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KR on the Web
Rinke Hoekstra and Stefan Schlobach
KR on the Web Pitch Slides by Rinke Hoekstra, licensed under a Creative Commons Attribution 4.0 International License.
Thanks to: These slides are based on decks by Stefan Schlobach, Frank van Harmelen, Paul Groth, Laura Hollink, Antonis Loizou, Ronald Siebes,
and the "Semantic Technologies" course at the University of Oslo.
rinke.hoekstra@vu.nl

k.s.schlobach@vu.nl
What is Knowledge Representation?
Represent information about the world in a form that
a computer system can use to solve complex tasks
Formalisms guided by how humans solve
problems and represent knowledge
Incorporate findings form logic to
automate various kinds of reasoning
What is Knowledge Representation?
Represent information about the world in a form that
a computer system can use to solve complex tasks
Formalisms guided by how humans solve
problems and represent knowledge
Incorporate findings form logic to
automate various kinds of reasoning
Traditional KR deals with relatively 

small, curated knowledge bases
The Web as a Ginormous Knowledge Graph?
From: https://www-01.ibm.com/software/data/bigdata/images/4-Vs-of-big-data.jpg
Overview - KR on the Web
• Study the implications of veracity, variety and volume for KR on the Web
• Organisation:
• Invited lectures by key people from research & industry
• Literature groups prepare & present for invited lectures
• Project groups write “conference papers” on each theme (milestones).
• Related courses are Knowledge Representation, and Knowledge
Engineering, and Semantic Web
Preliminaries - A “systems paper”
• Learn how to use the technologies for KR on the Web
• Learn about the formal semantics of KR languages for the Web
• Convert existing datasets to Linked Data
• Define models at different levels of expressiveness over the data
• Query the data, and use a reasoner to infer new knowledge
• Build a simple web-application that shows it all.
with the tools and techniques of KR on the web (bring your own laptop!).
5. EHBO Lectures where students work on the practical assignments and
can ask feedback and help on the choices they make.
Every module ends with the submission of a milestone, a research paper that
describes and defends the work done over two weeks.
The papers are written and prepared by the project groups, and are based on
the work done by the two students in that group.
The papers are to be submitted to EasyChair for peer review (see Evaluation
& Grading below).
The papers should be accompanied by a proof that the reported work has
been done (as is customary in academic peer review). This proof is typically in
the form of a link to the dataset, model, code or working system that is
reported on.
These assignments are (in short):
1. Milestone 1 - Systems Paper
A paper (8 pages, Springer LNCS style) that describes a live Semantic Web
system, its use case and potential benefits, the datasets used and how they
were converted, a formal model for the data and interesting queries over the
data.
3. Assignments
Milestones
Veracity - A “data & ontology” paper
• What is the best model & level of expressiveness for your data?
• Quality measures of model and data
• Prevent ambiguity, safeguard trust (where does the data come from?)
• Maximise findability, understandability and reusability
• Follow best practices for data publication
Every module ends with the submission of a milestone, a research paper that
describes and defends the work done over two weeks.
The papers are written and prepared by the project groups, and are based on
the work done by the two students in that group.
The papers are to be submitted to EasyChair for peer review (see Evaluation
& Grading below).
The papers should be accompanied by a proof that the reported work has
been done (as is customary in academic peer review). This proof is typically in
the form of a link to the dataset, model, code or working system that is
reported on.
These assignments are (in short):
1. Milestone 1 - Systems Paper
A paper (8 pages, Springer LNCS style) that describes a live Semantic Web
system, its use case and potential benefits, the datasets used and how they
were converted, a formal model for the data and interesting queries over the
data.
1. Milestone 2 - Data and Ontology Track Paper
A paper (8 pages, Springer LNCS style) that convincingly argues why the
model and data are correct (for the envisioned purpose), shows that they are
evaluated using state-of-the-art methods (quality, suitability), compared to
other data, and that it follows best practices.
1. Milestone 3 - Ontology Matching Paper
Variety - An “ontology matching” paper
• How to algorithmically integrate data with that of others?
• Identity reconciliation: what entities are the same? What does identity mean?
• Ontology alignment: what concepts and relations are the same?
• How do these integrations affect your own model and data?
• What new use cases can we cover?
A paper (8 pages, Springer LNCS style) that describes, motivates and
evaluates the methods and algorithms used to extend the data and model with
external data (from other students or from the LOD cloud).
1. Milestone 4 - Reasoning Track Paper
A paper (8 pages, Springer LNCS style) in which students, based on an
analysis of the data, describe, motivate and evaluate a reasoner that
implements (part of the) standard Semantic Web entailments over their data.
For each of the Milestones there will be a separate folder on Blackboard,
containing a more detailed description of the task (see also the respective
Volume - A “reasoning track” paper
• Knowledge graphs as complex system
• How does volume affect our ability to query and reason over the data?
• What complex system properties does the data display?
• Can we exploit the structure of the graph to guide computation?
A paper (8 pages, Springer LNCS style) that describes, motivates and
evaluates the methods and algorithms used to extend the data and model with
external data (from other students or from the LOD cloud).
1. Milestone 4 - Reasoning Track Paper
A paper (8 pages, Springer LNCS style) in which students, based on an
analysis of the data, describe, motivate and evaluate a reasoner that
implements (part of the) standard Semantic Web entailments over their data.
For each of the Milestones there will be a separate folder on Blackboard,
containing a more detailed description of the task (see also the respective
Latex templates) as well as some example papers from recent major
conferences in the field.
Literature Groups
• Expose traditional KR to the idiosyncrasies of the Web
• Combine research themes from Big Data with semantics and KR
• Learn about state-of-the-art research in this field
• Learn how to do your own state-of-the-art research
• Discuss, engage, and communicate your work
Rinke Hoekstra and Stefan Schlobach
Summary - KR on the Web
rinke.hoekstra@vu.nl k.s.schlobach@vu.nl
• Expose traditional KR to the idiosyncrasies of the Web
• Combine research themes from Big Data with semantics and KR
• Learn about state-of-the-art research in this field
• Learn how to do your own state-of-the-art research
• Discuss, engage, and communicate your work
Rinke Hoekstra and Stefan Schlobach
Summary - KR on the Web
rinke.hoekstra@vu.nl k.s.schlobach@vu.nl

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Knowledge Representation on the Web

  • 1. KR on the Web Rinke Hoekstra and Stefan Schlobach KR on the Web Pitch Slides by Rinke Hoekstra, licensed under a Creative Commons Attribution 4.0 International License. Thanks to: These slides are based on decks by Stefan Schlobach, Frank van Harmelen, Paul Groth, Laura Hollink, Antonis Loizou, Ronald Siebes, and the "Semantic Technologies" course at the University of Oslo. rinke.hoekstra@vu.nl
 k.s.schlobach@vu.nl
  • 2. What is Knowledge Representation? Represent information about the world in a form that a computer system can use to solve complex tasks Formalisms guided by how humans solve problems and represent knowledge Incorporate findings form logic to automate various kinds of reasoning
  • 3. What is Knowledge Representation? Represent information about the world in a form that a computer system can use to solve complex tasks Formalisms guided by how humans solve problems and represent knowledge Incorporate findings form logic to automate various kinds of reasoning Traditional KR deals with relatively 
 small, curated knowledge bases
  • 4.
  • 5. The Web as a Ginormous Knowledge Graph?
  • 7. Overview - KR on the Web • Study the implications of veracity, variety and volume for KR on the Web • Organisation: • Invited lectures by key people from research & industry • Literature groups prepare & present for invited lectures • Project groups write “conference papers” on each theme (milestones). • Related courses are Knowledge Representation, and Knowledge Engineering, and Semantic Web
  • 8. Preliminaries - A “systems paper” • Learn how to use the technologies for KR on the Web • Learn about the formal semantics of KR languages for the Web • Convert existing datasets to Linked Data • Define models at different levels of expressiveness over the data • Query the data, and use a reasoner to infer new knowledge • Build a simple web-application that shows it all. with the tools and techniques of KR on the web (bring your own laptop!). 5. EHBO Lectures where students work on the practical assignments and can ask feedback and help on the choices they make. Every module ends with the submission of a milestone, a research paper that describes and defends the work done over two weeks. The papers are written and prepared by the project groups, and are based on the work done by the two students in that group. The papers are to be submitted to EasyChair for peer review (see Evaluation & Grading below). The papers should be accompanied by a proof that the reported work has been done (as is customary in academic peer review). This proof is typically in the form of a link to the dataset, model, code or working system that is reported on. These assignments are (in short): 1. Milestone 1 - Systems Paper A paper (8 pages, Springer LNCS style) that describes a live Semantic Web system, its use case and potential benefits, the datasets used and how they were converted, a formal model for the data and interesting queries over the data. 3. Assignments Milestones
  • 9. Veracity - A “data & ontology” paper • What is the best model & level of expressiveness for your data? • Quality measures of model and data • Prevent ambiguity, safeguard trust (where does the data come from?) • Maximise findability, understandability and reusability • Follow best practices for data publication Every module ends with the submission of a milestone, a research paper that describes and defends the work done over two weeks. The papers are written and prepared by the project groups, and are based on the work done by the two students in that group. The papers are to be submitted to EasyChair for peer review (see Evaluation & Grading below). The papers should be accompanied by a proof that the reported work has been done (as is customary in academic peer review). This proof is typically in the form of a link to the dataset, model, code or working system that is reported on. These assignments are (in short): 1. Milestone 1 - Systems Paper A paper (8 pages, Springer LNCS style) that describes a live Semantic Web system, its use case and potential benefits, the datasets used and how they were converted, a formal model for the data and interesting queries over the data. 1. Milestone 2 - Data and Ontology Track Paper A paper (8 pages, Springer LNCS style) that convincingly argues why the model and data are correct (for the envisioned purpose), shows that they are evaluated using state-of-the-art methods (quality, suitability), compared to other data, and that it follows best practices. 1. Milestone 3 - Ontology Matching Paper
  • 10. Variety - An “ontology matching” paper • How to algorithmically integrate data with that of others? • Identity reconciliation: what entities are the same? What does identity mean? • Ontology alignment: what concepts and relations are the same? • How do these integrations affect your own model and data? • What new use cases can we cover? A paper (8 pages, Springer LNCS style) that describes, motivates and evaluates the methods and algorithms used to extend the data and model with external data (from other students or from the LOD cloud). 1. Milestone 4 - Reasoning Track Paper A paper (8 pages, Springer LNCS style) in which students, based on an analysis of the data, describe, motivate and evaluate a reasoner that implements (part of the) standard Semantic Web entailments over their data. For each of the Milestones there will be a separate folder on Blackboard, containing a more detailed description of the task (see also the respective
  • 11. Volume - A “reasoning track” paper • Knowledge graphs as complex system • How does volume affect our ability to query and reason over the data? • What complex system properties does the data display? • Can we exploit the structure of the graph to guide computation? A paper (8 pages, Springer LNCS style) that describes, motivates and evaluates the methods and algorithms used to extend the data and model with external data (from other students or from the LOD cloud). 1. Milestone 4 - Reasoning Track Paper A paper (8 pages, Springer LNCS style) in which students, based on an analysis of the data, describe, motivate and evaluate a reasoner that implements (part of the) standard Semantic Web entailments over their data. For each of the Milestones there will be a separate folder on Blackboard, containing a more detailed description of the task (see also the respective Latex templates) as well as some example papers from recent major conferences in the field. Literature Groups
  • 12. • Expose traditional KR to the idiosyncrasies of the Web • Combine research themes from Big Data with semantics and KR • Learn about state-of-the-art research in this field • Learn how to do your own state-of-the-art research • Discuss, engage, and communicate your work Rinke Hoekstra and Stefan Schlobach Summary - KR on the Web rinke.hoekstra@vu.nl k.s.schlobach@vu.nl
  • 13. • Expose traditional KR to the idiosyncrasies of the Web • Combine research themes from Big Data with semantics and KR • Learn about state-of-the-art research in this field • Learn how to do your own state-of-the-art research • Discuss, engage, and communicate your work Rinke Hoekstra and Stefan Schlobach Summary - KR on the Web rinke.hoekstra@vu.nl k.s.schlobach@vu.nl