Dyszynski: Scaling up local knowledge using innovative online knowledge management tools
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Dyszynski: Scaling up local knowledge using innovative online knowledge management tools
- 1. Sharing information on climate adaptation
- 2. Knowledge sharing opportunities, synthesis of lessons learned and communication of climate adaptation stories
- 9. Increase the exposure of your work and that of your partners
- 10. Kenya Story at Copenhagen, 2009: Wangari Maathai
- 11. Add adaptation projects to the weADAPT Google Earth Browser and search for what others are doing!
- 12. High and low bandwidth options
- 13. Edit placemarks within the browser – no need for spreadsheets (as with Google Earth)
- 14. Make edits...and immediately preview changes
- 15. For large amounts of data, generate a flexible custom-made spreadsheet
- 16. Using search terms you are interested in and create adaptation stories (example Kenya Story)
- 17. Low bandwidth view of Google Earth data points Placemarks are meaningfully integrated in our Knowledge Base, creating intelligent links with other data
- 18. There is a brand new user-friendly interface for wikiADAPT
- 19. wikiADAPT is also intelligently integrated in the Knowledge Base
- 20. All this data is linked in to all other relevant content including articles, projects, individuals, partner organisations and networks
- 22. E.g. weADAPT links people in relevant networks and relevant documents. Or if the search term was ‘drought in Kenya’ it would link people writing on this issue, or those who have registered an interest in it.
- 23. Link to people interested in the same issues and grow your network
- 24. Connecting YOU to people already working in your area of interest
- 25. Strengthen and create links in your knowledge network – bringing knowledge and organisations together in an integrated way
- 26. To experience it, register at weADAPT.org and create a profile about who you are and what you are interested in…. This will allow relevant content including articles, placemarks, projects, individuals, partner organisations and networks to intelligently find you in a context-relevant way
- 30. Case countries and systems Country System type Tanzania Extensive livestock Nepal Highland mixed Malawi Maize mixed Rwanda Tree (coffee-banana) crop Bangladesh Rice based
- 31. Rwanda example Changes in Max Temperature
Editor's Notes
- Identified at Nairobi workshop where participative methods were used to establish relationships and the nature of interaction between different actors, from community based organizations, to international research institutions and NGOs, to national government.
- Semantic search seeks to improve search accuracy by understanding searcher intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Author Seth Grimes lists "11 approaches that join semantics to search", and Hildebrand et al. provide an overview that lists semantic search systems and identifies other uses of semantics in the search process. Guha et al. Rather than using ranking algorithms such as Google's PageRank to predict relevancy, Semantic Search uses semantics , or the science of meaning in language, to produce highly relevant search results. In most cases, the goal is to deliver the information queried by a user rather than have a user sort through a list of loosely related keyword results. In order to understand what a user is searching for, word sense disambiguation must occur. When a term is ambiguous, meaning it can have several meanings (for example, if one considers the lemma " bark ", which can be understood as "the sound of a dog," "the skin of a tree," or "a three-masted sailing ship"), the disambiguation process is started, thanks to which the most probable meaning is chosen from all those possible. Such processes make use of other information present in a semantic analysis system and takes into account the meanings of other words present in the sentence and in the rest of the text.
- User friendly interface
- Emphasizing user friendliness…if you’re familiar with google earth speadsheets
- Keyhole Markup Language (KML ) is an XML schema for expressing geographic annotation and visualization within Internet-based, two-dimensional maps and three-dimensional Earth browsers. KML was developed for use with Google Earth, which was originally named Keyhole Earth Vie . KML files are often distributed in KMZ files, zipped.
- Example Asia platform