Linked Data for Knowledge Discovery: Introduction
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This document summarizes the LD4KD 2015 workshop, which brought together researchers from the linked data and knowledge discovery communities. The workshop included two paper presentations, a demo session, and discussions on opportunities and challenges at the intersection of linked data and knowledge discovery. Some opportunities discussed were using linked data as input for knowledge discovery due to its large, global scale and ability to be extended and enriched. Challenges discussed included dealing with linked data as a graph structure, its distributed and incomplete nature, and ensuring its quality and reducing bias. The goal of the workshop was to further understanding and develop practical tools to address these challenges.
Linked Data for Knowledge Discovery: Introduction
- 1. Welcome to LD4KD 2015 #LD4KD2015 Ilaria Tiddi - @IlaTiddi Mathieu d’Aquin - @mdaquin Claudia d’Amato - @cldamat
- 2. Yes, this is a small workshop... 2 paper presentations out of 4 papers submitted but A lot of interest from both communities (KD/ML and Linked Data). LD4KD is more than a set of paper presentations. Working on existing opportunities and challenges and the way they can be better supported/addressed.
- 3. Program 10:00 – 10:15 Welcome 10:15 – 10:45 Linked Data for Knowledge Discovery: the story so far 10:45 – 11:15 Mehwish Alam and Amedeo Napoli, Navigating and Exploring RDF Data using Formal Concept Analysis 11:15 – 11:30 Coffee Break 11:30 – 12:00 Denis Krompaß and Volker Tresp, Ensemble Solutions for Link-Prediction in Knowledge Graphs 12:00 – 12:45 Demo session 12:45 – 13:00 Wrap-up and conclusions
- 4. Linked Data for Knowledge Discovery: the story so far
- 5. LD for KD - KD with LD
- 6. LD for KD - KD with LD A set of techniques and methods to extract meaningful information patterns from raw data
- 7. LD for KD - KD with LD A set of techniques and methods to extract meaningful information patterns from raw data A set of principles and technologies for sharing and integrating data through the architecture of the W eb
- 8. LD for KD - KD with LD
- 9. LD for KD - KD with LD A complex, information-intensive process
- 10. LD for KD - KD with LD A complex, information-intensive process A global, distributed and collaborative information source
- 11. LD for KD - KD with LD
- 12. LD for KD - KD with LD
- 13. LD for KD - KD with LD
- 14. LD for KD - KD with LD
- 15. LD for KD - KD with LD
- 16. LD for KD - KD with LD
- 17. LD for KD - KD with LD
- 18. Needs a more systematic understanding... Of the way the properties of the process of KD and of the information source of LD create new opportunities and challenges for both communities. Knowledge Discovery Linked Data Talking about communities: ?
- 19. Started in LD4KD 2014 http://events.kmi.open.ac.uk/ld4kd2014/ http://goo.gl/NEu1d7 A collaborative document to share information about issues, challenges, tools and methods at the intersection of Linked Data and Knowledge Discovery
- 20. Opportunities Linked Data as Input Large, global, accessible, convenient, multilingual - Separation of data and process - Easily extended, integrated, enriched. Link Discovery Using DM/ML techniques to find connections across disparate datasets Exploiting links across dataset for richer data, and richer patterns Can this be done “on the fly”, i.e. within DM/ML process? Background knowledge to enrich the KDD process Can Linked Data be part of the bottom arrow in the KDD diagram? A global, universally accessible knowledge base of almost everything?
- 21. Is it too hard? “RDF and SPARQL - are they really complicated?” “Not really, but SPARQL is not what ML researchers want to worry about. Most of us don't even like SQL. Just a CSV file is the easiest format. It's messy, but we really don't care.”
- 22. Challenges Linked data is a graph but is this really an issue? Linked data is a distributed, collaborative graph accessibility issues, link explosion, termination need to build the graph on the fly, not all data is known at the start Linked data is incomplete and biased And we don’t know the bias - how to evaluate KDD that uses Linked Data? Linked data is redundant, unbalanced and unreliable noisy, bad formating (no control), lack of documentation, which ID/source to choose and what is the impact?
- 23. And we haven’t even started talking about... Mr. Slow and Mr. Nosey
- 24. Continuing this year... Start with the practical aspects: What tools and applications exist, through which we can explore the use of linked data in KD, and from which we can learn how to solve some of the challenges
- 25. Discuss, Contribute, Question and of course Enjoy the workshop! #LD4KD2015