted to Data Science in Journalism
Dr. Preslav Nakov, Principal Scientist at QCRI, presented the #Tanbih news aggregator, which makes people aware of what they are reading.
The aggregator features media profiles that show the general factuality of reporting, the degree of propagandistic content, the hyper-partisanship, the leading political ideology, the general frame of reporting, the stance with respect to various claims and topics, as well as the audience reach and the audience bias in social media. This is part of the Tanbih project, which is developed in collaboration with MIT.
Special thanks to our partners from #Ontotext, #Telelink and #Leanplum!
#DSS #DataMeetup
Computer Vision in Real Estate
Vassil Lunchev, CEO of Homeheed (https://www.homeheed.com/) presented at our July Meetup how to detect fake listings using #ComputerVision and #MachineLearning.
Imagine that you have 600,000 real estate listings with a total of 5,000,000 photos. What you know is that many of these listings are fake and some of the challenges Vassil shared in his presentation how you can detect the fake ones including the approach that works, and those which do not. Apart from that, he presented what kind of additional data is necessary to detect the fake ones.
ML in Proptech - Concept to Production
Boyan Bonev and Demir Tonchev from Gaida.AI covered the process from the very initial concept to working software. The focus of their talk at our July Meetup was put on the challenges the domain of real estate presents for some of the standard approaches and models (#CollaborativeFiltering).
In the presentation, you can find information about all the way from #DataExploration and #modeling to the nitty-gritty of getting it all up and running in a production environment.
Demir and Boyan shared lessons they learned, mistakes they made and things they are still looking to improve in Gaida.AI (https://www.gaida.ai/).
Lessons Learned: Linked Open Data implemented in 2 Use Cases
In this presentation for the ESSnet Linked Open Statistics final event, Sergi Segiev is presenting the learned lessons from two implemented use cases with open data for finding valuable insights.
You can also refer to the presentation 'Data Reveals Corruption Practices' by Yasen Kiprov - http://bit.ly/2WsFxsP
AI methods for localization in noisy environment
The presentation with the topic AI methods for localization in a noisy environment, held by Ana Antonova and Kameliya Kosekova, was introduced at Robotics Days '19.
In the next slides, you can find information techniques for Robot localization in more details and several GitHub Repos on the topic.
Object Identification and Detection Hackathon Solution
Team Nishki consisted of 11th-graders is presenting a Hackathon ML solution to a Kaufland Airmap case for which they won a Datathon special award.
Used methodologies and algorithms: OCR, DarkFlow, YOLO
The solution can be found at:
https://www.datasciencesociety.net/datathon/kaufland-case-datathon-2019/
Team: Evgeni Dimov, Kalin Doichev, Kostadin Kostadinov and Aneta Tsvetkova
Data Science for Open Innovation in SMEs and Large Corporations
Latest trends in Data Science and why the open-source culture and open innovation is expanding so fast. Find more about Data Science Society, its latest activities and how they cooperate with different local communities around the world for stimulating the new forms of education. At the end of the presentation, there are the results of two business cases from a telecom company (SNA) and a German retailer (object detection), which were solved during the Data Science Society’s hackathons (Global Datathons).
Air Pollution in Sofia - Solution through Data Science by Kiwi team
Some of you have already know how serious is the problem with air pollution in the capital of Bulgaria, Sofia but ...
▶️Do you know how it could be solved?
Our community represented by 1800 members all around the world tried to tackle the issue at our previous #GlobalDatathon and our international #DataScience #MonthlyChallenge, part of a university program.
Team Kiwi is solving the problem by implementing algorithms and statistical methods for air pollution prediction in the next 24 hours.
Machine Learning in Astrophysics
This document discusses using machine learning for light curve analysis in astrophysics. Specifically, it discusses using supervised learning on light curve data to detect exoplanets via the transit method. A deep neural network can be trained on light curve data to distinguish true planetary transits from other events like eclipsing binaries or stellar variability. This trained model was then used to analyze new candidates from the Kepler space telescope, resulting in the confirmation of planets like Kepler 80 g and the eight planet system around Kepler 90.
#AcademiaDatathon Finlists' Solution of Crypto Datathon Case
Team UNWE, one of the finalists from #AcademiaDatathon will present their solution to the #cryptocurrency data case. Explore how to perform data modeling with ARIMA and Neural Network.
To learn more visit: https://bit.ly/2uhfF37
Video from the presentation: https://bit.ly/2LlaeYd
Coreference Extraction from Identric’s Documents - Solution of Datathon 2018
The whole NLP Data Science solution @ https://goo.gl/iEFb1L
Syntactic Parsing or Dependency Parsing is the task of recognizing a sentence and assigning a syntactic structure to it. The most widely used syntactic structure is the parse tree which can be generated using some parsing algorithms. These parse trees are useful in various applications like grammar checking or more importantly it plays a critical role in the semantic analysis stage. For example to answer the question “Who is the point guard for the LA Laker in the next game ?” we need to figure out its subject, objects, attributes to help us figure out that the user wants the point guard of the LA Lakers specifically for the next game. This was mostly the identification and extraction NLP task for team Coala at the First Global Online Datathon.
DNA Analytics - What does really goes into Sausages - Datathon2018 Solution
Link to whole Data Science solution: https://goo.gl/nY3iuE
The task for the Telelink case of the First Global Datathon 2018 is to obtain the complete set of genome traces found in a single food sample and ALL organisms that should not be found in the food sample. The business needs a solution to this DNA Sequence identification case for improved quality control to be utilized in supply chains supervision and health care and protection.
- by Polina Krustanova
Relationships between research tasks and data structure (basic methods and a...
This document discusses various statistical methods for analyzing different data structures, including descriptive statistics, segmentation, dimension reduction, and measures of association. For each statistical method, it provides the appropriate data scale and examples applying the method to real world datasets. Statistical methods are matched to the type of research task and data structure, whether involving only variables, dependent and independent variables, or time series analysis. Pros and cons are outlined for several predictive analytic techniques.
Data science tools - A.Marchev and K.Haralampiev
Video:https://www.youtube.com/watch?v=ZheT2yv_QsA
For more information:
http://datasciencesociety.net/
https://ask.datasciencesociety.net/
Problems of Application of Machine Learning in the CRM - panel
Video: https://www.youtube.com/watch?v=LHA19ZAlv9I
For more information:
http://datasciencesociety.net/
https://ask.datasciencesociety.net/
Disruptive as Usual: New Technologies and Data Value Professor Severino Mereg...
Disruptive as Usual: New Technologies and Data Value Professor Severino Mereg...Data Science Society
Video: https://www.youtube.com/watch?v=7-u2c8bFOgg
For more information:
http://datasciencesociety.net/
https://ask.datasciencesociety.net/Intelligent Question Answering Using the Wisdom of the Crowd, Preslav Nakov
Video: https://www.youtube.com/watch?v=fTAA9JXazUY
For more information:
http://datasciencesociety.net/
https://ask.datasciencesociety.net/
Master class Hristo Hadjitchonev - Aubg
Video: https://www.youtube.com/watch?v=onmF8SRLvVA
For more information:
http://datasciencesociety.net/
https://ask.datasciencesociety.net/
More from Data Science Society (20)
[Data Meetup] Data Science in Finance - Factor Models in Finance
[Data Meetup] Data Science in Finance - Factor Models in Finance
[Data Meetup] Data Science in Finance - Building a Quant ML pipeline
[Data Meetup] Data Science in Finance - Building a Quant ML pipeline
[Data Meetup] Data Science in Journalism - Tanbih, QCRI and MIT
[Data Meetup] Data Science in Journalism - Tanbih, QCRI and MIT
Lessons Learned: Linked Open Data implemented in 2 Use Cases
Lessons Learned: Linked Open Data implemented in 2 Use Cases
Object Identification and Detection Hackathon Solution
Object Identification and Detection Hackathon Solution
Data Science for Open Innovation in SMEs and Large Corporations
Data Science for Open Innovation in SMEs and Large Corporations
Air Pollution in Sofia - Solution through Data Science by Kiwi team
Air Pollution in Sofia - Solution through Data Science by Kiwi team
#AcademiaDatathon Finlists' Solution of Crypto Datathon Case
#AcademiaDatathon Finlists' Solution of Crypto Datathon Case
Coreference Extraction from Identric’s Documents - Solution of Datathon 2018
Coreference Extraction from Identric’s Documents - Solution of Datathon 2018
DNA Analytics - What does really goes into Sausages - Datathon2018 Solution
DNA Analytics - What does really goes into Sausages - Datathon2018 Solution
Relationships between research tasks and data structure (basic methods and a...
Relationships between research tasks and data structure (basic methods and a...
Problems of Application of Machine Learning in the CRM - panel
Problems of Application of Machine Learning in the CRM - panel
Disruptive as Usual: New Technologies and Data Value Professor Severino Mereg...
Disruptive as Usual: New Technologies and Data Value Professor Severino Mereg...
Intelligent Question Answering Using the Wisdom of the Crowd, Preslav Nakov
Intelligent Question Answering Using the Wisdom of the Crowd, Preslav Nakov
Recently uploaded
一比一原版爱尔兰都柏林大学毕业证(本硕）ucd学位证书如何办理
原版一模一样【微信：741003700 】【爱尔兰都柏林大学毕业证(本硕）ucd成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
PyData London 2024: Mistakes were made (Dr. Rebecca Bilbro)
To honor ten years of PyData London, join Dr. Rebecca Bilbro as she takes us back in time to reflect on a little over ten years working as a data scientist. One of the many renegade PhDs who joined the fledgling field of data science of the 2010's, Rebecca will share lessons learned the hard way, often from watching data science projects go sideways and learning to fix broken things. Through the lens of these canon events, she'll identify some of the anti-patterns and red flags she's learned to steer around.
Open Source Contributions to Postgres: The Basics POSETTE 2024
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
原版办【微信号:BYZS866】【兰加拉学院毕业证(Langara毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
Gran libro. Lo recomiendo
一比一原版加拿大渥太华大学毕业证（uottawa毕业证书）如何办理
原版一模一样【微信：741003700 】【渥太华大学毕业证（uottawa毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
How To Control IO Usage using Resource Manager
How To Control IO Usage using Resource Manager
In Multitenant and Exadata architecture
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
一比一原版加拿大麦吉尔大学毕业证（mcgill毕业证书）如何办理
原版一模一样【微信：741003700 】【加拿大麦吉尔大学毕业证（mcgill毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版澳洲西澳大学毕业证（uwa毕业证书）如何办理
原版一模一样【微信：741003700 】【澳洲西澳大学毕业证（uwa毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理澳洲西澳大学毕业证（uwa毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理澳洲西澳大学毕业证（uwa毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理澳洲西澳大学毕业证（uwa毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理澳洲西澳大学毕业证（uwa毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版悉尼大学毕业证如何办理
原版一模一样【微信：741003700 】【悉尼大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理悉尼大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理悉尼大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理悉尼大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理悉尼大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
社内勉強会資料_Hallucination of LLMs .
社内エンジニア・リサーチャー勉強会の発表資料「Hallucination of LLMs」を公開しました！
大規模言語モデル(LLM)に起こるハルシネーションのメカニズムと、それらを軽減する方法について紹介しています。
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
原版一模一样【微信：741003700 】【(uom毕业证书)曼彻斯特大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(uom毕业证书)曼彻斯特大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uom毕业证书)曼彻斯特大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uom毕业证书)曼彻斯特大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uom毕业证书)曼彻斯特大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Recently uploaded (20)
A gentle exploration of Retrieval Augmented Generation
A gentle exploration of Retrieval Augmented Generation
PyData London 2024: Mistakes were made (Dr. Rebecca Bilbro)
PyData London 2024: Mistakes were made (Dr. Rebecca Bilbro)
Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024
Econ3060_Screen Time and Success_ final_GroupProject.pdf
Econ3060_Screen Time and Success_ final_GroupProject.pdf
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
Digital Marketing Performance Marketing Sample .pdf
Digital Marketing Performance Marketing Sample .pdf
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
Namma-Kalvi-11th-Physics-Study-Material-Unit-1-EM-221086.pdf
Namma-Kalvi-11th-Physics-Study-Material-Unit-1-EM-221086.pdf
DBPedia-past-present-future
- 1. past, present & future
- 3. Get me all soccer players, who played as goalkeeper for a club that has a stadium with more than 40.000 seats and who are born in a country with more than 10 million inhabitants
- 6. How it all started - 2006 - Sören Auer (busy with his PhD) asking people: “Wikipedia fact tables look like triples, don’t you want to write some extractor?” - 6 months later: Sören wrote the extractor himself and asked Jens Lehmann to help with writing a paper - Chris Bizer : “We are extracting people and place information from Wikipedia too – lets join efforts and call it DBpedia.” - Kingsley Idehen: “I need a showcase for my Virtuoso triple store.”
- 8. Taking a closer look at heterogeneity… - DBpedia Mappings wiki
- 9. Milestones - 2008: DBpedia Live - 2009: Scala-Based framework - 2009: Mappings wiki - 2011: Internationalization - 2011: DBpedia Spotlight - 2014: DBpedia Association
- 10. Now DBpedia 2014 (English): 4.58 mio. entities and 583 mio. triples 131,2 mio. fact assertions (derived from infoboxes) 168,5 mio. triples representing Wikipedia structure 57,1 mio. links to external datasets Localized DBpedia version for 125 languages, built from corresponding Wikipedia versions 12 DBpedia language chapters
- 11. Now X
- 13. DBpedia has to evolve - Fusion - Validation - NLP - Enterprize
- 14. Fusion
- 15. Validation
- 16. NLP - Exploit the text… - Let different NLP tools & approaches compete for the best quality (in a certain language) - Need to define the interface (help needed)
- 17. Every Enterprise needs its DBpedia - Represent common sense knowledge (DBpedia and other LOD datasets) as well as the specific enterprise knowledge - Crystallization points for Linked Data intranets – an addition to SOA facilitating enterprise-wide data linking & integration - Slicing & Dicing
- 18. Other ideas?
- 20. The soccer players (for the curious)
- 21. Thank you Big thanks to Sören Auer & Markus Ackermann for slide contributions
- 22. Big thanks to Sören Auer & Markus Ackermann for slide contributions