Universal Search and Social Networking

1. Universal Search and Social Networking Exploiting the features of each to enhance the other and the tools that make it possible Peter Wallqvist Ravn Systems peter@ravn.co.uk

2. Universal Search Incorporates traditional ‘Enterprise Search’ with emphasis on any data source, including external Modern systems include features such as faceting, meta data biasing, query and result suggestions Can do a good job if users knows what they are looking for

3. Enterprise Social Networking Provides a platform for the people in the organisation to communicate and share content Makes information open and available to all Keeps a record of previous discussions that would otherwise get lost Useful tool if users are incentivised to use it

4. Limitations of stand-alone solutions Universal Search Social Networking Often becomes an isolated application that is used only by some Little or no relation to the user’s everyday work Total focus on other users to interact with, ignoring other knowledge assets • There is little distinction between quality content and items that are of less use • Little control over ranking and relevance once system is running • New and updated content doesn’t get attention • Annotations and value additions might not be searched

5. Solution: Integration Treat any and all information within and outside the organisation as knowledge assets Create conversations, share ideas and add knowledge about these assets The focus will shift from social networking being simply about communication to being about new and existing knowledge and experience sharing

6. Integration in practice • Let users interact with any knowledge asset • Let users explicitly express interest in any knowledge asset (‘follow’) • Make the implicit and explicit interactions with assets visible • Have a search engine tightly integrated with a NoSQL object based data store handy

7. Integration in practice • Instant updates to the index • Multiple name spaces • Links and hierarchy natural • Queries can be made based on far more than keywords • Make use of a Knowledge Graph – Exploit links that are available

8. Simple links – derived from content

9. Simple links – derived from content

10. Simple links – derived from content

11. Simple links – derived from content

12. Clever links – derived from semantics

13. Clever links – derived from semantics

14. Clever links – derived from semantics

15. Social links – derived from interactions

16. Social links – derived from interactions

17. Enhanced Universal Search Boost relevance of content that has been scored, graded, linked or accessed a lot Make added annotations and comments searchable Find content based on links (simple, clever, interactions) Adjust relevance and suggestions based on user profile and other input In essence – “Enterprise Graph Search”

18. Enhanced Social Networking Treat all knowledge assets (people, documents, projects, discussions, etc) as entities you get updated on in your feed The anchor to the assets users are used to interacting with each day makes the initial use of the system more natural Identify Expert groups based on interaction with knowledge assets

20. The Knowledge Graph

21. The Knowledge Graph

22. The Knowledge Graph

23. The Knowledge Graph

24. The Knowledge Graph

25. Take Home Points Universal Search and Social Networking services are great ways to access and distribute knowledge and information If the two work together, the impact can be substantially greater Modern search / store / link platform required – classic flat reverse term index obsolete

26. Peter Wallqvist Ravn Systems peter@ravn.co.uk