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Open Knowledge NetworkA.W. Moore & R.V.Guha
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Google Now
Microsoft Cortana
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Why this initiative: Closed vs Open…
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Lessons from history
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Open Knowledge Network
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• GIS• Amazon• UMLS Codes• Indexed Web Docs• CYC• wikidata• freebase• tripadvisor etc• schema.org
• Existing Entity Stores
• Architecture– Catalog– Matching Engine– Facts– Normalization
Engine• Use Cases• Risks
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Global ID = 0xA3569B
Short Title = [Logitech Digital I/O Pen]Short Mentions
Logitech Digital IODigital IO PenLong
MentionsImages
Global ID = 0x220CCC
Short Title = [Pittsburgh]Short Mentions
Pittsburg
PITLong MentionsImages
. . .
. . .
. . .
. . .
• Existing Entity Stores
• Architecture– Catalog– Matching Engine– Facts– Normalization
Engine• Use Cases• Risks
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“….unlike Logi-Techs’ new digital IO stylus, which…”
Global ID = 0xA3569B with probability 0.94Global ID = 0xEEA001 with probability 0.02…
• Existing Entity Stores
• Architecture– Catalog– Matching Engine– Facts– Normalization
Engine• Use Cases• Risks
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“Triples” is one popular approach:
• <Banana ID>.color = <yellow ID>
• (<HSBC ID> is_a <Bank ID>)
• (<Dell XPS 13” notebook 2015 ID> has_a <2mm 12 Volt DC composite power socket ID>)
• There is and will continue to be a major intellectual war on the expressiveness of the semantics.
• Winner should be decided by use cases.
• Existing Entity Stores
• Architecture– Catalog– Matching Engine– Facts– Normalization
Engine• Use Cases• Risks
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“Jersey”
“NYC” “Big Apple”
• Existing Entity Stores
• Architecture– Catalog– Matching Engine– Facts– Normalization
Engine• Use Cases• Risks
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• Existing Entity Stores
• Architecture– Catalog– Matching Engine– Facts– Normalization
Engine• Use Cases• Risks
Question answering:Fact Questions:• [How old is vice president Pence?]• [Which Washington-based think tanks have worked on projects involving South
American trade?]• [Which building am I in? Where do I go for a taxi?]Research Questions:• [What are good things to do with kids in Pittsburgh?]• [Which Hodgkins Lymphoma treatments are covered under the Affordable Care Act
for my mother?]• [What do the cells in capillary systems of liver tumors unresponsive to sorenafib
have in common?]The right-click on a spreadsheet-column use case
A scientist or analyst wishes to canonicalize and then do joins with data she is using.Knowledge-powered machine learning
Allowing secondary and tertiary features and aggregates to be used in machine learning algorithms.
Knowledge-powered roboticsCommon sense reasoning; a robot needs to understand, not simply sense, its environment.
Knowledge powered startup and app developer ecosystemGenerally making it easier to write a useful app for domain X which needs to know about entities in domain Y (e.g. a great liver cancer app actually needs to know bus routes to treatment centers).
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• Existing Entity Stores
• Architecture– Catalog– Matching Engine– Facts– Normalization
Engine• Use Cases• Risks
Technical Risks• Undermerging, Overmerging, Multilevel taxonomies,
Time, Uncertainty, Provenance.• Entity stores are alive: You don’t build an entity store
once; you build a process to maintain, grow, and update a set of entities.
• Physics and Sensing: Many use cases (robotics and sensing) need to maintain information about visual, acoustic, and physics of physical-world objects.
Non-technical risks• Privacy. Very serious problem. We recommend not
including PII in such a project. There will need to be practical privacy technology in place to ask “what is the average age of women in Pittsburgh?” without having any explicit representation of all the people in Pittsburgh.
• Provenance: many major industries have their business model around obtaining facts.
• Why not leave this up to a large internet company to build? (Ans: this is bigger than Google or Apple or….)
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Open Knowledge Network
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Making progress: Open Data
…
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Making Progress: Curated Data
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Almost there ...
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…
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➔
→ →
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…
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…
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Lots of interesting problems
…
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Chuck Norris
Ryan, Oklahoma
March 10th 1940
birthplace
Actor
type
citizenOf
USA
birthdate
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Concluding
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Questions?
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Reserve slides below (may not be needed)
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Where we are now
More than 15m sites publishing snippets of structured data using schema.org, Facebook OGP, etc.Biggest problem --- getting publishers to publish is starting to see solutionSearch & personal assistants are killer app
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Many challenges remain
- Only content of interest to search engines- Centralized small schema
-- Only big players consume the data
- Crawl/index is too big a barrier
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Analogy with Web: HTML : ?
- HTML provided small set of standard terms (‘div’, ‘table’, ‘body’, etc.)
- All documents that stuck to these were understood by all browsers
- What is the equivalent here? Billions of entities ...
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Game of the name
~1000s of terms like Actor, birthdate~10s for most sitesCommon across sites
~1b-100b terms like Chuck Norris and Ryan, Oklahama Cannot expect agreement on these Need something much more sophisticated than HTML
birthplacetype
citizenOf birthdate
spouseChuck NorrisGena O’Kelley
USA
Ryan, OKActor
March 10, 1940
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Web Analogy: Search engines
- Search engines made web usable- Need something similar here
- Collect data from different publishers (Crawl)- Aggregate it (Index)- Serve (Ranking)
- Consumer here is a program, not human!
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Challenges
- Crawling: hyperlinks help web crawl. - What is the analog here? - Overlay of web pages that link to datasets?
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Challenges: Building the index
Analog of words : entitiesBuilding the index ---> large scale entity recon
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Challenges: Ranking
Single answer vs ranked set of possible answers
Ranking could be based on authoritativeness of source
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Representation
Simple graph representation is easy to understand
Can’t do a lot of things, e.g., timeHow rich should the KR lang be?
N-ary rels, negation, quantifiers, …Do we have to agree?
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Representation
Hybrid representations- structured + unstructured- embeddings
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Concluding
- Many interesting research problems
- Good news:- Lot of data already being published- First generation of apps already there- Don’t have to solve all these problems before useful
things can be done
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Questions?
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Deep pool of research
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Long history of systems
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Structured data & WebMaking structured data a first class thing on the web
Structured Data
Web server
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The Goal
Graph Data ModelCommon Vocabulary
birthplacetype
citizenOf birthdate
spouseChuck NorrisGena O’Kelley
USA
Ryan, OKActor
March 10, 1940
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Timeline of efforts
…
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Schema.orgWork started in August 2010. Google, Microsoft, Yahoo … Now also Apple, W3C …
Provides core vocabulary for people, places, events, offers, actions, ... Understood by the search engines
Search (structured data in search) was driving application
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Schema.org … the numbersApprox. 1800 terms (classes + attributes)
In use by ~15 million sites Roughly 30% of pages in search index have markup ~25 ‘triples’ per page 30% growth over last 12 months
~40% of US/EU ecommerce emails (sales confirmation, reservations, etc.) use schema.org markup
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Schema.org: Major sitesNews: Nytimes, guardian, bbc,Movies: imdb, rottentomatoes, movies.comJobs / careers: careerjet, monster, indeed, simplyhiredPeople: linkedin.com, facebookProducts: ebay, alibaba, sears, cafepress, sulit, fotoliaLocal: yelp, allmenus, urbanspoonEvents: wherevent, meetup, zillow, eventfulMusic: last.fm, soundcloud….
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…
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Game of the name
~1000s of terms like Actor, birthdate~10s for most sitesCommon across sites
~1b-100b terms like Chuck Norris and Ryan, Oklahama Cannot expect agreement on these Reference by Description Consuming applications reconcile entity references
birthplacetype
citizenOf birthdate
spouseChuck NorrisGena O’Kelley
USA
Ryan, OKActor
March 10, 1940
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What Schema.org data looks like
<h1 itemprop="name"> Chuck Norris</h1> ...
<time datetime="1940-3-10" itemprop="birthDate">
citizenOf birthdate
USA March 10, 1940
Chuck Norris/nm0001569 Actortype
birthplace
birthdatespouse
March 10, 1940Gena O’Kelley
Ryan, OKWikidata
Actortype
Carlos Ray Norris/Q2673
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birthplace
birthDate
spouse
March 10, 1940
citizenOfbirthdate
USA March 10, 1940
birthplacetype
citizenOf birthdate
spouseChuck NorrisGena O’Kelley
USA
Ryan, OKActor
March 10, 1940
Ryan, OKWikidataIMDb
Actortype
Actortype
nm0001569 Q2673
Q8392
Stitch
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Aggregate datasets
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DataCommons.org
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→
…
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Thank you