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Semex: A Platform for Personal Information Management and Integration

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Semex: A Platform for Personal Information Management and Integration

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    1. Semex: A Platform for Personal Information Management and Integration Xin (Luna) Dong University of Washington June 24, 2005

    2. Is Your Personal Information a Mine or a Mess? Mention Tim-Bernslee PIM workshop last VLDB?Mention Tim-Bernslee PIM workshop last VLDB?

    3. Is Your Personal Information a Mine or a Mess? Mention Tim-Bernslee PIM workshop last VLDB?Mention Tim-Bernslee PIM workshop last VLDB?

    4. Questions Hard to Answer Where are my SEMEX papers and presentation slides (maybe in an attachment)?

    5. Index Data from Different Sources E.g. Google, MSN desktop search Mention Tim-Bernslee PIM workshop last VLDB?Mention Tim-Bernslee PIM workshop last VLDB?

    6. Questions Hard to Answer Where are my SEMEX papers and presentation slides (maybe in an attachment)? Who are working on SEMEX? What are the emails sent by my PKU alumni? What are the phone numbers and emails of my coauthors?

    7. Organize Data in a Semantically Meaningful Way Mention Tim-Bernslee PIM workshop last VLDB?Mention Tim-Bernslee PIM workshop last VLDB?

    8. Questions Hard to Answer Where are my SEMEX papers and presentation slides (maybe in an attachment)? Who are working on SEMEX? What are the emails sent by my PKU alumni? What are the phone numbers and emails of my coauthors? Whom of SIGMOD’05 authors do I know?

    9. Integrate Organizational and Public Data with Personal Data Mention Tim-Bernslee PIM workshop last VLDB?Mention Tim-Bernslee PIM workshop last VLDB?

    11. SEMEX (SEMantic EXplorer) – I. Provide a Logical View of Data

    12. SEMEX (SEMantic EXplorer) – II. On-the-fly Data Integration

    13. How to Find Alon’s Papers on My Desktop?

    14. How to Find Alon’s Papers on My Desktop? – Google Search Results

    15. How to Find Alon’s Papers on My Desktop? – Google Search Results

    16. How to Find Alon’s Papers on My Desktop? – Google Search Results

    17. Semex Goal Build a Personal Information Management (PIM) system prototype that provides a logical view of personal information Build the logical view automatically Extract object instances and associations Remove instance duplications Leverage the logical view for on-the-fly data integration Exploit the logical view for information search and browsing to improve people’s productivity Be resilient to the evolution of the logical view

    18. An Ideal PIM is a Magic Wand

    19. An Ideal PIM is a Magic Wand

    20. Outline Problem definition and project goals Technical issues: System architecture and instance extraction [CIDR’05] Reference reconciliation [Sigmod’05] On-the-fly data integration Association search and browsing Domain model personalization and evolution [WebDB’05] Interleaved with Semex demo [Best demo in Sigmod’05] Overarching PIM Themes

    21. System Architecture

    22. Outline Problem definition and project goals Technical issues: System architecture and instance extraction [CIDR’05] Reference reconciliation [Sigmod’05] On-the-fly data integration Association search and browsing Domain model personalization and evolution [WebDB’05] Interleaved with Semex demo [Best demo in Sigmod’05] Overarching PIM Themes

    23. Reference Reconciliation in Semex

    24. Semex Without Reference Reconciliation

    25. Semex Without Reference Reconciliation

    26. Semex Without Reference Reconciliation

    27. Semex Without Reference Reconciliation

    28. Semex NEEDS Reference Reconciliation

    29. Reference Reconciliation A very active area of research in Databases, Data Mining and AI. (Surveyed in [Cohen, et al. 2003]) Traditional approaches assume matching tuples from a single table Based on pair-wise comparisons Harder in our context

    30. Challenges Article: a1=(“Bounds on the Sample Complexity of Bayesian Learning”, “703-746”, {p1,p2,p3}, c1) a2=(“Bounds on the sample complexity of bayesian learning”, “703-746”, {p4,p5,p6}, c2) Venue: c1=(“Computational learning theory”, “1992”, “Austin, Texas”) c2=(“COLT”, “1992”, null) Person: p1=(“David Haussler”, null) p2=(“Michael Kearns”, null) p3=(“Robert Schapire”, null) p4=(“Haussler, D.”, null) p5=(“Kearns, M. J.”, null) p6=(“Schapire, R.”, null)

    31. Challenges Article: a1=(“Bounds on the Sample Complexity of Bayesian Learning”, “703-746”, {p1,p2,p3}, c1) a2=(“Bounds on the sample complexity of bayesian learning”, “703-746”, {p4,p5,p6}, c2) Venue: c1=(“Computational learning theory”, “1992”, “Austin, Texas”) c2=(“COLT”, “1992”, null) Person: p1=(“David Haussler”, null) p2=(“Michael Kearns”, null) p3=(“Robert Schapire”, null) p4=(“Haussler, D.”, null) p5=(“Kearns, M. J.”, null) p6=(“Schapire, R.”, null) p7=(“Robert Schapire”, “schapire@research.att.com”) p8=(null, “mkearns@cis.uppen.edu”) p9=(“mike”, “mkearns@cis.uppen.edu”)

    32. Intuition Complex information spaces can be considered as networks of instances and associations between the instances Key: exploit the network, specifically, the clues hidden in the associations

    33. I. Exploiting Richer Evidences Cross-attribute similarity – Name&email p5=(“Stonebraker, M.”, null) p8=(null, “stonebraker@csail.mit.edu”) Context Information I – Contact list p5=(“Stonebraker, M.”, null, {p4, p6}) p8=(null, “stonebraker@csail.mit.edu”, {p7}) p6=p7 Context Information II – Authored articles p2=(“Michael Stonebraker”, null) p5=(“Stonebraker, M.”, null) p2 and p5 authored the same article

    34. Considering Only Attribute-wise Similarities Cannot Merge Persons Well

    35. Considering Richer Evidence Improves the Recall

    36. II. Propagate Information between Reconciliation Decisions Article: a1=(“Distributed Query Processing”,“169-180”, {p1,p2,p3}, c1) a2=(“Distributed query processing”,“169-180”, {p4,p5,p6}, c2) Venue: c1=(“ACM Conference on Management of Data”, “1978”, “Austin, Texas”) c2=(“ACM SIGMOD”, “1978”, null) Person: p1=(“Robert S. Epstein”, null) p2=(“Michael Stonebraker”, null) p3=(“Eugene Wong”, null) p4=(“Epstein, R.S.”, null) p5=(“Stonebraker, M.”, null) p6=(“Wong, E.”, null)

    37. Propagating Information between Reconciliation Decisions Further Improves Recall

    38. III. Reference Enrichment p2=(“Michael Stonebraker”, null, {p1,p3}) p8=(null, “stonebraker@csail.mit.edu”, {p7}) p9=(“mike”, “stonebraker@csail.mit.edu”, null) p8-9 =(“mike”, “stonebraker@csail.mit.edu”, {p7})

    39. References Enrichment Improves Recall More than Information Propagation

    40. Applying Both Information Propagation and Reference Enrichment Gets the Highest Recall

    41. Outline Problem definition and project goals Technical issues: System architecture and instance extraction [CIDR’05] Reference reconciliation [Sigmod’05] On-the-fly data integration Association search and browsing Domain model personalization and evolution [WebDB’05] Interleaved with Semex demo [Best demo in Sigmod’05] Overarching PIM Themes

    42. Importing External Data Sources

    43. Traditional approaches: proceed in two steps Step 1. Schema matching (Surveyed in [Rahm&Bernstein, 2001]) Generate term matching candidates E.g., “paperTitle” in table Author matches “title” in table Article Step 2. Query discovery [Miller et al., 2000] Take term matching as input, generate mapping expressions (typically queries) E.g., SELECT Article.title as paperTitle, Person.name as author FROM Article, Person WHERE Article.author = Person.id Intuition— Explore associations in schema mapping

    44. Traditional approaches: proceed in two steps Step 1. Schema matching (Surveyed in [Rahm&Bernstein, 2001]) Generate term matching candidates E.g., “paperTitle” in table Author matches “title” in table Article Step 2. Query discovery [Miller et al., 2000] Take term matching as input, generate mapping expressions (typically queries) E.g., SELECT Article.title as paperTitle, Person.name as author FROM Article, Person WHERE Article.author = Person.id User’s input is needed to fill in the gap between Step 1 output and Step 2 input Our approach: check association violations to filter inappropriate matching candidates Intuition— Explore associations in schema mapping

    45. Integration Example

    46. Integration Example

    47. Outline Problem definition and project goals Technical issues: System architecture and instance extraction [CIDR’05] Reference reconciliation [Sigmod’05] On-the-fly data integration Association search and browsing Domain model personalization and evolution [WebDB’05] Interleaved with Semex demo [Best demo in Sigmod’05] Overarching PIM Themes

    48. Explore the association network – 1. Find the relationship between two instances Example: How did I know this person? Solution: Lineage Find an association chain between two object instances Shortest chain? “Earliest” chain OR “Latest” chain

    49. Explore the association network – 2. Find all instances related to a given keyword Example: Who are working on “Schema Matching”? Solution: Naive approach: index object instances on attribute values ?A list of papers on schema matching ?A list of emails on schema matching ?A list of persons working on schema matching ?A list of conferences for schema-matching papers ?A list of institutes that conduct schema-matching research Our approach: index objects on the attributes of associated objects

    50. Explore the association network – 3. Rank returned instances in a keyword search Example: What are important papers on “schema matching”? Solution: Naive approach: rank by TF/IDF metric Our approach: ranking by Significance score: PageRank measure Relevance score: TF/IDF metric Usage score: last visit time and modification time

    51. Explore the association network – 4. Fuzzy Queries Queries we pose today—something we can describe Find me something with (related to) keyword X Find me the co-authors of Person Y Fuzzy queries: Q: What do I want to know? A: In this webpage, 5 papers are written by your friends Q: What significant things have happened today? A: The President wrote an email to you!!

    52. Outline Problem definition and project goals Technical issues: System architecture and instance extraction [CIDR’05] Reference reconciliation [Sigmod’05] On-the-fly data integration Association search and browsing Domain model personalization and evolution [WebDB’05] Interleaved with Semex demo [Best demo in Sigmod’05] Overarching PIM Themes

    53. The Domain Model

    54. Problems in Domain Model Personalization Problem: hard to precisely model a domain At certain point we are not able to give a precise domain model Not enough knowledge of the domain Inherently evolution of a domain Non-existence of a precise model Overly detailed models may be a burden to users Modeling every details of the information on one’s desktop is often overwhelming We may want to leave part of the domain unstructured Extract descriptions at different levels of granularity Address v.s. street, city, state, zip

    55. Malleable Schemas

    56. Malleable Schema Introduce “text” into schemas Phrases as element names E.g., “InitialPlanningPhaseParticipant” Regular expressions as element names E.g., “*Phone”, “State|Province” Chains as element names E.g., “name/firstName” Introduce imprecision into queries SELECT S.~name, S.~phone FROM Student as S, ~Project as P WHERE (S ~initialParticipant P) AND (P.name = “Semex”)

    57. Outline Problem definition and project goals Technical issues: System architecture and instance extraction [CIDR’05] Reference reconciliation [Sigmod’05] On-the-fly data integration Association search and browsing Domain model personalization and evolution [WebDB’05] Interleaved with Semex demo [Best demo in Sigmod’05] Overarching PIM Themes

    58. Overarching PIM Themes It is PERSONAL data! How to build a system supporting users in their own habitat? How to create an ‘AHA!’ browsing experience and increase user’s productivity? There can be any kind of INFORMATION How to combine structured and un-structured data? We are pursuing life-long data MANAGEMENT What is the right granularity for modeling personal data? How to manage data and schema that evolve over time?

    59. Related Work Personal Information Management Systems Indexing Stuff I’ve Seen (MSN Desktop Search) [Dumais et al., 2003] Google Desktop Search [2004] Richer relationships MyLifeBits [Gemmell et al., 2002] Placeless Documents [Dourish et al., 2000] LifeStreams [Freeman and Gelernter, 1996] Objects and associations Haystack [Karger et al., 2005]

    60. Summary 60 years passed since the personal Memex was envisioned It’s time to get serious Great challenges for data management Deliverables of the project An approach to automatically build a database of objects and associations from personal data An algorithm for on-the-fly integration Algorithms for data analysis for association search and browsing The concept of malleable schema as a modeling tool A PIM system incorporating the above

    61. Association Network for Semex

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