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Pedro DeRose University of Wisconsin-Madison

Cimple 1.0: A Community Information Management Workbench. Pedro DeRose University of Wisconsin-Madison. Preliminary Examination. The CIM Problem. Numerous online communities database researchers, movie fans, legal professionals, bioinformatics, enterprise intranets, tech support groups

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Pedro DeRose University of Wisconsin-Madison

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  1. Cimple 1.0: A Community Information ManagementWorkbench Pedro DeRose University of Wisconsin-Madison Preliminary Examination

  2. The CIM Problem • Numerous online communities • database researchers, movie fans, legal professionals, bioinformatics, enterprise intranets, tech support groups • Each community = many data sources + many members • Database community • home pages, project pages, DBworld, DBLP, conference pages... • Movie fan community • review sites, movie home pages, theatre listings... • Legal profession community • law firm home pages

  3. The CIM Problem • Members often want to discover, query, and monitor information in the community • Database community • what is new in the past week in the database community? • any interesting connection between researchers X and Y? • find all citations of this paper in the past one week on the Web • what are current hot topics? who has moved where? • Legal profession community • which lawyers have moved where? • which law firms have taken on which cases? • Solving this problem is becoming increasingly crucial • initial efforts at UW, Y!R, MSR, Washington, IBM Almaden

  4. Planned Contributions of Thesis • Decompose the overall CIM problem • [IEEE 06, CIDR 07, VLDB 07] HV Jagadish HV Jagadish Data Sources Web Pages * * served in * * * Day 1 * SIGMOD-07 SIGMOD-07 * * * * Community Portal User services - keyword search - query - browse - mine … . . . . . . . . . . . . Leveraging the Community * * * Day n * * * * * * * Incremental Expansion

  5. Planned Contributions of Thesis • Provide concrete solutions to key sub-problem • creating ER graphs: key novelty is composing plans from operators [VLDB 07] • facilitates developing, maintaining, and optimizing • leveraging communities: wiki solution with key novelties [ICDE 08] • combines contributions from both humans and machines • combines both structured and text contributions CreateE CreateR MatchMStrict c(person, label)‏ MatchMbyName ExtractLabel main pages ExtractMbyName ExtractMbyName person entities conference entities Union \ DBLP {s1 … sn} DBLP

  6. Planned Contributions of Thesis • Capture solutions in the Cimple 1.0 workbench [VLDB 07] • empty portal shell, including basic services and admin tools • set of general operators, and means to compose them • simple implementation of operators • end-to-end development methodology • extraction/integration plan optimizers • developers can employ Cimple tools to quickly build and maintain portals • will release publicly to drive research and evaluation in CIM

  7. Planned Contributions of Thesis • Evaluate solutions and workbench on several domains • use Cimple to build portals for multiple communities • evaluate ease of development, extensibility, and accuracy of portal • have built DBLife, a portal for the database community [CIDR 07, VLDB 07] • will build a second portal for a non-research domain (e.g., movies, NFL)

  8. Planned Thesis Chapters • Selecting initial data sources • Creating the daily ER graph • Merging daily graphs into the global ER graph • Incrementally expanding sources and data • Leveraging community members • Developing the Cimple 1.0 workbench • Evaluating the solutions and workbench

  9. Selecting Initial Sources • Current solutions often use a "shotgun" approach • select as many potentially relevant sources as possible • lots of noisy sources, which can lower accuracy • Communitites often show an 80-20 phenomenon • small set of sources already covers 80% of interesting activity • Select these 20% of sources • e.g., for database community, sites of prominent researchers, conferences, departments, etc. • Can incrementally expand later • semi-automatically or mass collaboration • Crawl sources periodically • e.g., DBLife crawls ~10,000 pages (+160 MB) daily

  10. Creating the ER Graph – Current Solutions • Manual • e.g., DBLP • require a lot of human effort • Semi-automatic, but domain-specific • e.g., Yahoo! Finance, Citeseer • difficult to adapt to new domains • Semi-automatic and general • many solutions from the database, WWW, and Semantic Web communities, e.g., Rexa, Libra, Flink, Polyphonet, Cora, Deadliner • often use monolithic solutions, e.g., learning methods such as CRFs • require little human effort • can be difficult to tailor to individual communities HV Jagadish served in SIGMOD-07

  11. CreateE CreateR MatchMStrict c(person, label)‏ MatchMbyName ExtractLabel ExtractMbyName ExtractMbyName main pages person entities conference entities Union \ {s1 … sn} DBLP DBLP Proposed Solution: A Compositional Approach Discover Entities Discover Relationships HV Jagadish HV Jagadish Web Pages * * served in * * * * SIGMOD-07 SIGMOD-07 * * * *

  12. Benefits of the Proposed Solution • Easier to develop, maintain, and extend • e.g., 2 students less than 1 week to create plans for initial DBLife • Provides opportunities for optimization • e.g., extraction and integration plans allow for plan rewriting • Can achieve high accuracy with relatively simple operators by exploiting community properties • e.g., finding people names on seminar pages yields talks with 88% F1

  13. Raghu Ramakrishnan s1 … sn Creating Plans to Discover Entities CreateE MatchM ExtractM Union

  14. Creating Plans to Discover Entities (cont.) s1 … sn • These operators address well-known problems • mention recognition, entity disambiguation... • many sophisticated solutions • In DBLife, we find simple implementations can already work surprisingly well • often easy to collect entity names from community sources (e.g., DBLP)‏ • ExtractMByName: finds variations of names • entity names within a community are often unique • MatchMByName: matches mentions by name • these simple methods work with 98% F1 in DBLife CreateE MatchM ExtractM Union

  15. Union \ {s1 … sn} DBLP Extending Plans to Handle Difficult Spots CreateE • Can decide which operators to apply where • e.g., stricter operators over more ambiguous data • Provides optimization opportunities • similarly to relational query plans • see ICDE-07 for a way to optimize such plans MatchMStrict DBLP: Chen Li · · · 41. Chen Li, Bin Wang, Xiaochun Yang. VGRAM. VLDB 2007. · · · 38. Ping-Qi Pan, Jian-Feng Hu, Chen Li. Feasible region contraction. Applied Mathematics and Computation. · · · MatchMbyName ExtractMbyName ExtractMbyName DBLP

  16. Creating Plans to Discover Relationships • Categorize relations into general classes • e.g., co-occur, label, neighborhood… • Then provide operators for each class • e.g., ComputeCoStrength, ExtractLabels, neighborhood selection… • And compose them into a plan for each relation type • makes plans easier to develop • plans are relatively simple to understand • can easily add new plans for new relation types

  17. CreateR Select (strength > θ)‏ ComputeCoStrength × Union s1 person entities org entities … sn Illustrating Example: Co-occur • To find affiliated(person, org) relationship... • e.g., affiliated(Raghu, UW Madison), affiliated(Raghu, Yahoo! Research)‏ • categorized as a co-occur relationship • ...compose a simple co-occur plan • This simple plan already finds affiliations with 80% F1

  18. Illustrating Example: Label ICDE'07 Istanbul Turkey General Chair • Ling Liu • Adnan Yazici Program Committee Chairs • Asuman Dogac • Tamer Ozsu • Timos Sellis Program Committee Members • Ashraf Aboulnaga • Sibel Adali … Plan for served-in(person, conf) CreateR c(person, label)‏ ExtractLabel main pages person entities conference entities

  19. Illustrating Example: Neighborhood UCLA Computer Science Seminars Title: Clustering and Classification Speaker: Yi Ma, UIUC Contact: Rachelle Reamkitkarn Title: Mobility-Assisted Routing Speaker: Konstantinos Psounis, USC Contact: Rachelle Reamkitkarn … Plan for gave-talk(person, venue)‏ CreateR c(person, neighborhood)‏ seminar pages person entities org entities

  20. Creating Daily ER Graphs Daily ER Graph HV Jagadish HV Jagadish Data Sources Web Pages * * served in * * * Day 1 * SIGMOD-07 SIGMOD-07 * * * * Global ER Graph . . . . . . . . . . . . * * * Day n * * * * * * *

  21. Merging Daily ER Graphs Global ER Graph UIUC Enrich gave talk AnHai gave talk Match Stanford daily ER Graph global ER Graph Day n Day n+1 UIUC UIUC . . . gave talk gave talk AnHai AnHai Stanford

  22. datasources.xml http://… http://… Discover Entities Person Publication ExtractM ExtractM … * * * MatchM MatchM * * * CreateE CreateE entities.xml relationships.xml Cimple Workflow Blackboard Crawler index.xml crawledPages/… Discover Relationships Merge ER Graphs globalGraph.xml Services superhomepages, browsing, search

  23. ExtractMentions.pl #!/usr/bin/perl ################################################################### # Arguments: <moduleDir> <fileIndex> <variationsFile> # moduleDir: the relative path to the module # fileIndex: the file index of crawled files # variationsFile: a file containing mention name variations # # Finds mentions in crawled files by searching for name variations ################################################################### use dblife::utils::CrawledDataAccess; use dblife::utils::OutputAccess; # First get arguments my ($moduleDir, $fileIndex, $variationsFile) = @ARGV; # Parse the crawled file index for info by URL my %urlToInfo open(FILEINDEX, "< $fileIndex"); while(<FILEINDEX>) { if(/^<file>/) { ... } elsif(/^<\/file>/) { ... } ... } close(FILEINDEX); # Output as we go open(OUT, "> $moduleDir/output/output.xml"); ... # Search through crawled file for variations foreach my $url (keys %urlToInfo) { ... } ... close(OUT); discoverPeopleEnities.cfg # Look for names in pages and mark them as mentions EXTRACT_MENTIONS = tasks/getMentions/extractMentions $EXTRACT_MENTIONS PerlSearch $CRAWLER->index.xml $NAME_VARIATIONS # Match mentions MATCH_MENTIONS = tasks/getEntities/matchMentions $MATCH_ENTITIES RootNames $EXTRACT_MENTIONS->mentions.xml # Create entities CREATE_ENTITIES = tasks/getEntities/createEntities $CREATE_ENTITIES RootNames $MATCH_ENTITIES->mentionGroups.xml Example Plan Specification

  24. extractMentions/output.xml createEntities/output.xml <mention id="mention-537390"> <mentionType>people</mentionType> <rootName>Jeffrey F. Naughton</rootName> <nameVariation>(Jeffrey|Jefferson|Jeff)\s+Naughton</nameVariation> <filepath>www.cse.uconn.edu/icde04/icde04cfp.txt</filepath> <url>http://www.cse.uconn.edu/icde04/icde04cfp.txt</url> <mentionLeftContextLength>100</mentionLeftContextLength> <mentionRightContextLength>100</mentionRightContextLength> <mentionStartPos>3786</mentionStartPos> <mentionEndPos>3798</mentionEndPos> <mentionMatch>Jeff Naughton</mentionMatch> <mentionWindow> PROGRAM CHAIR 6. Mary Fernandez, AT&T Gail Mitchell, BBN Technologies 7. Ling Liu, Georgia Tech 8. Jeff Naughton, U. Wisc. SEMINAR CHAIR 9. Guy Lohman, IBM Almaden Mitch Cherniack, Brandeis U. 10. Panos Chrysanth</mentionWindow> </mention> <mention id="mention-1143"> <mentionType>people</mentionType> <rootName>Guy M. Lohman</rootName> <nameVariation>Guy\s+Lohman</nameVariation> <filepath>www.cse.uconn.edu/icde04/icde04cfp.txt</filepath> <url>http://www.cse.uconn.edu/icde04/icde04cfp.txt</url> <mentionLeftContextLength>100</mentionLeftContextLength> <mentionRightContextLength>100</mentionRightContextLength> <mentionStartPos>3850</mentionStartPos> <mentionEndPos>3859</mentionEndPos> <mentionMatch>Guy Lohman</mentionMatch> <mentionWindow>il Mitchell, BBN Technologies 7. Ling Liu, Georgia Tech 8. Jeff Naughton, U. Wisc. SEMINAR CHAIR 9. Guy Lohman, IBM Almaden Mitch Cherniack, Brandeis U. 10. Panos Chrysanthis, U. Pittsburgh 11. Aidong Zhang, SU</mentionWindow> </mention> matchMentions/output.xml <entity id="entity-1979"> <entityName>Daniel Urieli</entityName> <entityType>people</entityType> <mention>mention-545376</mention> <mention>mention-2510</mention> <mention>mention-2511</mention> <mention>mention-2512</mention> </entity> <entity id="entity-1980"> <entityName>Jeffrey F. Naughton</entityName> <entityType>people</entityType> <mention>mention-537390</mention> <mention>mention-537678</mention> <mention>mention-538967</mention> <mention>mention-539718</mention> <mention>mention-539973</mention> <mention>mention-539974</mention> <mention>mention-540184</mention> <mention>mention-540265</mention> <mention>mention-554093</mention> <mention>mention-540732</mention> <mention>mention-540733</mention> <mention>mention-541072</mention> <mention>mention-541073</mention> <mention>mention-541964</mention> <mention>mention-542572</mention> <mention>mention-198996</mention> <mention>mention-543725</mention> <mention>mention-544495</mention> <mention>mention-545057</mention> ... </entity> <mentionGroup> <mention>mention-545376</mention> <mention>mention-2510</mention> <mention>mention-2511</mention> <mention>mention-2512</mention> </mentionGroup> <mentionGroup> <mention>mention-537390</mention> <mention>mention-537678</mention> <mention>mention-538967</mention> <mention>mention-539718</mention> <mention>mention-539973</mention> <mention>mention-539974</mention> <mention>mention-540184</mention> <mention>mention-540265</mention> <mention>mention-554093</mention> <mention>mention-540732</mention> <mention>mention-540733</mention> <mention>mention-541072</mention> <mention>mention-541073</mention> <mention>mention-541964</mention> <mention>mention-542572</mention> <mention>mention-198996</mention> <mention>mention-543725</mention> <mention>mention-544495</mention> <mention>mention-545057</mention> ... </mentionGroup> Example Operator APIs

  25. Initial DBLife (May 31, 2005)‏ Time Data Sources (846): researcher homepages (365), department/organization homepages (94), conference homepages (30), faculty hubs (63), group pages (48), project pages (187), colloquia pages (50), event pages (8), DBWorld (1), DBLP (1)‏ 2 days, 2 persons Core Entities (489): researchers (365), department/organizations (94), conferences (30)‏ 2 days, 2 persons Operators: DBLife-specific implementation of MatchMStrict 1 day, 1 person Relation Plans (8): authored, co-author, affiliated with, gave talk, gave tutorial, in panel, served in, related topic 2 days, 2 persons Maintenance and Expansion Time Data Source Maintenance: adding new sources, updating relocated pages, updating source metadata 1 hour/month, 1 person Current DBLife (Mar 21, 2007)‏ Data Sources (1,075): researcher homepages (463), department/organization homepages (103), conference homepages (54), faculty hubs (99), group pages (56), project pages (203), colloquia pages (85), event pages (11), DBWorld (1), DBLP (1)‏ Mentions (324,188): researchers (125,013), departments/organizations (30,742), conferences (723), publication: (55,242), topics (112,468)‏ Entities (16,674): researchers (5,767), departments/organizations (162), conferences (232), publications (9,837), topics (676)‏ Relation Instances (63,923): authored (18,776), co-author (24,709), affiliated with (1,359), served in (5,922), gave talk (1,178), gave tutorial (119), in panel (135), related topic (11,725)‏ Experimental Evaluation: Building DBLife

  26. Relatively Easy to Deploy, Extend, and Debug • DBLife has been deployed and extended by several developers • CS at IL, CS at WI, Biochemistry at WI, Yahoo! Research • development started after only a few hours Q&A • Developers quickly grasped our compositional approach • easily zoomed in on target components • could quickly tune, debug, or replace individual components • e.g., new student extended ComputeCoStrength operator and added the "affiliated" plan in just a couple days

  27. Experiment Mean Recall Mean Precision Mean F1 Extracting mentions with ExtractMByName 0.99 0.98 0.98 Discovering entities with default plan 1.00 0.96 0.98 Discovering entities with source-aware plan 0.97 0.99 0.98 Finding "authored" relations (DBLP plan) 0.76 0.98 0.84 Finding "affiliated" relations (co-occurrence) 0.85 0.83 0.80 Finding "served in" relations (labels) 0.84 0.81 0.77 Finding "gave talk" relations (neighborhood) 0.87 1.00 0.88 Finding "gave tutorial" relations (labels) 0.90 1.00 0.92 Finding "on panel" relations (labels) 0.95 0.92 0.89 Matching entities across daily graphs 1.00 1.00 1.00 Accuracy in DBLife Mean accuracy over 20 randomly chosen researchers

  28. Proposed Future Work: Data Model • Requirements • relatively easy to understand and manipulate for non-technical users • temporal dimension to represent changing data • Candidate: Temporal ER Graph Day n Day n+1 SIGMOD-07 SIGMOD-07 served in served in HV Jagadish HV Jagadish mentionOf mentionOf mentionOf "Jagadish, HV" "HV Jagadish" "HV Jagadish" extractedFrom extractedFrom extractedFrom http://cse.umich.edu/... http://cse.umich.edu/...

  29. Data Model Implementation • Current implementation is ad hoc • combination of XML, RDBMS, and unstructured files • no clean separation between data and operators • Will explore a more principled implementation • efficient storage and processing • abstraction through an API

  30. Proposed Future Work: Operator Model • Identify core set of efficient operators that encompass many common CIM extraction/integration tasks • Data acquisition • query search engines • crawl sources (e.g., Crawler) • Data extraction • extract mentions (e.g., ExtractM) • discover relations (e.g., ComputeCoStrength, ExtractLabels) • Data integration • match mentions (e.g., MatchM) • match entities over time (e.g., Match operator in merge plan) • match entities across portals • Data manipulation • select subgraphs • join graphs

  31. Operator Model Extensibility • Input parameters that can be tuned for each domain • e.g., MatchMByName • "HV Jagadish" = "Jagadish, HV" • input parameters: name permutation rules • e.g., MatchMByNeighborhood • "Cong Yu,HV Jagadish,Yun Chen" = "Yun Chen,HV Jagadish,Amab Nandi" • input parameters: window size, overlapping tokens required

  32. Proposed Future Work: Plan Model • Need language for developers to compose operators • Workflow language • specifies operator order, how they are composed • used by current Cimple implementation • Declarative language • use a Datalog-like language to compose operators • existing work [Shen, VLDB 07] seems promising • provides opportunities for data-specific optimizations

  33. Planned Thesis Chapters • Selecting initial data sources • Creating the daily ER graph • Merging daily graphs into the global ER graph • Incrementally expanding sources and data • Leveraging community members • Developing the Cimple 1.0 workbench • Evaluating the solutions and workbench

  34. Incrementally Expanding • Most prior work periodically re-runs source discovery step • Cimple leverages a common community property • important new sources and entities are often mentioned in certain community sources (e.g., DBWorld)‏ • monitor these sources with simple extraction plans Message type: conf. ann. Subject: Call for Participation: VLDB Workshop on Management of Uncertain Data Call for Participation Workshop on "Management of Uncertain Data" in conjunction with VLDB 2007 http://mud.cs.utwente.nl ...

  35. Leveraging Community Members • Machine-only contributions • little human effort, reasonable initial portal, automatic updates • inaccuracies from imperfect methods, limited coverage • Human-only contributions • accurate, can provide data not in sources • significant human effort, difficult to solicit, typically unstructured • Combine contributions from both humans and machines • benefits of both • encourage human contributions with machine's initial portal • Combine both structured and text contributions • allows structured services (e.g., querying)

  36. David J. DeWitt Professor Interests:Parallel DB <# person(id=1){name}=David J. DeWitt #> <# person(id=1){title}=Professor #><strong>Interests:</strong><# person(id=1).interests(id=3).topic(id=4){name}=Parallel DB #> Illustrating Example • Madwiki solution (Machine assisted development of wikipedias) David J. DeWitt John P. Morgridge ProfessorUW-Madison since 1976Interests:Parallel DB Privacy <# person(id=1){name}=David J. DeWitt #> <# person(id=1){name}=David J. DeWitt #> <# person(id=1){title}= John P. Morgridge Professor #> <# person(id=1){organization}=UW-Madison#>since 1976 <strong>Interests:</strong><# person(id=1).interests(id=3).topic(id=4){name}=Parallel DB #><# person(id=1).interests(id=5).topic(id=6){name}=Privacy #> <# person(id=1){title}=John P. Morgridge Professor #><# person(id=1) {organization}=UW #> since 1976 <strong>Interests:</strong><# person(id=1).interests(id=3).topic(id=4){name}=Parallel DB #>

  37. V1 W1 V2 W2 G M DataSources V3 W3 V3’ W3’ u1 T T3’ The Madwiki Architecture • Community wikipedia, backed by a structured database • Challenges include • How to model underlying databases G and T • How to export a view Vi as a wiki page Wi • How to translate edits to a wiki page Wi into edits to G • How to propagate changes to G to affected wiki pages

  38. Modeling the Underlying Database G • Use an entity-relationship graph • commonly employed by current community portals • familiar to ordinary, database-illiterate users id = 6 name = Privacy id = 4 name = Parallel DB interests interests id = 7 name = Statistics interests id = 3 id = 5 id = 8 services id = 12 name = SIGMOD 02 id = 11 as =general chair id = 1 name = David J. DeWitt organization = UW-Madison

  39. Storing ER Graph G Use an RDBMS for efficient querying and concurrency Extend with temporal functionality for undo [Snodgrass 99] Entity_ID Organization_m … … … … … … Relationship_ID Organization_u … … … … … … … …

  40. Reconciling Human and Machine Edits • When edits conflict, must reconcile to choose attribute values • Reconciliation policy encoded as view over attribute tables • e.g., "latest": current value is latest value, whether human or machine • e.g., "humans-first": current value is latest human value, or latest machine value if there are no human values Organization_p … … … … … …

  41. Views over ER graph G select sub-graphs Use s-slots to represent sub-graph's data in wiki pages Exporting Views over G as Wiki Pages id = 4 name = Parallel DB interests id = 6 name = Privacy interests id = 3 id = 5 id = 1 name = David J. DeWitt organization = UW-Madison David J. DeWitt UW-Madison Interests: Parallel DB Privacy <# person(id=1){name}=David J. DeWitt #> <# person(id=1){organization}=UW-Madison#> <strong>Interests:</strong><# person(id=1).interests(id=3).topic(id=4){name}=Parallel DB #><# person(id=1).interests(id=5).topic(id=6){name}=Privacy #>

  42. Translating Wiki Edits to Database Edits • Use wiki edits to infer ER edits to underlying view • Map ER edits to RDBMS edits over G • See ICDE 08 for details of mapping <# person(id=1){name}=David J. DeWitt #> <# person(id=1){name}=David J. DeWitt #> <# person(id=1){organization}=UW #> <strong>Interests:</strong><# person(id=1).interests(id=3).topic(id=4){name}=Parallel DB #> <# person(id=1){organization}=UW- Madison #><strong>Interests:</strong><# person(id=1).interests(id=3).topic(id=4){name}=Parallel DB #> <# person(id=1).interests(id=5).topic(id=6){name}=Privacy #> id = 4 name = Parallel DB id = 4 name = Parallel DB interests interests id = 6 name = Privacy id = 3 interests id = 3 id = 5 id = 1 name = David J. DeWitt organization = UW id = 1 name = David J. DeWitt organization = UW-Madison

  43. Resolving Ambiguous Wiki Edits • Users can edit data, views, and schemas • change the attribute age of person X to 42 • display the attribute homepage of conference entities on wiki pages • add the new attribute pages to the publication entity schema • Wiki edits can be ambiguous • change the attribute title of person X to NULL • do not display the attribute title of people entities on wiki pages • delete the attribute title from the people entity schema • Recognize ambiguous edits, then ask users to clarify intention

  44. Propagating Database Edits to Wiki Pages • Update wiki pages when G changes • similar to view update • Eager propagation • pre-materialize wiki text, and immediately propagate all changes • raises complex concurrency issues • Simpler alternative: lazy propagation • materialize wiki text on-the-fly when requested by users • underlying RDBMS manages edit concurrency • preliminary evaluation indicates lazy propagation is tractable

  45. Madwiki Evaluation • S-slot expressiveness • prototype shows s-slots can express all structured data in DBLife superhomepages except aggregation and top-k • Materializing and serving wiki pages • materializing and serving time increases linearly with page size • vast majority of current pages are small, hence served in < 1 second

  46. Proposed Future Work • Structured data in wiki pages • s-slot tags can be confusing and cumbersome to users • propose to explore a structured data representation closer to natural text • key challenge: how to avoid requiring an intractable NLP solution • Reconciling conflicting edits • preliminary work identifies problem, does not provide satisfactory solution • propose to explore reconciling edits by learning editor trustworthiness • lots of existing work, but not in CIM settings • key challenge: edits are sparse, and not missing at random

  47. Developing the Cimple 1.0 Workbench • Set of tools for compositional portal building • empty portal shell, including basic services and admin tools • browsing, keyword search… • set of general operators, and means to compose them • MatchM, ExtractM… • simple implementation of operators • MatchMbyName, ExtractMbyName… • end-to-end development methodology • 1. select sources, 2. discover entities… • extraction/integration plan optimizers • see VLDB 07 for an optimization solution

  48. Proposed Evaluation: A Second Domain • Use Cimple to build portal for non-research domain • Evaluate portal's ease of development, extensibility, and accuracy • Movie Domain • movie, actor, director, appeared in, directed… • sources include critic homepages, celebrity news sites, fan sites • existent portals (e.g., IMDB), arguably an unfair advantage • NFL Domain • player, coach, team, played for, coached… • sources include homepages for players, teams, tournaments, stadiums • Digital Camera Domain • manufactureres, cameras, stores, reviewers, makes, sells, reviewed… • sources include manufacturer homepages, online stores, reviewer sites

  49. Conclusions • CIM is an interesting and increasingly crucial problem • many online communities • initial efforts at UW, Y!R, MSR, Washington, IBM Almaden • Preliminary work on CIM seems promising • decomposed CIM and developed initial solutions • began work on the Cimple 1.0 workbench • developed the DBLife portal • Much work remains to realize the potential of this approach • formal data, operator, and plan model • more natural editing and robust reconciliation for Madwiki • Cimple 1.0 workbench • a second portal

  50. Proposed Schedule • February • formal data, operator, and plan model for Cimple • March • solution for reconciling human and machine edits based on trust • June • more natural structured data representation for Madwiki wiki text • July • completion of Cimple 1.0 beta workbench • August • deployment of second portal built using the Cimple 1.0 workbench • public release of Cimple 1.0 workbench

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