Big Text from Language to Knowledge Gerhard Weikum

Big Text: from Language to Knowledge Gerhard Weikum Max Planck Institute for Informatics & Saarland University Saarbrücken, Germany http: //www. mpi-inf. mpg. de/~weikum/

From Natural-Language Text to Knowledge more knowledge, analytics, insight knowledge acquisition Web Contents Knowledge intelligent interpretation

Web of Data & Knowledge (Linked Open Data) > 50 Bio. subject-predicate-object triples from > 1000 sources Read. The. Web Cyc Babel. Net SUMO Text. Runner/ Re. Verb Concept. Net 5 Wiki. Taxonomy/ Wiki. Net http: //richard. cyganiak. de/2007/10/lod-datasets_2011 -09 -19_colored. png

Web of Data & Knowledge > 50 Bio. subject-predicate-object triples from > 1000 sources • 10 M entities in 350 K classes • 120 M facts for 100 relations • 100 languages • 95% accuracy • 4 M entities in 250 classes • 500 M facts for 6000 properties • live updates • 600 M entities in 15000 topics • 20 B facts • 40 M entities in 15000 topics • 1 B facts for 4000 properties • core of Google Knowledge Graph

Web of Data & Knowledge > 50 Bio. subject-predicate-object triples from > 1000 sources Bob_Dylan type songwriter taxonomic knowledge Bob_Dylan type civil_rights_activist songwriter subclass. Of artist Bob_Dylan composed Hurricane factual knowledge Hurricane is. About Rubin_Carter Steve_Jobs married. To Sara_Lownds temporal knowledge valid. During [Sep-1965, June-1977] Bob_Dylan known. As „voice of a generation“ terminological knowledge Steve_Jobs „was big fan of“ Bob_Dylan „briefly dated“ Joan_Baez evidence & belief knowledge

Knowledge for Intelligent Applications Enabling technology for: • disambiguation in written & spoken natural language • deep reasoning (e. g. QA to win quiz game) • machine reading (e. g. to summarize book or corpus) • semantic search in terms of entities&relations (not keywords&pages) • entity-level linkage for Big Data & Big Text analytics

Big Text Analytics: Who Covered Whom? 1000‘s of Databases in different language, country, key, … with more sales, awards, media buzz, … 100 Mio‘s of Web Tables 100 Bio‘s of Web &. . . Social Media Pages Musician Original Title Hannes Wader Elvis Presley Tote Hosen. . . Elvis Presley F. Silcher Hannes Wader. . . Wooden Heart Muss i denn Heute hier morgen dort. . .

Big Text Analytics: Who Covered Whom? 1000‘s of Databases in different language, country, key, … with more sales, awards, media buzz, … 100 Mio‘s of Web Tables 100 Bio‘s of Web &. . . Big Data & Big Text: Social Media Pages Musician challenge Variety & Veracity. Musician Performed. Title Elvis F. Silcher Hans E. Wader. . . Hannes Wader Wooden Heart Hannes Wader Heute Hier Tote Hosen Morgen Dort Name Place U 2 Dublin Dagstuhl Wadern n r e W ad Created. Title Wood Heart Muss i denn Heute Hier. . . Name Group Bono U 2 Campino Tote Hosen

Big Data & Big Text Analytics Entertainment: Who covered which other singer? Who influenced which other musicians? Health: Drugs (combinations) and their side effects Politics: Politicians‘ positions on controversial topics and their involvement with industry Business: Customer opinions on small-company products, gathered from social media Culturomics: Trends in society, cultural factors, etc. General Design Pattern: • Identify relevant contents sources • Identify entities of interest & their relationships • Position in time & space • Group and aggregate • Find insightful patterns & predict trends 9

Outline Introduction Lovely NERD The New Chocolate The Dark Side Conclusion

Lovely NERD

Named Entity Recognition & Disambiguation (NERD) Hurricane, about Carter, is on Bob‘s Desire. It is played in the film with Washington. contextual similarity: mention vs. entity (bag-of-words, language model) prior popularity of name-entity pairs

Named Entity Recognition & Disambiguation Coherence of entity pairs: (NERD) • semantic relationships • shared types (categories) • overlap of Wikipedia links Hurricane, about Carter, is on Bob‘s Desire. It is played in the film with Washington.

Named Entity Recognition & Disambiguation Coherence: (partial) overlap of (statistically weighted) entity-specific keyphrases racism protest song boxing champion wrong conviction Hurricane, about Carter, is on Bob‘s Desire. It is played in the film with Washington. racism victim middleweight boxing nickname Hurricane falsely convicted Grammy Award winner protest song writer film music composer civil rights advocate Academy Award winner African-American actor Cry for Freedom film Hurricane film

Named Entity Recognition & Disambiguation Hurricane, about Carter, is on Bob‘s Desire. It is played in the film with Washington. KB provides building blocks: • • NED algorithms compute mention-to-entity mapping over weighted graph of candidates by popularity & similarity & coherence name-entity dictionary, relationships, types, text descriptions, keyphrases, statistics for weights

Joint Mapping m 1 m 2 50 30 20 30 100 m 3 m 4 e 1 e 2 e 3 30 e 4 90 100 5 e 5 50 10 10 90 20 80 30 90 e 6 • Build mention-entity graph or joint-inference factor graph from knowledge and statistics in KB • Compute high-likelihood mapping (ML or MAP) or dense subgraph (with high total edge weight) such that: each m is connected to exactly one e (or at most one e) 16

Coherence Graph Algorithm m 1 m 2 50 30 20 30 m 4 180 e 1 e 2 50 e 3 30 470 e 4 90 100 5 145 e 5 230 e 6 100 m 3 140 50 10 10 90 20 80 30 90 • Compute dense subgraph to maximize min weighted degree among entity nodes such that: each m is connected to exactly one e (or at most one e) • Approx. algorithms (greedy, randomized, …), hash sketches, … • 82% precision on Co. NLL‘ 03 benchmark • Open-source software & online service AIDA D 5 Overview May 14, http: //www. mpi-inf. mpg. de/yago-naga/aida/ 17

NERD at Work https: //gate. d 5. mpi-inf. mpg. de/webaida/

NERD auf Deutsch

NERD on Tables

Entity Matching in Structured Data Variety & Veracity ! Hurricane 1975 Forever Young 1972 Like a Hurricane 1975 ………. Dylan Hurricane Katrina New Orleans 2005 Hurricane Like a Hurricane Young Hurricane Sandy New York 2012 Hurricane Everette. ………. ? Dylan Thomas Young Denny Bob Dylan Brigham Neil Sandy 1941 Swansea 1914 1801 Toronto 1945 London 1947 entity linkage: • key to data integration • long-standing problem, very difficult, unsolved H. L. Dunn: Record Linkage. American Journal of Public Health 36 (12), 1946 H. B. Newcombe et al. : Automatic Linkage of Vital Records. Science 130 (3381), 1959

Linking Big Data & Big Text Musician Song Bob Dylan Death is not … Bob Dylan Don‘t think twice Bob Dylan Make you feel … Nick Cave Death is not … Kronos Q. Don‘t think twice Adele Make you feel … H. Wader Heute hier. . . Tote Hosen Heute hier … Year Listeners 1988 14 218 1962 319 588 1997 72 468 1996 85 333 2012 679 2008 559 715 1972 2 630 2012 6 432 Charts . . .

Outline Introduction Lovely NERD The New Chocolate The Dark Side Conclusion

Big Text: the New Chocolate

Semantic Search over News https: //stics. mpi-inf. mpg. de

Entity Analytics over News https: //stics. mpi-inf. mpg. de

Machine Reading of Scholarly Papers https: //gate. d 5. mpi-inf. mpg. de/knowlife/

Machine Reading of Health Forums https: //gate. d 5. mpi-inf. mpg. de/knowlife/

Big Data & Text Analytics: Side Effects of Drug Combinations Structured Expert Data http: //dailymed. nlm. nih. gov Deeper insight from both expert data & social media: • actual side effects of drugs • … and drug combinations • risk factors and complications of (wide-spread) diseases • alternative therapies • aggregation & comparison by age, gender, life style, etc. Social Media http: //www. patient. co. uk

Machine Reading: from Names and Phrases to Entities, Classes, and Relations The Maestro from Rome wrote scores for westerns. Ma played his version of the Ecstasy. Maestro Card Leonard Bernstein Ennio Morricone born in plays for Rome (Italy) AS Roma Lazio Roma goal in football film music Jack Ma MDMA Yo-Yo Ma plays sport plays music l‘Estasi dell‘Oro cover of story about western movie Western Digital

Disambiguation for Entities, Classes & Relations Maestro from ILP optimizers like Gurobi solve this in seconds Rome wrote scores for westerns e: Maestro. Card e: Ennio Morricone c: conductor c: musician r: acted. In r: born. In e: Rome (Italy) e: Lazio Roma r: composed r: give. Exam c: soundtrack r: soundtrack. For r: shoots. Goal. For c: western movie e: Western Digital Combinatorial Optimization by ILP (with type constraints etc. ) weighted edges (coherence, similarity, etc. ) (M. Yahya et al. : EMNLP’ 12, CIKM‘ 13)

Outline Introduction Lovely NERD The New Chocolate The Dark Side Conclusion

The Dark Side of Big Data

Zoe Entity Linking: Privacy at Stake publish & recommend search discuss & seek help female 25 -30 Somalia female 29 y Jamame Synthroid tremble ………. Addison disorder ………. Cry Nive Nielsen Freedom social network online forum Internet Levothroid shaking Addison’s disease ……… Nive concert Greenland singers Somalia elections Steve Biko search engine

Privacy Adversaries publish & recommend search discuss & seek help Linkability Threats: § Weak cues: profiles, friends, etc. § Semantic cues: health, taste, queries § Statistical cues: correlations female 25 -30 Somalia female 29 y Jamame Synthroid tremble ………. Addison disorder ………. Cry Nive Nielsen Freedom social network online forum Internet Levothroid shaking Addison’s disease ……… Nive concert Greenland singers Somalia elections Steve Biko search engine

Goal: Automated Privacy Advisor publish & recommend discuss & seek help search female 25 -30 Somalia Privacy Adviser (PA): Software tool that § analyses risk § alerts user § advises user • explains consequences • recommends policy changes female 29 y Jamame Levothroid shaking Synthroid tremble Addison’s disease ………. Addison disorder ……… Your queries may lead to linking your identies ………. Nive concert in Facebook and patient. co. uk ! Greenland singers …………. Somalia elections Cry Nive Nielsen Would you like to use an anonymization tool Freedom for your search requests? social ………. . online search network forum ERC Project im. PACT engine Internet (Backes/Druschel/Majumdar/Weikum)

Outline Introduction Lovely NERD The New Chocolate The Dark Side Conclusion

Big Text & Big Data Big Text & NERD: valuable content about entities lifted towards knowledge & analytic insight Machine Reading: discover and interpret names & phrases as entities, classes, relations, spatio-temporal modifiers, sentiments, beliefs, …. Big Data: interlink natural-language text, social media, structured data & knowledge bases, images, videos and help users coping with privacy risks

Take-Home Message: From Language to Knowledge more knowledge, analytics, insight knowledge acquisition Web Contents Knowledge intelligent interpretation „Who Covered Whom? “ and More! (Entities, Classes, Relations)