1. A Web of Concepts Dalvi, et al. Presented by Andrew Zitzelberger

2. Vision Transform hyperlinked bags of words into semantically rich aggregate view of information on the web.

3. Concept Things of interest – Searching for information – Accomplishing a task Reservations, etc.

4. Instances Record of a concept – Restaurant Gochi (19980 Homestead Rd Cupertino CA) – Academia? Publications, research institutions

5. Instance Representation Loosely-structured record (lrec) – Attribute-key, value pairs – Unique id field Entity matching problem – Metadata Attribute list

6. Domain Set of related concepts – Academic community domain = {publications, people, conferences}

7. Usage Study Instance vs. Concept Search yelp.com – Month of queries resulting in a click (restaurants) – 59% specific business URL – 19% search URL either specific business or group – 11% specific group URL

8. Usage Study Concept Attribute Search Remove restaurant name and location information from query Co-occuring words: – Menu (3%), coupons (1.8%), online, weekly specials, locations (1.5%) – Nutrition, to go, delivery, careers, cod

9. Usage Study Aggregation Value 59% clicked on at least one other URL 35% clicked on at least two other URLs Small manual evaluation indicates pages are often about the same business.

10. Usage Study Concepts vs. Browsing 42% of homepage visits are from search engine – Immediately following URL 11.5% location 9% menu 1% coupons 10.5% of user trails contain more than one distinct instance of the restaurant concept

11. Extraction Create new records from the web – Information extraction – Linking – Analysis Meta-data tagging (cuisine type)

12. Domain-centric vs. Site-centric Extraction Site-centric extraction – Wrappers for page structure – Probabilistic models (CRF) Domain-centric extraction – Fields of interest – Statistical properties (single zip code, etc.) – Structure components (lists, link relationships)

13. Domain-centric Extraction Aggregator mining – Learn from extracted knowledge (similar menus) Matching – Text is “about” a record (restaurant review)

14. Application Aggregation

15. Application Session Optimization User understanding – Historical modeling – Session modeling Content understanding Example: Birks – Birks and Mayors (luxury Jewelers) vs. Birk’s Steakhouse

16. Application Browse Optimization Alternatives: (Restaurants) – Similar type of cuisine – Similar location – Similar quality Augmentations: (Camera) – Batteries – Memory cards

17. Concept Search Result Pages – shows multiple records Concept Pages – information about an instance Article Pages – a piece of authored text

18. Advertising Increase in targeted advertisements Target concepts rather than keywords

19. Challenges Transfer learning – Transfer extractor knowledge Tracking uncertainty – Accuracy issues – “Web of concepts is not a one time affair” Wrapper problems Concept updates Relevance Measures – User satisfaction

20. Related Work Information Extraction/Integration Systems Dataspace Systems Semantic Web

21. Future Work Enrich representation model – Path storage to data – Provenance, versions, uncertainty – Hierarchal relationships (containment or inheritance) Ranking of disparate sources