The document provides an overview of knowledge graphs and introduces metaphactory, a knowledge graph platform. It discusses what knowledge graphs are, examples like Wikidata, and standards like RDF. It also outlines an agenda for a hands-on session on loading sample data into metaphactory and exploring a knowledge graph.
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
The slideset used to conduct an introduction/tutorial
on DBpedia use cases, concepts and implementation
aspects held during the DBpedia community meeting
in Dublin on the 9th of February 2015.
(slide creators: M. Ackermann, M. Freudenberg
additional presenter: Ali Ismayilov)
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
The slideset used to conduct an introduction/tutorial
on DBpedia use cases, concepts and implementation
aspects held during the DBpedia community meeting
in Dublin on the 9th of February 2015.
(slide creators: M. Ackermann, M. Freudenberg
additional presenter: Ali Ismayilov)
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
Dmitry Kan, Principal AI Scientist at Silo AI and host of the Vector Podcast [1], will give an overview of the landscape of vector search databases and their role in NLP, along with the latest news and his view on the future of vector search. Further, he will share how he and his team participated in the Billion-Scale Approximate Nearest Neighbor Challenge and improved recall by 12% over a baseline FAISS.
Presented at https://www.meetup.com/open-nlp-meetup/events/282678520/
YouTube: https://www.youtube.com/watch?v=RM0uuMiqO8s&t=179s
Follow Vector Podcast to stay up to date on this topic: https://www.youtube.com/@VectorPodcast
Data Catalog in Denodo Platform 7.0: Creating a Data Marketplace with Data Vi...Denodo
Watch Alberto's session from Fast Data Strategy on-demand here: https://buff.ly/2wByS41
Gartner’s recently published report “Data Catalogs Are the New Black in Data Management Analytics” emphasizes the importance of data catalogs.
Watch this session to learn more about:
• The vision behind the Denodo Data Catalog
• How to maximize information value with the Denodo Data Catalog
• Why it is essential to combine data delivery with a data catalog
Introduction to DBpedia, the most popular and interconnected source of Linked Open Data. Part of EXPLORING WIKIDATA AND THE SEMANTIC WEB FOR LIBRARIES at METRO http://metro.org/events/598/
Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016)
Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
What’s New with Databricks Machine LearningDatabricks
In this session, the Databricks product team provides a deeper dive into the machine learning announcements. Join us for a detailed demo that gives you insights into the latest innovations that simplify the ML lifecycle — from preparing data, discovering features, and training and managing models in production.
This workshop presentation from Enterprise Knowledge team members Joe Hilger, Founder and COO, and Sara Nash, Technical Analyst, was delivered on June 8, 2020 as part of the Data Summit 2020 virtual conference. The 3-hour workshop provided an interdisciplinary group of participants with a definition of what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your organization’s datas. This slide deck gives an overview of the KM concepts that are necessary for the implementation of knowledge graphs as a foundation for Enterprise Artificial Intelligence (AI). Hilger and Nash also outlined four use cases for knowledge graphs, including recommendation engines and natural language query on structured data.
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
Technologists have increasingly tossed around complicated terminology for organizing information. This a translation for less technical business people.
Organizing information in various structures help discoverability and understand-ability. Obscuring the techniques with unnecessarily complex terminology is counter to this goal .
This uses familiar metaphors like the Dewey Decimal System to help take the mystery out of these terms.
This presentation also breaks down the concepts regarding which ones are more, or less, flexible and abstract. This helps users understand the advantages of the various information classification approaches.
Data products derive their value from data and generate new data in return; as a result, machine learning techniques must be applied to their architecture and their development. Machine learning fits models to make predictions on unknown inputs and must be generalizable and adaptable. As such, fitted models cannot exist in isolation; they must be operationalized and user facing so that applications can benefit from the new data, respond to it, and feed it back into the data product. Data product architectures are therefore life cycles and understanding the data product lifecycle will enable architects to develop robust, failure free workflows and applications. In this talk we will discuss the data product life cycle, explore how to engage a model build, evaluation, and selection phase with an operation and interaction phase. Following the lambda architecture, we will investigate wrapping a central computational store for speed and querying, as well as incorporating a discussion of monitoring, management, and data exploration for hypothesis driven development. From web applications to big data appliances; this architecture serves as a blueprint for handling data services of all sizes!
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
stackconf 2022: Introduction to Vector Search with WeaviateNETWAYS
In machine learning, e.g., recommendation tools or data classification, data is often represented as high-dimensional vectors. These vectors are stored in so-called vector databases. With vector databases you can efficiently run searching, ranking and recommendation algorithms. Therefore, vector databases became the backbone of ML deployments in industry. This session is all about vector databases. If you are a data scientist or data/software engineer this session would be interesting for you. You will learn how you can easily run your favourite ML models with the vector database Weaviate. You will get an overview of what a vector database like Weaviate can offer: such as semantic search, question answering, data classification, named entity recognition, multimodal search, and much more. After this session, you are able to load in your own data and query it with your preferred ML model!
Session outline
What is a vector database?
You will learn the basic principles of vector databases. How data is stored, retrieved, and how that differs from other database types (SQL, knowledge graphs, etc).
Performing your first semantic search with the vector database Weaviate.
In this phase, you will learn how to set up a Weaviate vector database, how to make a data schema, how to load in data, and how to query data. You can follow along with examples, or you can use your own dataset.
Advanced search with the vector database Weaviate.
Finally, we will cover other functionalities of Weaviate: multi-modal search, data classification, connecting custom ML models, etc.
DAS Slides: Data Governance and Data Architecture – Alignment and SynergiesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, Data Governance consists of committee meetings and stewardship roles. To others, it focuses on technical Data Management and controls. Holistic Data Governance combines both of these aspects, and a robust Data Architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning Data Architecture and Data Governance for business and IT success.
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
Dmitry Kan, Principal AI Scientist at Silo AI and host of the Vector Podcast [1], will give an overview of the landscape of vector search databases and their role in NLP, along with the latest news and his view on the future of vector search. Further, he will share how he and his team participated in the Billion-Scale Approximate Nearest Neighbor Challenge and improved recall by 12% over a baseline FAISS.
Presented at https://www.meetup.com/open-nlp-meetup/events/282678520/
YouTube: https://www.youtube.com/watch?v=RM0uuMiqO8s&t=179s
Follow Vector Podcast to stay up to date on this topic: https://www.youtube.com/@VectorPodcast
Data Catalog in Denodo Platform 7.0: Creating a Data Marketplace with Data Vi...Denodo
Watch Alberto's session from Fast Data Strategy on-demand here: https://buff.ly/2wByS41
Gartner’s recently published report “Data Catalogs Are the New Black in Data Management Analytics” emphasizes the importance of data catalogs.
Watch this session to learn more about:
• The vision behind the Denodo Data Catalog
• How to maximize information value with the Denodo Data Catalog
• Why it is essential to combine data delivery with a data catalog
Introduction to DBpedia, the most popular and interconnected source of Linked Open Data. Part of EXPLORING WIKIDATA AND THE SEMANTIC WEB FOR LIBRARIES at METRO http://metro.org/events/598/
Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016)
Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
What’s New with Databricks Machine LearningDatabricks
In this session, the Databricks product team provides a deeper dive into the machine learning announcements. Join us for a detailed demo that gives you insights into the latest innovations that simplify the ML lifecycle — from preparing data, discovering features, and training and managing models in production.
This workshop presentation from Enterprise Knowledge team members Joe Hilger, Founder and COO, and Sara Nash, Technical Analyst, was delivered on June 8, 2020 as part of the Data Summit 2020 virtual conference. The 3-hour workshop provided an interdisciplinary group of participants with a definition of what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your organization’s datas. This slide deck gives an overview of the KM concepts that are necessary for the implementation of knowledge graphs as a foundation for Enterprise Artificial Intelligence (AI). Hilger and Nash also outlined four use cases for knowledge graphs, including recommendation engines and natural language query on structured data.
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
Technologists have increasingly tossed around complicated terminology for organizing information. This a translation for less technical business people.
Organizing information in various structures help discoverability and understand-ability. Obscuring the techniques with unnecessarily complex terminology is counter to this goal .
This uses familiar metaphors like the Dewey Decimal System to help take the mystery out of these terms.
This presentation also breaks down the concepts regarding which ones are more, or less, flexible and abstract. This helps users understand the advantages of the various information classification approaches.
Data products derive their value from data and generate new data in return; as a result, machine learning techniques must be applied to their architecture and their development. Machine learning fits models to make predictions on unknown inputs and must be generalizable and adaptable. As such, fitted models cannot exist in isolation; they must be operationalized and user facing so that applications can benefit from the new data, respond to it, and feed it back into the data product. Data product architectures are therefore life cycles and understanding the data product lifecycle will enable architects to develop robust, failure free workflows and applications. In this talk we will discuss the data product life cycle, explore how to engage a model build, evaluation, and selection phase with an operation and interaction phase. Following the lambda architecture, we will investigate wrapping a central computational store for speed and querying, as well as incorporating a discussion of monitoring, management, and data exploration for hypothesis driven development. From web applications to big data appliances; this architecture serves as a blueprint for handling data services of all sizes!
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
stackconf 2022: Introduction to Vector Search with WeaviateNETWAYS
In machine learning, e.g., recommendation tools or data classification, data is often represented as high-dimensional vectors. These vectors are stored in so-called vector databases. With vector databases you can efficiently run searching, ranking and recommendation algorithms. Therefore, vector databases became the backbone of ML deployments in industry. This session is all about vector databases. If you are a data scientist or data/software engineer this session would be interesting for you. You will learn how you can easily run your favourite ML models with the vector database Weaviate. You will get an overview of what a vector database like Weaviate can offer: such as semantic search, question answering, data classification, named entity recognition, multimodal search, and much more. After this session, you are able to load in your own data and query it with your preferred ML model!
Session outline
What is a vector database?
You will learn the basic principles of vector databases. How data is stored, retrieved, and how that differs from other database types (SQL, knowledge graphs, etc).
Performing your first semantic search with the vector database Weaviate.
In this phase, you will learn how to set up a Weaviate vector database, how to make a data schema, how to load in data, and how to query data. You can follow along with examples, or you can use your own dataset.
Advanced search with the vector database Weaviate.
Finally, we will cover other functionalities of Weaviate: multi-modal search, data classification, connecting custom ML models, etc.
DAS Slides: Data Governance and Data Architecture – Alignment and SynergiesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, Data Governance consists of committee meetings and stewardship roles. To others, it focuses on technical Data Management and controls. Holistic Data Governance combines both of these aspects, and a robust Data Architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning Data Architecture and Data Governance for business and IT success.
Enabling better science - Results and vision of the OpenAIRE infrastructure a...Paolo Manghi
Enabling better science: presentation on the results and vision of the OpenAIRE infrastructure and RDA Publishing Data Services Working Group in this direction.
This paper surveys the landscape of linked open data projects in cultural heritage, exam- ining the work of groups from around the world. Traditionally, linked open data has been ranked using the five star method proposed by Tim Berners-Lee. We found this ranking to be lacking when evaluating how cultural heritage groups not merely develop linked open datasets, but find ways to used linked data to augment user experience. Building on the five-star method, we developed a six-stage life cycle describing both dataset development and dataset usage. We use this framework to describe and evaluate fifteen linked open data projects in the realm of cultural heritage.
Publishing the British National Bibliography as Linked Open Data / Corine Del...CIGScotland
Presented at Linked Open Data: current practice in libraries and archives (Cataloguing & Indexing Group in Scotlland 3rd Linked Open Data Conference), Edinburgh, 18 Nov 2013
Lunch talk at the Centre for Digital Humanities by Laurents Sesink, Peter Verhaar and Ben Companjen on the implementation of IIIF by Leiden University Libraries.
Engaging Information Professionals in the Process of Authoritative Interlinki...Lucy McKenna
Through the use of Linked Data (LD), Libraries, Archives and Museums (LAMs) have the potential to expose their collections to a larger audience and to allow for more efficient user searches. Despite this, relatively few LAMs have invested in LD projects and the majority of these display limited interlinking across datasets and institutions. A survey was conducted to understand Information Professionals' (IPs') position with regards to LD, with a particular focus on the interlinking problem. The survey was completed by 185 librarians, archivists, metadata cataloguers and researchers. Results indicated that, when interlinking, IPs find the process of ontology and property selection to be particularly challenging, and LD tooling to be technologically complex and unsuitable for their needs.
Our research is focused on developing an authoritative interlinking framework for LAMs with a view to increasing IP engagement in the linking process. Our framework will provide a set of standards to facilitate IPs in the selection of link types, specifically when linking local resources to authorities. The framework will include guidelines for authority, ontology and property selection, and for adding provenance data. A user-interface will be developed which will direct IPs through the resource interlinking process as per our framework. Although there are existing tools in this domain, our framework differs in that it will be designed with the needs and expertise of IPs in mind. This will be achieved by involving IPs in the design and evaluation of the framework. A mock-up of the interface has already been tested and adjustments have been made based on results. We are currently working on developing a minimal viable product so as to allow for further testing of the framework. We will present our updated framework, interface, and proposed interlinking solutions.
Linked Open Data and The Digital Archaeological Workflow at the Swedish Natio...Marcus Smith
A presentation of two aspects of the linked open data work ongoing at the Swedish National Heritage Board (Riksantikvarieämbetet): Swedish Open Cultural Heritage (SOCH/K-samsök) and the Digital Archaeological Process (DAP).
Delivered at the Smithsonian, Washington, DC, 2014-11-10
OpenAIRE Guidelines for data providers: new Metadata Application Profile for ...OpenAIRE
Presentation at the "OpenAIRE webinar series for repository managers 2017/2018" - Nov. 14, 2017 (11h00 CET) | "OpenAIRE Guidelines for data providers: new Metadata Application Profile for Literature Repositories", presented by Jochen Schirrwagen, Univ. Bielefeld.
Similar to ESWC 2017 Tutorial Knowledge Graphs (20)
Visual Ontology Modeling for Domain Experts and Business Users with metaphactoryPeter Haase
Visual Ontology Modeling for Domain Experts and Business Users with metaphactory
Presentation at the OntoCommons Workshop on Ontology Engineering Tools @ Fri Mar 19, 2021
The Information Workbench - Linked Data and Semantic Wikis in the EnterprisePeter Haase
The Information Workbench is a platform for Linked Data applications in the enterprise. Targeting the full life-cycle of Linked Data applications, it facilitates the integration and processing of Linked Data following a Data-as-a-Service paradigm.
In this talk we present how we use Semantic Wiki technologies in the Information Workbench for the development of user interfaces for interacting with the Linked Data. The user interface can be easily customized using a large set of widgets for data integration, interactive visualization, exploration and analytics, as well as the collaborative acquisition and authoring of Linked Data. The talk will feature a live demo illustrating an example application, a Conference Explorer integrating data about the SMWCon conference, publications and social media.
We will also present solutions and applications of the Information Workbench in a variety of other domains, including the Life Sciences and Data Center Management.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
1. Getting Started with Knowledge Graphs
European Semantic Web Conference
May 29, 2017
Peter Haase
2. 2
Peter Haase
• Interest and experience in
ontologies, semantic
technologies and Linked Data
• PhD in KR and semantic
technologies
• 15 years in academic research
and software development
• Contributor to OWL 2
standard
metaphacts Company Facts
• Founded in Q4 2014
• Headquartered in Walldorf,
Germany
• Currently ~10 people
• Platform for knowledge graph
interaction & application
development
About the Speaker
3. 3
Introduction: What are Knowledge Graphs?
Examples and Applications
• Wikidata
• Cultural Heritage
• Industrial Applications
Standards and Principles
Knowledge Graph Management
Hands-on Exercises:
• metaphactory Knowledge Graph Platform
Agenda
8. 8
• We need a structured and formal representation of
knowledge
• We are surrounded by entities, which are connected by
relations
• Graphs are a natural way to represent entities and their
relationships
• Graphs can be managed efficiently
Why (Knowledge) Graphs?
9. 9
A (very small) Knowledge Graph
http://www.w3.org/TR/2014/NOTE-rdf11-primer-20140225/example-graph.jpg
10. 10
• Semantic descriptions of entities and their relationships
• Uses a knowledge representation formalism
(Focus here: RDF, RDF-Schema, OWL)
• Entities: real world objects (things, places, people) and
abstract concepts (genres, religions, professions)
• Relationships: graph-based data model where
relationships are first-class
• Semantic descriptions: types and properties with a well-
defined meaning (e.g. through an ontology)
• Possibly axiomatic knowledge (e.g. rules) to support
automated reasoning
What are Knowledge Graphs?
21. 21
Wikipedia page A query against Wikipedia
Query the Knowledge of Wikipedia like a Database
21
22. 22
• Collecting structured data. Unlike the
Wikipedias, which produce encyclopedic
articles, Wikidata collects data, in a
structured form.
• Collaborative. The data in Wikidata is
entered and maintained by Wikidata editors,
who decide on the rules of content creation
and management in Wikidata supporting the
notion of verifiability.
• Free. The data in Wikidata is published
under the Creative Commons
• Large.
• 26 million entities
• 150 million statements
• 130 million labels
• 350 languages
• >1500 million triples
Wikidata
26. 26
• Build your applications using Wikidata
• Free corpus of structured knowledge
• Easily accessible and standards-based
• See http://query.wikidata.org/
• Contextualize your enterprise data
• Wikidata provides stable identifiers into the open data world
• Seamless integration of private data with open data
• Enrich Wikidata with your data
• Contribute your data to Wikidata
• Link to your own data, make it visible
• Examples:
• Open biomedical databases – Wikidata as a central hub
• Cultural heritage
Use Cases for the Wikidata Knowledge Graph
30. 30
• Challenge:
• Very context-rich data
• Multi-disciplinary data, e.g. archaeologists, historians, librarians
• Multi-institutional data
• Complex domain, relationships, e.g. temporal, spatial, historical, political
• Benefits of Knowledge Graphs
• Integration and interchange of heterogeneous cultural heritage information
• Rich ontologies for knowledge representation
• Deep semantics for true conceptual merging
• Multi-lingual knowledge representation
• Knowledge access across museums and organizations
• Enabling knowledge sharing and collaboration
Benefits of Knowledge Graphs for Cultural Heritage
31. 31
• Collaboration environment for
researchers in Cultural Heritage
• Expert users: researchers, curators
• Based on CIDOC-CRM: very rich,
expressive ontology
• Large, cross-museum data sets
• E.g. British Museum: 100s millions of
triples
• Advanced search capabilities
• Supporting query construction
• Sharing of searches, results,
visualizations
• Knowledge sharing
• Discussions around cultural heritage
annotations
• Argumentation support:
Representation of conflicting views and
opionions
ResearchSpace: Knowledge Graphs for Cultural Heritage
http://researchspace.org/
34. 34
• Instance data (ground truth)
• Schema data (vocabularies,
ontologies)
• Metadata (e.g. provenance,
versioning, documentation
licensing)
• Comprehensive taxonomies to
categorize entities
• Links between internal and external
data
• Mappings to data stored in other
systems and databases
Multi-domain, source, granularity
Holistic Knowledge
35. 35
Semantics on the Web
Semantic Web Stack
Berners-Lee (2006)
Syntactic basis
Basic data model
Simple vocabulary
(schema) language
Expressive vocabulary
(ontology) language
Query language
Application specific
declarative-knowledge
Digital signatures,
recommendations
Proof generation,
exchange, validation
36. 36
Knowledge Graphs Built on the Semantic Web Layer Cake
Unicode URIs
RDF (Resource Description Framework)
RDF-Schema
OWLSKOS
SPARQL
Query language
Entities
Relationships
Vocabularies
Ontologies
Expressive Ontology
Language
Thesauri,
classification
schemes
Graph data
model
Simple vocabulary
language
38. 38
Graph consists of:
• Resources
(identified via
URIs)
• Literals: data
values with data
type (URI) or
language
(multilinguality
integrated)
• Attributes of
resources are
also URI-
identified (from
vocabularies)
Our Knowledge Graph again (a bit more technical)
• Various data sources and vocabularies can be arbitrarily mixed and meshed
• URIs can be shortened with namespace prefixes; e.g. schema: →
http://schema.org/
39. 39
Allows one to talk about anything
Uniform Resource Identifier (URI) can be used to identify entities
http://dbpedia.org/resource/Leonardo_da_Vinci
is a name for Leonardo da Vinci
http://www.wikidata.org/entity/Q12418
is a name for the Mona Lisa painting
Resource Description Framework (RDF)
40. 40
Allows one to express statements
An RDF statement consists of:
• Subject: resource identified by a URI
• Predicate: resource identified by a URI
• Object: resource or literal
Resource Description Framework (RDF)
42. 42
• Every set of RDF assertions can then be drawn and
manipulated as a (labelled directed) graph:
• Resources – the subjects and objects are nodes of the
graph.
• Predicates – each predicate use becomes a label for an arc,
connecting the subject to the object.
RDF Graphs
Subject Object
Predicate
44. 44
• Turtle is a syntax for RDF more readable.
• Since many URIs share same basis we use prefixes:
RDF Turtle
@prefix foaf: <http://xmlns.com/foaf/0.1/>.
@prefix xsd: <http://www.w3.org/2001/XMLSchema#>.
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix schema: <http://schema.org/>.
@prefix dcterms: <http://purl.org/dc/terms/>.
@prefix wd: <http://www.wikidata.org/entity/>.
45. 45
• Also has a simple shorthand for class membership:
@prefix foaf: <http://xmlns.com/foaf/0.1/>.
<http://example.org/bob#me> a foaf:Person .
Is equivalent to:
<http://example.org/bob#me>
<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>
<http://xmlns.com/foaf/0.1/Person>.
RDF Turtle
46. 46
• When multiple statements apply to same subject they can
be abbreviated as follows:
wd:Q12418 dcterms:title "Mona Lisa" ;
dcterms:creator <http://dbpedia.org/resource/Leonardo_da_Vinci> .
RDF Turtle
Same subject
48. 48
• Collections of defined relationships and classes of
resources.
• Classes group together similar resources.
• Terms from well-known vocabularies should be reused
wherever possible.
• New terms should be defined only if you can not find
required terms in existing vocabularies.
Describing Data: Vocabularies
49. 49
A set of well-known vocabularies has evolved in the Semantic
Web community. Some of them are:
Describing Data: Vocabularies
Vocabulary Description Classes and Relationships
Friend-of-a-Friend (FOAF) Vocabulary for describing
people.
foaf:Person, foaf:Agent, foaf:name,
foaf:knows, foaf:member
Dublin Core (DC) Defines general metadata
attributes.
dc:FileFormat, dc:MediaType,
dc:creator, dc:description
Organization Ontology (org) Publishing of organization
information.
org:Organisation, org:Site,
org:Role, org:member, org:hasSite
schema.org Cross-domain vocabulary for
annotation of Web pages.
schema:Event, schema:Product,
schema:location, schema:image
50. 50
• Language for two tasks w.r.t. the RDF data model:
• Definition of vocabulary – nominate:
• the ‘types’, i.e., classes, of things we might make assertions
about, and
• the properties we might apply, as predicates in these
assertions, to capture their relationships.
• Inference – given a set of assertions, using these classes
and properties, specify what should be inferred about
assertions that are implicitly made.
RDF-S – RDF Schema
51. 51
• rdfs:Class – Example:
foaf:Person – Represents the class of persons
• rdf:Property – Class of RDF properties. Example:
foaf:knows – Represents that a person “knows” another
• rdfs:domain – States that any resource that has a given property
is an instance of one or more classes
foaf:knows rdfs:domain foaf:Person
• rdfs:range – States that the values of a property are instances of
one or more classes
foaf:knows rdfs:range foaf:Person
RDF-S – RDF Schema
52. 52
RDF-S – RDF Schema
foaf:knows
rdfs:range
foaf:Person .
<http://example.org/bob#me>
foaf:knows
<http://example.org/alice#me>.
<http://example.org/alice#me>
rdf:type
foaf:Person.
Schema
Existing
fact
Inferred
fact
We expect to use this
vocabulary to make
assertions about persons.
Having made such an
assertion...
Inferences can be drawn that
we did not explicitly make
53. 53
1. Load foaf.rdf via
http://localhost:10214/resource/Admin:DataImportExport
2. Browse and explore FOAF vocabulary
Hands-on: Load FOAF Vocabulary
54. 54
• RDFS provides a simplified ontological language for
defining vocabularies about specific domains.
• OWL provides more ontological constructs for knowledge
representation.
• Semantics grounded in Description Logics.
• Most graph databases concentrate on the use of RDFS with
a subset of OWL features.
OWL – Web Ontology Language
55. 55
Extends the DL further, but has three more
computable fragments (profiles).
OWL 2.0 – Web Ontology Language 2.0
55
OWL 2 Full
• Used informally to refer to RDF graphs considered as OWL 2
ontologies and interpreted using the RDF-Based Semantics.
OWL 2 DL
• Used informally to refer to OWL 2 DL ontologies interpreted
using the Direct Semantics.
OWL 2 EL
• Limited to basic classification, but with polynomial-time
reasoning.
OWL 2 QL
• Designed to be translatable to relational database querying.
OWL 2 RL
• Designed to be efficiently implementable in rule-based systems.
OWL 2 Full
OWL 2 DL
OWL 2 EL
OWL 2 QL OWL 2 RL
56. 56
OWL is made up of terms which provide for:
• Class construction: forming new classes from
membership of existing ones (e.g., unionOf,
intersectionOf, etc.).
• Property construction: distinction between OWL
ObjectProperties (resources as values) and OWL
DatatypeProperties (literals as values).
• Class axioms: sub-class, equivalence and disjointness
relationships.
• Property axioms: sub-property relationship, equivalence
and disjointness, and relationships between properties.
• Individual axioms: statements about individuals
(sameIndividual, differentIndividuals).
OWL – Web Ontology Language
57. 57
Example: CIDOC-CRM Ontology
Class: Person
SubClassOf: Actor
SubClassOf: Biological Object
SubClassOf: was_born exactly 1
SubClassOf: has_parent min 2
Class: Physical Thing
SubClassOf: Legal Object
SubClassOf: Spacetime Volume
DisjointWith: Conceptual Object
SubClassOf: consists_of some Material
58. 58
1. Load ecrm_current.owl via
http://localhost:10214/resource/Admin:DataImportExport
2. Browse and explore CIDOC-CRM ontology
Hands-on: Load CIDOC-CRM Ontology
59. 59
• Data model for knowledge organization systems (thesauri,
classification scheme, taxonomies)
• Conceptual resources (concepts) can be
• identified with URIs,
• labeled with lexical strings in natural language,
• documented with various types of note,
• semantically related to each other in informal hierarchies
and association networks and
• aggregated into concept schemes.
SKOS - Simple Knowledge Organization System
http://www.w3.org/TR/skos-reference/
61. 61
• Query language for RDF-based knowledge graphs.
• Designed to use a syntax similar to SQL for retrieving data
from relational databases.
• Different query forms:
• SELECT returns variables and their bindings directly.
• CONSTRUCT returns a single RDF graph specified by a graph
template.
• ASK test whether or not a query pattern has a solution. Returns
yes/no.
• DESCRIBE returns a single RDF graph containing RDF data about
resources.
SPARQL – * Protocol and RDF Query Language
62. 62
Main idea: Pattern matching
• Queries describe sub-graphs of the queried graph
• Graph patterns are RDF graphs specified in Turtle syntax, which
contain variables (prefixed by either “?” or “$”)
• Sub-graphs that match the graph patterns yield a result
SPARQL – * Protocol and RDF Query Language
dbpedia:
Leonardo_da_Vinci
?var
dcterms:creator
63. 63
SPARQL – * Protocol and RDF Query Language
?interest
<http://example.org/
bob#me>
foaf:topic_interest
Results:
?interest
wd:Q12418
Graph pattern:
Data graph:
64. 64
SPARQL – * Protocol and RDF Query Language
?interest
<http://example.org/
bob#me>
foaf:topic_interest
Results:
?creator
dcterms:creator
?interest ?creator
wd:Q12418 dbpedia:
Leonardo_da_Vinci
Graph pattern:
Data graph:
65. 65
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX dcterms: <http://purl.org/dc/terms/>
SELECT ?creator
WHERE {
<http://example.org/bob#me> foaf:topic_interest ?interest .
?interest dcterms:creator ?creator
}
SPARQL Query: Components
Prologue:
• Prefix definitions
• Subtly different from Turtle syntax - the final period is not used
66. 66
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX dcterms: <http://purl.org/dc/terms/>
SELECT ?creator
WHERE {
<http://example.org/bob#me> foaf:topic_interest ?interest .
?interest dcterms:creator ?creator
}
SPARQL Query: Components
Query form:
• ASK, SELECT, DESCRIBE or CONSTRUCT
• SELECT retrieves variables and their bindings as a table
67. 67
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX dcterms: <http://purl.org/dc/terms/>
SELECT ?creator
WHERE {
<http://example.org/bob#me> foaf:topic_interest ?interest .
?interest dcterms:creator ?creator
}
SPARQL Query: Components
Query pattern:
• Defines patterns to match against the data
• Generalises Turtle with variables and keywords – N.B. final period optional
68. 68
• Use the local SPARQL endpoint:
http://localhost:10214/sparql
• Execute the queries from the tutorial
1. Select all statements about Bob
2. Select the creator of the things that Bob is interested in.
Hands-on: Execute SPARQL Query
69. 69
• Namespaces are added with the ‘PREFIX’ directive
• Statement patterns that make up the graph are specified
between brackets (“{}”)
Query Form: ASK
PREFIX foaf: http://xmlns.com/foaf/0.1/
ASK WHERE {
<http://example.org/bob#me> foaf:knows
<http://example.org/alice#me> }
Does Bob know Alice?Query:
true
Results:
Is Alice interested in the Mona Lisa?Query:
false
Results:
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
<http://example.org/alice#me> foaf:topic_interest
wd:Q12418}
70. 70
• A property path is a possible route through a graph
between two graph nodes
• Property paths allow for more concise expression of some
SPARQL basic graph patterns and also add the ability to
match arbitrary length paths
Query Form: SELECT – Property Paths
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX dcterms: <http://purl.org/dc/terms/>
SELECT ?creator WHERE {
<http://example.org/bob#me>
foaf:topic_interest / dcterms:creator ?creator
}
Query: Creator of things that Bob is interested in?
71. 71
Filter expressions
• Different types of filters and functions may be used
Query Form: SELECT - FILTER Expressions
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX dcterms: <http://purl.org/dc/terms/>
SELECT ?person ?another WHERE {
?person foaf:knows ?another
FILTER NOT EXISTS { ?another foaf:knows ?person }
}
Query: People who know someone who does not know them?
Filter: Comparison and logical operators
72. 72
• Calculate aggregate values: COUNT, SUM, MIN, MAX, AVG,
GROUP_CONCAT and SAMPLE
• Built around the GROUP BY operator
• Prune at group level (cf. FILTER) using HAVING
Query Form: SELECT - Aggregates
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?person (COUNT(?another) as ?num_friends) WHERE {
?person a foaf:Person
OPTIONAL { ?person foaf:knows ?another }
}
GROUP BY ?person
How many friends do people have?Query:
73. 73
• CONSTRUCT WHERE: In order to query for a subgraph,
without change, it is no longer necessary to repeat the
graph pattern in the template
Query Form: CONSTRUCT
CONSTRUCT
WHERE {
<http://example.org/bob#me> ?p ?o
}
Example:
75. 75
• Allows federating across multiple SPARQL endpoints
• The SERVICE keyword instructs to invoke a portion of
a SPARQL query against a remote SPARQL endpoint
SPARQL Federated Query
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX dcterms: <http://purl.org/dc/terms/>
SELECT * WHERE {
<http://example.org/bob#me> foaf:topic_interest ?interest .
SERVICE <https://query.wikidata.org/sparql> {
?interest ?predicate ?object }
}
Find information about things that Bob likes at WikidataQuery:
76. 76
SPARQL 1.1 provides data update operations:
• INSERT data: adds some triples, given inline in the
request, into a graph
• DELETE data: removes some triples, given inline in the
request, if the respective graphs contains those
• DELETE/INSERT data: uses in parallel INSERT and DELETE
SPARQL 1.1 Update
79. 79
Aspects of Knowledge Graph Management
KNOWLEDGE GRAPH
STORAGE
• Scalable data loading and
storage
• Querying and analytics
• Built-in inferencing and
custom services
• Management of hybrid
data sources
KNOWLEDGE GRAPH
CREATION
• Model- and ontology
development
• Semi-automatic creation
from structures sources
(data lifting)
• Interlinking of data from
heterogeneous sources
• Collaborative authoring
• Data annotation
• Versioning and
provenance
• Quality Assurance
KNOWLEDGE GRAPH
CONSUMPTION
• Development of end-user
oriented applications
• Interactive visualization
• Exploration
• Semantic search
• Natural language
interfaces
80. 80
metaphacts Supports the Whole Data Lifecyle
Data
Extraction &
Integration
Data Linking
& Enrichment
Storage &
Repositories
Querying &
Inferencing
Search
Visualization
Authoring
end-to-end
platform
81. 81
metaphactory as an Open Platform
BUILT IN OPEN SOURCE
ü Dual licensing (LGPL & commercial license)
ü Open Platform API and SDK
ü Integration of external tools and application via APIs
ü Easy development of own web components and services
ü Full HTML5 compliance
ü Re-usable, declaratively configurable Web Components
= Easy modification, customization, and extensibility
BUILT ON OPEN STANDARDS
ü W3C Web Components
ü W3C Open Annotation Data Model
ü W3C Linked Data Platform Containers
ü Data processing based on W3C standards such as RDF, SPARQL
ü Expressive ontologies for schema modeling based on OWL 2, SKOS
ü Rules, constraints, and query specification based on SPIN and RDF Data
Shapes
= Sustainable Solution
83. 83
Users and their Benefits
EXPERT USERS
• Collaboratively construct
and manage knowledge
graphs
• Integrate data from
heterogeneous sources
• Use standard connectors
for a variety of data
formats
• Benefit from scalable data
processing for big graphs
• Conduct high-
performance querying and
analytics
DEVELOPERS
• Rapidly develop Web and
mobile end-user oriented
applications
• Benefit from various
deployment modes: stand-
alone, HA, scale-up, scale-
out
• Interact with an easy-to-
use interface
• Collaboratively manage,
annotate and author data
• Use large set of custom
query and templates
catalogs
• Capture of provenance
information
END USERS
• Benefit from user-friendly
interaction with data
• Gain insights into
complex relationships
• Enable transparency and
extract value
• Ask questions and obtain
precise results
• Reduce effort for data
analysis
• Reduce noise – obtain
targeted, high quality
results
• Enhance quality of
business decisions
84. 84
Search
• Domain independent, fully
customizable search widget
• Satisfy complex information needs
without learning SPARQL
• Search functionalities
• Graphical query construction
• End user friendly search
interfaces for building and
sharing complex queries
• Semantic auto suggestion
• Interactive result visualization
• Faceted search and exploration
of item collections
• Ability to invoke external full text
search indices such as Solr including
the possibility to score, rank and limit
the results for responsive
autosuggestion
• Saving and sharing of queries and
search results
Search
85. 85
Table
Transform your queries into
durable, interactive tables
Many customization
possibilities, e.g. pagination,
filters and cell templates
Graph
Visualize and explore connections in a graph view
Custom styling of the graph
Variety of graph layouts
Carousel
Animated browsing through a list of
result items
Chart
Visualize trends and
relationships between
numbers, ratios, or
proportions
Visuali-
zation
Tree Table
Tree-based
visualization,
navigation and
browsing through sub-
tree structures
Map
Displaying spatial
data on a
geographic map
Visualization
86. 86
Autho-
ring
• Annotations
• Based on W3C Open
Annotation Data Model
• Automated semantic link
extraction
• Form based authoring
• Manually author and update
instance data, backed by
query templates, data
dependencies, and type
constraints
• Rich editing components for
special data types
• Customizable flexible forms
• Autosuggestion and
validation against the
knowledge graph
• Capturing of provenance
information
• User group management
Authoring
87. 87
Install & Go: Out-of-the-Box Functionality
Getting Started Tutorial
to guide you through your
first steps with metaphactory
Get
started
Management of Queries in
Catalog
for easy reuse and updating
Keyword Search Interface
with semantic autosuggestion,
driven by SPARQL
Search
Data Overview Pages
with Web components for
end-user friendly data
presentation and interaction
Template-based Data Browser
used to define generic views
which are automatically applied
to entire sets of instances
Explore
88. 88
Example: Simple Semantic Search
Keyword search with semantic
autosuggestion, driven by SPARQL
Set up in ~2 minutes!
Declarative Components
Developer embeds
‘semantic-simple-search’
into the page
<semantic-simple-search data-
config='{
"query":"
SELECT ?result ?label ?desc
?img WHERE {
?result rdfs:label ?label .
?result rdfs:comment ?desc .
?result foaf:thumbnail ?img .
FILTER(CONTAINS(?label,
?token))
}",
"searchTermVariable":"token", //
user input
"template":"
<span title="{{result}}">
<img src="{{img}}"
height="30"/>
{{label}} ({{desc}})</span>"
}'/>
1
Rendered component is
displayed to the user and
can be used right away
2
Autosuggestions are
dynamically computed
based on query and user
input
3
89. 89
• Associate a class in the knowledge graph with a template
• The template is applied to instances of the class
HTML5 Template Pages
Bob
foaf:Person
rdf:type
91. 91
Hands-on Exercises
DATA LOADING &
QUERYING
• Loading your
data
• Querying your
data
VISUALIZATION
• Visualizing
results in a table
• Visualizing
results in a graph
SEARCH
• Embedding a
simple search
interface
AUTHORING
• Creating a
template
• Inserting and
updating data
92. 92
Data Loading & Querying
Load data into the store via
the data import and export
administration page
1 2
Query the data via the
SPARQL endpoint.
E.g.: issue a query for all
statements made about Bob
as a subject
3
Visualize results in a table
… or as raw data
93. 93
Visualizing Results in a Table
3
Visualize results in a table
displaying thumbnails as
images, the labels of the
resources as captions, and
links to the individual
resource pages
1
Embed ‘semantic-table’
component
<semantic-table config='{
"query":"SELECT * WHERE {
<http://example.org/bob#me>
?predicate ?object }“
}'>
</semantic-table>
to visualize previous query
as a table in a page
2
Customize the query to
embed thumbnail images
in the result visualization
SELECT ?uri ?label
?thumbnail WHERE { ?uri
rdfs:label ?label;
<http://schema.org/thumbnail
> ?thumbnail }
Use tupleTemplate to
define a template for
displaying the new table
94. 94
Visualizing Results in a Graph
1
Embed ‘semantic-graph’
component
<semantic-graph
query="CONSTRUCT WHERE { ?s ?p ?o
}">
</semantic-graph>
2
Visualize results in a graph
95. 95
Embedding a Simple Search Interface
Embed ‘semantic-simple-
search’ into the page
<semantic-simple-search
config='{
"query":"
SELECT ?uri ?label
WHERE {
FILTER REGEX(?label,
"?token", "i")
?uri rdfs:label
?label
} LIMIT 10
",
"searchTermVariable":"token",
"resourceSelection":{
"resourceBindingName":"uri",
"template":"<span
style="color: blue;"
title="{{uri.value}}">{{label.
value}}</span>"
},
"inputPlaceholder":"Search for
something e.g. "Bob""
}'>
</semantic-simple-search>
1
Rendered component is
displayed and can be used
right away
2
Autosuggestions are
dynamically computed
based on query and user
input
3
96. 96
Creating a Template
1
Use the templating mechanism to
create a template for the resource
type ‘Person’, to display:
• the person's name
• an image, if available
• his interests
• his friendship relationship
2
Visualize the result on
Bob’s instance page
97. 97
Inserting and Updating Data
1
Use a SPARQL UPDATE operation
against the SPARQL endpoint to
create and add new instance data
• the person's name
• an image, if available
• his interests
• his friendship relationship
2
Visualize the result
98. 98
• Knowledge graphs as a flexible model for data integration and knowledge representation
• Standards for “semantic” knowledge graphs
• RDF as graph-based data model
• OWL as expressive ontology language
• SKOS for taxonomic knowledge
• SPARQL as query language
• Application areas
• Open knowledge graphs, e.g. Wikidata
• Cultural Heritage
• Life Sciences
• And many more
• Get started with the metaphactory Knowledge Graph platform today!
Summary
99. 99
• Some slides build on the Euclid training material
• Thanks to the team at metaphacts
• Thanks to the ESWC organizers
Acknowledgements and Thanks
http://www.euclid-project.eu