Demo 11/2/2008. Who are we? True Knowledge: Open-domain question answering using structured knowledge and inference. Who am I?

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1 Who are we? True Knowledge: Open-domain question answering using structured knowledge and inference William Tunstall-Pedoe CEO, True Knowledge Ltd 23 rd of October 2008 Cambridge (UK) based start-up VC backed $4m raised July people and actively hiring! 3 years old (10 yrs research) Private beta invites available to anyone here! william.tp@trueknowledge.com 23rd October Who am I? William Tunstall-Pedoe william.tp@trueknowledge.com Cambridge University Computer Science grad Entrepreneur rather than academic or researcher Inventor of the True Knowledge Technology Founder & CEO of True Knowledge Background in developing AI products Open domain question answering platform Direct answers Any domain Understanding the question Structured Knowledge 23rd October rd October Demo 23rd October rd October

2 How it works Architecture Browser Interface Natural Language Translation Knowledge Addition Other Other Sources Sources User Assessment System Assessment Query/Answer System Knowledge Base A P I Knowledge Generator External feeds

3 Ingredients to achieve Open Domain How we represent knowledge (visual) Complete tree of classes Every entity is a member of multiple classes Distinctness knowledge known Unique Recognition String (URS) for every entity John Sidney McCain III, the senior United States Senator from Arizona and presidential nominee of the Republican Party in the 2008 presidential election Disambiguation techniques Simple knowledge representation Inference was built in 1856 Is an instance of Big Ben London The UK Is an instance of City Is a subclass of Timepoint Geographical area can denote Is an instance of Is a subclass of Country Representation Basic KR is very simple Three* field database table: [london] [is an instance of] [city] Temporal knowledge represented by facts about facts [fact: [ 123 ]] [applies for timeperiod] [<1970 onwards>] Richness within entity representation parametered objects [integer: [ 8128 ]] [group: [london]; [san francisco]] Query language Queries are like our KR but with variables. e.g: Who is the president of the us? query a [current time] [applies to] now f: a [is the president of] [the united states of america] f [applies at timepoint] now * Actually four as relations can be negative (or five including fact id) We use inference to extend knowledge Inference was built in 1856 Big Ben The UK London Inference achieved with rules ( generators ) invoked during query processing Rules designed to be as general as possible Query processing integrates use of these rules in a efficient way Explanations generated along the way Inference can also be used to validate and reject new knowledge System assessment

4 Example dumb generator Embodies symmetry. e.g. a is married to b implies b is married to a for the same period of time: generator a%,b%,tr [symmetric] [applies to] r$ f: a% r$ b% f [applies for timeperiod] tr => g: b% r$ a% * g [applies for timeperiod] tr Example smart generator Smart generators have attached program code Can thus do any calculation that can be done on a computer Example gives day of week for any date: generator day$ [is an instance of] [day of the week] =>dayofweeksmart@local [timepoint: string$] [is an instance of] day$ * Translation Turns natural language questions into queries Basic approach is via templates Each template gives method for translating a class of questions the larger the better Disambiguation is used to remove unlikely interpretations after translation Example template What is the capital of france? What is the age of the Eiffel Tower? Match: "what"/"which" a y Header: query r,d a [is a present central form of] r y [can denote] d Translation: query b [current time] [applies to] now f: b r d f [applies at timepoint] now Disambiguation Throws out unlikely interpretations of q Using knowledge e.g. Is Madonna single? Can eliminate Madonna, the place in Maryland Madonna the movie Scope of [unmarried] is [human being] Can infer places and movies are not Music interpretation of single would be ignored by template Similar technique used for left/right side of relations Progress 23 Octopus Ventures / 19 March

5 Users add knowledge and it is useful Several orders of magnitude Knowledge Growth True Knowledge : performance since beta launch Thousands Users Beta applicants Facts added by beta testers Facts 20% of users add facts 20% of answers use useradded facts 80k facts (June 08) Facts Objects Beta users Dec Jan 0Feb Mar 5 11/01/ /02/ /05/ /08/ /11/ /02/ /03/ /05/ /06/ /08/ /09/ /11/ /12/ /02/ /03/ /05/ /06/2008 Number of relations and classes 7000 Ability to answer questions is improving Classes Relations % /01/ /02/ /04/ /05/ /07/ /09/ /10/ /12/ /02/ /03/ /05/ /06/ /08/ /10/ /11/ /01/ /04/ /06/ /02/ /02/ /02/ /03/ /03/ /03/ /04/ /04/ /04/ /05/ /05/ /05/ /06/ /06/ /06/ The The opportunity future Section page Knowledge Addition Perfect response only possible when knowledge is in repository Want to enable orders of magnitude more knowledge through platform Improved crowd sourcing More imports NLP Live sources Success by others 30 5

6 Other languages Web knowledge hidden by a veil of language Growth is non English 1 billion current users of internet 5 billion non users of internet Most don t speak English Our technology can enable users to interact in multiple languages but get answers from the shared resource Just need Lexical knowledge for new language Separate catalog of translation templates 31 Questions? william.tp@trueknowledge.com