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Learning to Map between Structured Representations of Data

Learning to Map between Structured Representations of Data. AnHai Doan Database & Data Mining Group University of Washington, Seattle Spring 2002. Data Integration Challenge. Find houses with 2 bedrooms priced under 200K. New faculty member. realestate.com. homeseekers.com.

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Learning to Map between Structured Representations of Data

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  1. Learning to Map between Structured Representations of Data AnHai Doan Database & Data Mining Group University of Washington, Seattle Spring 2002

  2. Data Integration Challenge Find houses with 2 bedrooms priced under 200K New faculty member realestate.com homeseekers.com homes.com

  3. Architecture of Data Integration System Find houses with 2 bedrooms priced under 200K mediated schema source schema 1 source schema 2 source schema 3 realestate.com homeseekers.com homes.com

  4. Semantic Mappings between Schemas Mediated-schema price agent-name address 1-1 mapping complex mapping homes.com listed-price contact-name city state 320K Jane Brown Seattle WA 240K Mike Smith Miami FL

  5. Schema Matching is Ubiquitous! • Fundamental problem in numerous applications • Databases • data integration • data translation • schema/view integration • data warehousing • semantic query processing • model management • peer data management • AI • knowledge bases, ontology merging, information gathering agents, ... • Web • e-commerce • marking up data using ontologies (Semantic Web)

  6. Why Schema Matching is Difficult • Schema & data never fully capture semantics! • not adequately documented • Must rely on clues in schema & data • using names, structures, types, data values, etc. • Such clues can be unreliable • same names => different entities: area => location or square-feet • different names => same entity: area & address => location • Intended semantics can be subjective • house-style = house-description? • Cannot be fully automated, needs user feedback!

  7. Current State of Affairs • Finding semantic mappings is now a key bottleneck! • largely done by hand • labor intensive & error prone • data integration at GTE [Li&Clifton, 2000] • 40 databases, 27000 elements, estimated time: 12 years • Will only be exacerbated • data sharing becomes pervasive • translation of legacy data • Need semi-automatic approaches to scale up! • Many current research projects • Databases: IBM Almaden, Microsoft Research, BYU, George Mason, U of Leipzig, ... • AI: Stanford, Karlsruhe University, NEC Japan, ...

  8. Goals and Contributions • Vision for schema-matching tools • learn from previous matching activities • exploit multiple types of information • incorporate domain integrity constraints • handle user feedback • My contributions: solution for semi-automatic schema matching • can match relational schemas, DTDs, ontologies, ... • discovers both 1-1 & complex mappings • highly modular & extensible • achieves high matching accuracy (66 -- 97%) on real-world data

  9. Road Map • Introduction • Schema matching [SIGMOD-01] • 1-1 mappings for data integration • LSD(Learning Source Description) system • learns from previous matching activities • employs multi-strategy learning • exploits domain constraints & user feedback • Creating complex mappings [Tech. Report-02] • Ontology matching [WWW-02] • Conclusions

  10. Schema Matching for Data Integration:the LSD Approach Suppose user wants to integrate 100 data sources 1. User • manually creates mappings for a few sources, say 3 • shows LSD these mappings 2. LSDlearns from the mappings 3. LSDpredicts mappings for remaining 97 sources

  11. Learning from the Manual Mappings Mediated schema price agent-name agent-phone office-phone description If “office” occurs in name => office-phone listed-pricecontact-namecontact-phoneofficecomments Schema of realestate.com realestate.com listed-price contact-name contact-phone office comments $250K James Smith (305) 729 0831 (305) 616 1822 Fantastic house $320K Mike Doan (617) 253 1429 (617) 112 2315 Great location If “fantastic” & “great” occur frequently in data instances => description homes.com sold-at contact-agent extra-info $350K (206) 634 9435 Beautiful yard $230K (617) 335 4243 Close to Seattle

  12. Must Exploit Multiple Types of Information! Mediated schema price agent-name agent-phone office-phone description If “office” occurs in name => office-phone listed-pricecontact-namecontact-phoneofficecomments Schema of realestate.com realestate.com listed-price contact-name contact-phone office comments $250K James Smith (305) 729 0831 (305) 616 1822 Fantastic house $320K Mike Doan (617) 253 1429 (617) 112 2315 Great location If “fantastic” & “great” occur frequently in data instances => description homes.com sold-at contact-agent extra-info $350K (206) 634 9435 Beautiful yard $230K (617) 335 4243 Close to Seattle

  13. Multi-Strategy Learning • Use a set of base learners • each exploits well certain types of information • To match a schema element of a new source • apply base learners • combine their predictions using a meta-learner • Meta-learner • uses training sources to measure base learner accuracy • weighs each learner based on its accuracy

  14. Observed label (X1,C1) (X2,C2) ... (Xm,Cm) Object Classification model (hypothesis) Training examples Base Learners • Training • Matching • Name Learner • training: (“location”, address) (“contactname”, name) • matching: agent-name => (name,0.7),(phone,0.3) • Naive Bayes Learner • training: (“Seattle, WA”,address) (“250K”,price) • matching: “Kent, WA” => (address,0.8),(name,0.2) labels weighted by confidence score X

  15. The LSD Architecture Training Phase Matching Phase Mediated schema Source schemas Training data for base learners Base-Learner1 .... Base-Learnerk Meta-Learner Base-Learner1 Base-Learnerk Predictions for instances Hypothesis1 Hypothesisk Prediction Combiner Domain constraints Predictions for elements Constraint Handler Weights for Base Learners Meta-Learner Mappings

  16. Naive Bayes Learner Name Learner (“Miami, FL”, address) (“$250K”, price) (“James Smith”, agent-name) (“(305) 729 0831”, agent-phone) (“(305) 616 1822”, office-phone) (“Fantastic house”, description) (“Boston,MA”, address) (“location”, address) (“price”, price) (“contact name”, agent-name) (“contact phone”, agent-phone) (“office”, office-phone) (“comments”, description) Training the Base Learners Mediated schema addressprice agent-name agent-phone office-phone description realestate.com location price contact-name contact-phone office comments Miami, FL $250K James Smith (305) 729 0831 (305) 616 1822 Fantastic house Boston, MA $320K Mike Doan (617) 253 1429 (617) 112 2315 Great location

  17. Meta-Learner: Stacking[Wolpert 92,Ting&Witten99] • Training • uses training data to learn weights • one for each (base-learner,mediated-schema element) pair • weight (Name-Learner,address) = 0.2 • weight (Naive-Bayes,address) = 0.8 • Matching: combine predictions of base learners • computes weighted average of base-learner confidence scores area Name Learner Naive Bayes (address,0.4) (address,0.9) Seattle, WA Kent, WA Bend, OR Meta-Learner (address, 0.4*0.2 + 0.9*0.8 = 0.8)

  18. The LSD Architecture Training Phase Matching Phase Mediated schema Source schemas Training data for base learners Base-Learner1 .... Base-Learnerk Meta-Learner Base-Learner1 Base-Learnerk Predictions for instances Hypothesis1 Hypothesisk Prediction Combiner Domain constraints Predictions for elements Constraint Handler Weights for Base Learners Meta-Learner Mappings

  19. Applying the Learners homes.com schema area sold-at contact-agent extra-info area Name Learner Naive Bayes (address,0.8), (description,0.2) (address,0.6), (description,0.4) (address,0.7), (description,0.3) Meta-Learner Seattle, WA Kent, WA Bend, OR Name Learner Naive Bayes Meta-Learner Prediction-Combiner homes.com (address,0.7), (description,0.3) sold-at (price,0.9), (agent-phone,0.1) contact-agent (agent-phone,0.9), (description,0.1) extra-info (address,0.6), (description,0.4)

  20. Domain Constraints • Encode user knowledge about domain • Specified by examining mediated schema • Examples • at most one source-schema element can match address • if a source-schema element matches house-id then it is a key • avg-value(price) > avg-value(num-baths) • Given a mapping combination • can verify if it satisfies a given constraint area: address sold-at: price contact-agent: agent-phone extra-info: address

  21. The Constraint Handler • Searches space of mapping combinations efficiently • Can handle arbitrary constraints • Also used to incorporate user feedback • sold-at does not match price Predictions from Prediction Combiner Domain Constraints At most one element matches address area: (address,0.7), (description,0.3) sold-at: (price,0.9), (agent-phone,0.1) contact-agent: (agent-phone,0.9), (description,0.1) extra-info: (address,0.6), (description,0.4) 0.3 0.1 0.1 0.4 0.0012 area: address sold-at: price contact-agent: agent-phone extra-info: address 0.7 0.9 0.9 0.6 0.3402 area: address sold-at: price contact-agent: agent-phone extra-info: description 0.7 0.9 0.9 0.4 0.2268

  22. The Current LSD System • Can also handle data in XML format • matches XML DTDs • Base learners • Naive Bayes[Duda&Hart-93, Domingos&Pazzani-97] • exploits frequencies of words & symbols • WHIRL Nearest-Neighbor Classifier[Cohen&Hirsh KDD-98] • employs information-retrieval similarity metric • Name Learner [SIGMOD-01] • matches elements based on their names • County-Name Recognizer [SIGMOD-01] • stores all U.S. county names • XML Learner [SIGMOD-01] • exploits hierarchical structure of XML data

  23. Empirical Evaluation • Four domains • Real Estate I & II, Course Offerings, Faculty Listings • For each domain • created mediated schema & domain constraints • chose five sources • extracted & converted data into XML • mediated schemas: 14 - 66 elements, source schemas: 13 - 48 • Ten runs for each domain, in each run: • manually provided 1-1 mappings for 3 sources • asked LSD to propose mappings for remaining 2 sources • accuracy = % of 1-1 mappings correctly identified

  24. High Matching Accuracy Average Matching Acccuracy (%) LSD’s accuracy: 71 - 92% Best single base learner: 42 - 72% + Meta-learner: + 5 - 22% + Constraint handler: + 7 - 13% + XML learner: + 0.8 - 6%

  25. Contribution of Schema vs. Data LSD with only schema info. LSD with only data info. Complete LSD Average matching accuracy (%) More experiments in [Doan et al. SIGMOD-01]

  26. LSD Summary • LSD • learns from previous matching activities • exploits multiple types of information • by employing multi-strategy learning • incorporates domain constraints & user feedback • achieves high matching accuracy • LSD focuses on 1-1 mappings • Next challenge: discover more complex mappings! • COMAP (Complex Mapping) system

  27. 320K 53211 2 1 Seattle 98105 240K 11578 1 1 Miami 23591 The COMAP Approach • For each mediated-schema element • searches space of all mappings • finds a small set of likely mapping candidates • uses LSD to evaluate them • To search efficiently • employs a specialized searcher for each element type • Text Searcher, Numeric Searcher, Category Searcher, ... Mediated-schema price num-baths address homes.com listed-price agent-id full-baths half-baths city zipcode

  28. The COMAP Architecture [Doan et al., 02] Mediated schema Source schema + data Searcher1 Searcher2 Searcherk Mapping candidates Base-Learner1 .... Base-Learnerk Meta-Learner Prediction Combiner Domain constraints Constraint Handler LSD Mappings

  29. An Example: Text Searcher • Beam search in space of all concatenation mappings • Example: find mapping candidates for address • Best mapping candidates for address • (agent-id,0.7), (concat(agent-id,city),0.75), (concat(city,zipcode),0.9) Mediated-schema price num-baths address homes.com listed-price agent-id full-baths half-baths city zipcode 320K 532a 2 1 Seattle 98105 240K 115c 1 1 Miami 23591 concat(agent-id,city) concat(agent-id,zipcode) concat(city,zipcode) 532a Seattle 115c Miami 532a 98105 115c 23591 Seattle 98105 Miami 23591

  30. Empirical Evaluation • Current COMAP system • eight searchers • Three real-world domains • in real estate & product inventory • mediated schema: 6 -- 26 elements, source schema: 16 -- 31 • Accuracy: 62 -- 97% • Sample discovered mappings • agent-name = concat(first-name,last-name) • area = building-area / 43560 • discount-cost = (unit-price * quantity) * (1 - discount)

  31. Road Map • Introduction • Schema matching • LSD system • Creating complex mappings • COMAP system • Ontology matching • GLUE system • Conclusions

  32. Ontology Matching • Increasingly critical for • knowledge bases, Semantic Web • An ontology • concepts organized into a taxonomy tree • each concept has • a set of attributes • a set of instances • relations among concepts • Matching • concepts • attributes • relations CS Dept. US Entity Undergrad Courses Grad Courses People Faculty Staff Assistant Professor Associate Professor Professor name: Mike Burns degree: Ph.D.

  33. CS Dept. US Entity Undergrad Courses Grad Courses People Faculty Staff Assistant Professor Associate Professor Professor Matching Taxonomies of Concepts CS Dept. Australia Entity Courses Staff AcademicStaff TechnicalStaff Senior Lecturer Lecturer Professor

  34. Constraints in Taxonomy Matching • Domain-dependent • at most one node matches department-chair • a node that matches professor can not be a child of a node that matches assistant-professor • Domain-independent • two nodes match if parents & children match • if all children of X matches Y, then X also matches Y • Variations have been exploited in many restricted settings[Melnik&Garcia-Molina,ICDE-02], [Milo&Zohar,VLDB-98],[Noy et al., IJCAI-01], [Madhavan et al., VLDB-01] • Challenge: find a general & efficient approach

  35. Solution: Relaxation Labeling • Relaxation labeling [Hummel&Zucker, 83] • applied to graph labeling in vision, NLP, hypertext classification • finds best label assignment, given a set of constraints • starts with initial label assignment • iteratively improves labels, using constraints • Standard relax. labeling not applicable • extended it in many ways [Doan et al., W W W-02] • Experiments • three real-world domains in course catalog & company listings • 30 -- 300 nodes / taxonomy • accuracy 66 -- 97% vs. 52 -- 83% of best base learner • relaxation labeling very fast (under few seconds)

  36. Related Work Hand-crafted rules Exploit schema 1-1 mapping Single learner Exploit data 1-1 mapping TRANSCM [Milo&Zohar98] ARTEMIS [Castano&Antonellis99] [Palopoli et al. 98] CUPID [Madhavan et al. 01] PROMPT [Noy et al. 00] SEMINT [Li&Clifton94] ILA [Perkowitz&Etzioni95] DELTA [Clifton et al. 97] Learners + rules, use multi-strategy learning Exploit schema + data 1-1 + complex mapping Exploit domain constraints Rules Exploit data 1-1 + complex mapping CLIO [Miller et. al., 00] [Yan et al. 01] LSD [Doan et al., SIGMOD-01] COMAP [Doan et al. 2002, submitted] GLUE [Doan et al., WWW-02]

  37. Future Work • Learning source descriptions • formal semantics for mapping • query capabilities, source schema, scope, reliability of data, ... • Dealing with changes in source description • Matching objects across sources • More sophisticated user feedback • Focus on distributed information management systems • data integration, web-service integration, peer data management • goal: significantly reduce complexity of construction & maintenance

  38. Conclusions • Efficiently creating semantic mappings is critical • Developed solution for semi-automatic schema matching • learns from previous matching activities • can match relational schemas, DTDs, ontologies, ... • discovers both 1-1 & complex mappings • highly modular & extensible • achieves high matching accuracy • Made contributions to machine learning • developed novel method to classify XML data • extended relaxation labeling

  39. Backup Slides

  40. Training the Meta-Learner • For address Name Learner Naive Bayes True Predictions Extracted XML Instances <location> Miami, FL</> <listed-price> $250,000</> <area> Seattle, WA </> <house-addr>Kent, WA</> <num-baths>3</> ... 0.5 0.8 1 0.4 0.3 0 0.3 0.9 1 0.6 0.8 1 0.3 0.3 0 ... ... ... Least-SquaresLinear Regression Weight(Name-Learner,address) = 0.1 Weight(Naive-Bayes,address) = 0.9

  41. Sensitivity to Amount of Available Data Average matching accuracy (%) Number of data listings per source (Real Estate I)

  42. Contribution of Each Component Average Matching Acccuracy (%) Without Name Learner Without Naive Bayes Without Whirl Learner Without Constraint Handler The complete LSD system

  43. Exploiting Hierarchical Structure • Existing learners flatten out all structures • Developed XML learner • similar to the Naive Bayes learner • input instance = bag of tokens • differs in one crucial aspect • consider not only text tokens, but also structure tokens <contact> <name> Gail Murphy </name> <firm> MAX Realtors </firm> </contact> <description> Victorian house with a view. Name your price! To see it, contact Gail Murphy at MAX Realtors. </description>

  44. Reasons for Incorrect Matchings • Unfamiliarity • suburb • solution: add a suburb-name recognizer • Insufficient information • correctly identified general type, failed to pinpoint exact type • agent-name phoneRichard Smith(206) 234 5412 • solution: add a proximity learner • Subjectivity • house-style = description?Victorian Beautiful neo-gothic houseMexican Great location

  45. Evaluate Mapping Candidates • For address, Text Searcher returns • (agent-id,0.7) • (concat(agent-id,city),0.8) • (concat(city,zipcode),0.75) • Employ multi-strategy learning to evaluate mappings • Example: (concat(agent-id,city),0.8) • Naive Bayes Learner: 0.8 • Name Learner: “address” vs. “agent id city” 0.3 • Meta-Learner: 0.8 * 0.7 + 0.3 * 0.3 = 0.65 • Meta-Learner returns • (agent-id,0.59) • (concat(agent-id,city),0.65) • (concat(city,zipcode),0.70)

  46. Relaxation Labeling • Applied to similar problems in • vision, NLP, hypertext classification People Dept U.S. Dept Australia Courses Courses Courses Courses People Staff Faculty Staff Acad.Staff Tech.Staff Faculty Staff

  47. Relaxation Labeling for Taxonomy Matching • Must define • neighborhood of a node • k features of neighborhood • how to combineinfluence of features • Algorithm • init: for each pair <N,L>, compute • loop: for each pair <N,L>, re-compute Acad. Staff: Faculty Tech. Staff: Staff Staff = People Neighborhood configuration

  48. Relaxation Labeling for Taxonomy Matching • Huge number of neighborhood configurations! • typically neighborhood = immediate nodes • here neighborhood can be entire graph100 nodes, 10 labels => configurations • Solution • label abstraction + dynamic programming • guarantee quadratic time for a broad range of domain constraints • Empirical evaluation • GLUE system [Doan et. al., WWW-02] • three real-world domains • 30 -- 300 nodes / taxonomy • high accuracy 66 -- 97% vs. 52 -- 83% of best base learner • relaxation labeling very fast, finished in several seconds

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