DBIS

Knowledge Graphs are large graphs used to capture information about the real world in such a way that is is useful for applications. In these data structures, there are all sorts of entities (for example, people, events, places, organizations, etc.). Knowledge Graphs are used by many organizations to represent the information they need for their operations. The most well-known example is Google, where a knowledge graph is used to enrich the search results. Also personal assistants, such as Amazon’s Alexa, Apple’s Siri and Google Now, as well as question answering systems such as IBM Watson, make use of knowledge graphs to provide information to their users.

Besides these, also other information graphs, are in use by large organizations to improve or personalize their services. Examples include the Facebook graph, the Amazon product graph, and the Thompson Reuters Knowledge Graph.

Opensource Knowledge Graphs such as Wikidata and DBPedia provide universal access to linked entities from a large range of domains.

The graph also contains all sorts of information about these entities (e.g., age, opening hours, …) and relations between them (e.g., “this shop is located in Aachen”). Furthermore, it may contain context information (e.g., the source of some information) and schema information or background knowledge (e.g., “shops have opening hours”).

In this course we will give a basic practical introduction to working with these graphs. We plan to cover the following in the course:

• Graph representation of data
• Knowledge Graph basics
• Knowledge Graph creation and maintainance tasks: Creation, Hosting, Curation and Deployment
• Use of vocabularies and ontologies as schemas for graphs
• Searching information in knowledge graphs
• Information extraction into knowledge graphs
• Data mining techniques for knowledge graphs
• Knowledge graph completion (predicting links, finding anomalies)
• Data governance aspects, e.g., data quality
• Architectures for knowledge graphs (e.g., data lakes, central vs. decentral storage, knowledge graphs on top of relational or NoSQL databases)

This course provides participants with a comprehensive and versatile toolbox of data visualisation and analysis methods, which can be transferred to a vast number of applications.

Knowledge Graphs are large graphs used to capture information about the real world in such a way that is is useful for applications. In these data structures, there are all sorts of entities (for example, people, events, places, organizations, etc.). Knowledge Graphs are used by many organizations to represent the information they need for their operations. The most well-known example is Google, where a knowledge graph is used to enrich the search results. Also personal assistants, such as Amazon’s Alexa, Apple’s Siri and Google Now, as well as question answering systems such as IBM Watson, make use of knowledge graphs to provide information to their users.

Besides these, also other information graphs, are in use by large organizations to improve or personalize their services. Examples include the Facebook graph, the Amazon product graph, and the Thompson Reuters Knowledge Graph.

Opensource Knowledge Graphs such as Wikidata and DBPedia provide universal access to linked entities from a large range of domains.

The graph also contains all sorts of information about these entities (e.g., age, opening hours, …) and relations between them (e.g., “this shop is located in Aachen”). Furthermore, it may contain context information (e.g., the source of some information) and schema information or background knowledge (e.g., “shops have opening hours”).

In this course we will give a basic practical introduction to working with these graphs. We plan to cover the following in the course:

• Graph representation of data
• Knowledge Graph basics
• Knowledge Graph creation and maintainance tasks: Creation, Hosting, Curation and Deployment
• Use of vocabularies and ontologies as schemas for graphs
• Searching information in knowledge graphs
• Information extraction into knowledge graphs
• Data mining techniques for knowledge graphs
• Knowledge graph completion (predicting links, finding anomalies)
• Data governance aspects, e.g., data quality
• Architectures for knowledge graphs (e.g., data lakes, central vs. decentral storage, knowledge graphs on top of relational or NoSQL databases)

Knowledge Graphs are large graphs used to capture information about the real world in such a way that is is useful for applications. In these data structures, there are all sorts of entities (for example, people, events, places, organizations, etc.). Knowledge Graphs are used by many organizations to represent the information they need for their operations. The most well-known example is Google, where a knowledge graph is used to enrich the search results. Also personal assistants, such as Amazon’s Alexa, Apple’s Siri and Google Now, as well as question answering systems such as IBM Watson, make use of knowledge graphs to provide information to their users.

Besides these, also other information graphs, are in use by large organizations to improve or personalize their services. Examples include the Facebook graph, the Amazon product graph, and the Thompson Reuters Knowledge Graph.

Opensource Knowledge Graphs such as Wikidata and DBPedia provide universal access to linked entities from a large range of domains.

The graph also contains all sorts of information about these entities (e.g., age, opening hours, …) and relations between them (e.g., “this shop is located in Aachen”). Furthermore, it may contain context information (e.g., the source of some information) and schema information or background knowledge (e.g., “shops have opening hours”).

In this course we will give a basic practical introduction to working with these graphs. We plan to cover the following in the course:

• Graph representation of data
• Knowledge Graph basics
• Knowledge Graph creation and maintainance tasks: Creation, Hosting, Curation and Deployment
• Use of vocabularies and ontologies as schemas for graphs
• Searching information in knowledge graphs
• Information extraction into knowledge graphs
• Data mining techniques for knowledge graphs
• Knowledge graph completion (predicting links, finding anomalies)
• Data governance aspects, e.g., data quality
• Architectures for knowledge graphs (e.g., data lakes, central vs. decentral storage, knowledge graphs on top of relational or NoSQL databases)

This course provides participants with a comprehensive and versatile toolbox of data visualisation and analysis methods, which can be transferred to a vast number of applications.

Knowledge Graphs are large graphs used to capture information about the real world in such a way that is is useful for applications. In these data structures, there are all sorts of entities (for example, people, events, places, organizations, etc.). Knowledge Graphs are used by many organizations to represent the information they need for their operations. The most well-known example is Google, where a knowledge graph is used to enrich the search results. Also personal assistants, such as Amazon’s Alexa, Apple’s Siri and Google Now, as well as question answering systems such as IBM Watson, make use of knowledge graphs to provide information to their users.

Besides these, also other information graphs, are in use by large organizations to improve or personalize their services. Examples include the Facebook graph, the Amazon product graph, and the Thompson Reuters Knowledge Graph.

Opensource Knowledge Graphs such as Wikidata and DBPedia provide universal access to linked entities from a large range of domains.

The graph also contains all sorts of information about these entities (e.g., age, opening hours, …) and relations between them (e.g., “this shop is located in Aachen”). Furthermore, it may contain context information (e.g., the source of some information) and schema information or background knowledge (e.g., “shops have opening hours”).

In this course we will give a basic practical introduction to working with these graphs. We plan to cover the following in the course:

• Graph representation of data
• Knowledge Graph basics
• Knowledge Graph creation and maintainance tasks: Creation, Hosting, Curation and Deployment
• Use of vocabularies and ontologies as schemas for graphs
• Searching information in knowledge graphs
• Information extraction into knowledge graphs
• Data mining techniques for knowledge graphs
• Knowledge graph completion (predicting links, finding anomalies)
• Data governance aspects, e.g., data quality
• Architectures for knowledge graphs (e.g., data lakes, central vs. decentral storage, knowledge graphs on top of relational or NoSQL databases)

Knowledge Graphs are large graphs used to capture information about the real world in such a way that is is useful for applications. In these data structures, there are all sorts of entities (for example, people, events, places, organizations, etc.). Knowledge Graphs are used by many organizations to represent the information they need for their operations. The most well-known example is Google, where a knowledge graph is used to enrich the search results. Also personal assistants, such as Amazon’s Alexa, Apple’s Siri and Google Now, as well as question answering systems such as IBM Watson, make use of knowledge graphs to provide information to their users.

Besides these, also other information graphs, are in use by large organizations to improve or personalize their services. Examples include the Facebook graph, the Amazon product graph, and the Thompson Reuters Knowledge Graph.

Opensource Knowledge Graphs such as Wikidata and DBPedia provide universal access to linked entities from a large range of domains.

The graph also contains all sorts of information about these entities (e.g., age, opening hours, …) and relations between them (e.g., “this shop is located in Aachen”). Furthermore, it may contain context information (e.g., the source of some information) and schema information or background knowledge (e.g., “shops have opening hours”).

In this course we will give a basic practical introduction to working with these graphs. We plan to cover the following in the course:

• Graph representation of data
• Knowledge Graph basics
• Knowledge Graph creation and maintainance tasks: Creation, Hosting, Curation and Deployment
• Use of vocabularies and ontologies as schemas for graphs
• Searching information in knowledge graphs
• Information extraction into knowledge graphs
• Data mining techniques for knowledge graphs
• Knowledge graph completion (predicting links, finding anomalies)
• Data governance aspects, e.g., data quality
• Architectures for knowledge graphs (e.g., data lakes, central vs. decentral storage, knowledge graphs on top of relational or NoSQL databases)

In this practical course, the participants learn to run a software development project and create a software product from the very beginning – from requirement analysis to release. The students will learn the importance of Scrum as part of the agile software development process.

In this lab, we will apply these technologies to some data exchange/data sharing scenarios. Students are expected to develop a complete workflow for a data exchange, including data preparation, policy definition, apps for enriching data, etc.