Managing Belief Annotations in Databases: A Modal Logic Approach

Explore the concept of belief databases that enable data curation based on modal and default logic in a relational model. The work discusses managing inconsistent views in community databases and presents a motivating application scenario to illustrate the challenges and solutions in handling belief annotations.

• Belief Databases
• Modal Logic
• Data Curation
• Relational Model
• Community Databases

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1. August 25, VLDB 2009 Believe It or Not Adding belief annotations to databases Wolfgang Gatterbauer, Magda Balazinska, Nodira Khoussainova, and Dan Suciu University of Washington http://db.cs.washington.edu/beliefDB/

2. High-level overview DBMS: manage consistent data Applications need inconsistent DM Scientific databases Internet community databases Community DBMS: manage inconsistent views reason: disagreement ! This work: Belief databases manage data and curation grounded in modal and default logic implemented on top of relational model 2

3. Agenda Motivating example Logic foundations Relational implementation Discussion 3

4. Motivating application NatureMapping project (http://depts.washington.edu/natmap/) volunter contribute animal observations one person curates the database problem: does not scale! Observations id uid species date location comment 2 Alice Crow 06-14-08 Lake Placid found feathers Sightings (S) sid uid species date location s2 Alice Crow 06-14-08 Lake Placid Comments (C) cid comment sid c1 found feathers s2 4

5. 1. Distinct database instances S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid Alice S sid uid species date location s2 Alice Raven 06-14-08 Lake Placid Bob D1: Belief worlds: individually consistent, mutually possibly inconsistent 5

6. 1. Distinct database instances S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid Alice S sid uid species date location s2 Alice Raven 06-14-08 Lake Placid Bob Q: Who believes something different than Alice and what? BeliefSQL I: Alice believes that she saw a crow. select U2.name, S1.species, S2.species from Users as U, BELIEF Alice Sightings as S1, BELIEF U.uid Sightings as S2, where S1.sid = S2.sid and S1.species <> S2.species A: {( Bob , Crow , Raven )} insert into BELIEF Alice Sightings values ( s2 , Alice , Crow , 06-14-08 , Lake Placid ) I: Bob believes that she actually saw a raven. insert into BELIEF Bob Sightings values ( s2 , Alice , Raven , 06-14-08 , Lake Placid ) 6

7. 2. Open world assumption S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid Alice S sid uid species date location s2 Alice Raven 06-14-08 Lake Placid Bob S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid Carol s2 Alice Raven 06-14-08 Lake Placid Adapted key constraints ! D2: Model incomplete knowledge with exlicit negative beliefs 7

8. 2. Open world assumption I: Carol does not believe that Alice saw a crow nor a raven. S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid insert into BELIEF Carol notSightings values ( s2 , Alice , Crow , 06-14-08 , Lake Placid ) insert into BELIEF Carol notSightings values ( s2 , Alice , Raven , 06-14-08 , Lake Placid ) Alice S sid uid species date location s2 Alice Raven 06-14-08 Lake Placid Bob S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid Carol s2 Alice Raven 06-14-08 Lake Placid 8

9. 2. Open world assumption Q: Who disagrees with a sighting from 06-14-08 that Alice believes? select U.name, S1.species S sid uid species date location Users as U, BELIEF Alice Sightings as S1, BELIEF U.uid not Sightings as S2 where S1.sid = S2.sid and and 06-14-08 Lake Placid from s2 Alice Crow Alice location S1.uid = S2.uid S1.species = S2.species S1.date = 06-14-08 S2.date = 06-14-08 S1.location = S2.location S sid uid species date s2 Alice Raven 06-14-08 Lake Placid and and and Bob A: {( Bob , Crow ), ( Carol , Crow )} S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid Carol s2 Alice Raven 06-14-08 Lake Placid 9

10. 3. Higher-order beliefs S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid Alice S sid uid species date location s2 Alice Raven 06-14-08 Lake Placid C cid comment sid Bob c1 plain black feathers s2 C cid comment sid Alice Bob c1 purple-black feathers s2 D3: Beliefs about other user s beliefs: allow discussion between users 10

11. 3. Higher-order beliefs I: According to Bob, Alice believes that the S sid uid species date location 06-14-08 Lake Placid feathers of the sighted animal were plain black. insert into BELIEF Bob BELIEF Alice Comments values ( c1 , plain black feathers , s2 ) s2 Alice Crow Alice S sid uid species date location s2 Alice Raven 06-14-08 Lake Placid C cid comment sid Bob c1 plain black feathers s2 C cid comment sid Alice Bob c1 purple-black feathers s2 11

12. 3. Higher-order beliefs Q: Which comments does Alice believe according to Bob, which he does not believe himself? S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid select C1.cid, C1.comment from BELIEF Bob BELIEF Alice Comments as C1, BELIEF Bob not Comments as C2 where C1.cid = C2.cid and and Alice S sid uid species date location s2 Alice Raven 06-14-08 Lake Placid C1.comment = C2.comment C1.sid = C2.sid C cid comment sid Bob A: {( c1 , plain-black feathers )} c1 plain black feathers s2 C cid comment sid Alice Bob c1 purple-black feathers s2 12

13. 3. Higher-order beliefs Q: Which comments does Alice believe according to somebody, which (s)he does not believe themself? S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid select U.name, C1.sid, C1.comment from Users as U, BELIEF U.uid BELIEF Alice Comments as C1, BELIEF U.uid not Comments as C2 Alice S sid uid species date location s2 Alice Raven 06-14-08 Lake Placid where C1.cid = C2.cid and C1.comment = C2.comment and C cid comment sid C1.sid = C2.sid Bob c1 plain black feathers s2 A: {( Bob , c1 , plain-black feathers )} C cid comment sid Alice Bob c1 purple-black feathers s2 13

14. 4. Message board assumption S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid Alice S sid uid species date location s2 Alice Raven 06-14-08 Lake Placid C cid comment sid Bob c1 plain black feathers s2 S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid C cid comment sid Alice Bob c1 purple-black feathers s2 D4: Default assumption: models a trusting attitude & avoids repeated inserts 14

15. 4. Message board assumption Q: Which animal sightings does Alice believe according to Bob, which he does not S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid believe himself? Alice select S1.sid, S1.species from BELIEF Bob BELIEF Alice Sightings as S1, BELIEF Bob not Sightings as S2 S sid uid species date location 06-14-08 Lake Placid where S1.sid = S2.sid and S1.uid = S2.uid and and and s2 Alice Raven C cid comment sid S1.species = S2.species S1.date = S2.date S1.location = S2.location A: {( s2 , Crow )} Bob c1 plain black feathers s2 S sid uid species date location s2 Alice Crow 06-14-08 Lake Placid C cid comment sid Alice Bob c1 purple-black feathers s2 15

16. What we have seen so far 4 Design decisions for Belief Database model Distinct belief worlds Open world assumption (OWA) Higher-order beliefs Message board assumption BeliefSQL SQL + BELIEF (+ not ) 16

17. Agenda Motivating example Logic foundations Relational implementation Discussion 17

18. Logic foundations: Belief statements S sid uid species s2 Alice Crow Bob Alice insert into BELIEF Alice S values ( s2 , Alice , Crow , ) Carol Alice Alice i: Alice S+( s2 , Alice , Crow , ) Alice modal operator & belief path (w) relational tuple (t) sign (s) Bob Alice Bob Carol belief statement = w ts annotation of ground tuple Carol Alice Belief database D = { 1, , n} Bob 18

19. Logic foundations: Entailment S sid uid species s2 Alice Crow Bob Alice Carol Alice S sid uid species s2 Alice Crow Bob Alice Alice select * from BELIEF Bob BELIEF Alice S Alice Bob Alice Bob Carol One belief annotation: D = { 1} Carol 1= Alice S+( Crow , ) More than one entailed belief: Alice Bob D Bob Alice S+( Crow , ) 19

20. Logic foundations: Message board assumption Message board assumption Default logic If D w ts and u w ts consistent with D : u u then D u w ts non-monotonic reasoning ! D D \ D Implicit beliefs (assumptions) D Explicit beliefs (annotations) Entailed beliefs (extension) belief database contains more than the explicit belief annotations ! 20

21. Semi-formal problem statement INPUT OUTPUT Belief statements D ? i1: 1 i2: 2 ... in: n D w1 wdR+(x1, xl) ? q(x) : w Ri+(xi) Adapted key constraints Message board assumption : u u Belief Conjunctive Queries (BCQ) q(x) : w1R1s1(x1), , wgRgsg(xg) 21

22. Agenda Motivating example Logic foundations Relational implementation Discussion 22

23. Canonical Kripke structure Belief statements* i1: s11+ i2: Bob s11 i3: Bob s12 i4: Alice s21+ i5: Alice c11+ i6: Bob s22+ i7: Bob Alice c21+ i8: Bob c22+ Carol #1 Alice Carol {s11+,s21+,c11+} Carol #0 Bob #3 Alice Carol {s11+} {s11+,s21+,c11+,c21+} Bob Bob #2 {s11 ,s12 ,s22+,c22+} Message board assumption : i i * Running example from the paper 23

24. Relational representation Sightings_INTERNAL Sightings_V E tid sid uid species date location wid tid sid s e wid1 uid wid2 s1.1 s1 Carol Bald eagle 06-14-08 Lake Forest #0 s1.1 s1 + y #0 Alice #1 s1.2 s1 Carol Fish eagle 06-14-08 Lake Forest #1 s1.1 s1 + n #0 Bob #2 s2.1 s2 Alice Crow 06-14-08 Lake Placid #1 s2.1 s2 + y #0 Carol #0 s2.2 s2 Alice Raven 06-14-08 Lake Placid #2 s1.1 s1 y #1 Bob #2 #2 s1.2 s1 y #1 Carol #0 Comments_INTERNAL #2 s2.2 s2 + y #2 Alice #3 tid cid comment sid #3 s1.1 s1 + n #2 Carol #0 c1.1 c1 found feathers s2 #3 s2.1 s2 + n #3 Bob #2 c2.1 c2 plain black feathers s2 #3 Carol #0 c2.2 c2 purple-black feathers s2 Comments_V D S wd tid cid s e wid d wid1 wid2 #1 c1.1 c1 + y #0 0 #1 #0 #2 c2.2 c2 + y #1 1 #2 #0 #3 c1.1 c1 + n #2 1 #3 #1 #3 c2.1 c2 + y #3 2 24

25. Example Translation of a Belief CQ (BCQ) Q: Who disagrees with a sighting from 06-14-08 that Alice believes? BeliefSQL Translation into SQL select into from where and and and E1.uid as uid1, V.tid, V.sid, R.uid, R.species, R.date, R.location, V.s T2 E as E1, Sightings_V as V, Sightings_STAR as R E1.wid1=0 V.wid=E1.wid2 V.tid=R.tid E1.uid='1'; select U.name, S1.species from Users as U, BELIEF Alice Sightings as S1, BELIEF U.uid not Sightings as S2 where S1.sid = S2.sid and S1.uid = S2.uid and S1.species = S2.species and S1.date = 06-14-08 and S2.date = 06-14-08 and S1.location = S2.location select into from where and and E1.uid as uid1, V.tid, V.sid, R.uid, R.species, R.date, R.location, V.s T1 E as E1, Sightings_V as V, Sightings_STAR as R E1.wid1=0 V.wid=E1.wid2 V.tid=R.tid; select from where and and and T1.uid1, T2.species T1 as T1, T2 as T2 T1.sid=T2.sid ((T1.s=0 and T1.uid=T2.uid and T1.species=T2.species and T1.date='6-14-08' and T1.location=T2.location) or (T1.s=1 and (T1.uid<>T2.uid or T1.species<>T2.species or T1.date<>'6-14-08' or T1.location<>T2.location))) T2.s=1 T2.date='6-14-08'; q(x,y) : Alice S+(u,v,y, 06-14-08 ,z), x S (u,v,y, 06-14-08 ,z) drop drop table T2; table T1; 25

26. Agenda Motivating example Logic foundations Relational implementation Discussion 26

27. Experiments Size Relative overhead :=|R*| m #users dmax maximum depth of belief annotation = O(mdmax) n In theory: e.g. 100 users, max. depth 2 10,000 21 1,009 Experiments: Size not limitation of semantics, but of relational implementation! Time Depends on type of query (3 types in paper) Q1: ~0.1 s Q2: ~0.4 s Q3: ~4.5 s Experiments on 10,000 annotations ( =22.4): Considerable speed-up to come! 27

28. Inspirations and related work (excerpt) Annotations Buneman et al. [ICDT 2001 / ICDT 2007] Bhagwat et al. [VLDBJ 2005], Geerts et al. [ICDE 2006] Srivastava & Velegrakis [SIGMOD 2007] Modal logic Fagin et al. [1995] Calvanese et al. [IS 2008] Nguyen [LJ-IGPL 2008] Uncertain / incomplete information Sarma et al. [ICDE 2006] Green & Tannen [IEEE Data Eng. 2006] Dalvi & Suciu [PODS 2007] Inconsistency / key violations Arenas et al. [PODS 1999] Fuxman et al. [SIGMOD 2005] Peer-to-peer computing / collaborative data sharing Bernstein et al. [WebDB 2002] Ives et al. [SIGMOD record 2008] 28

29. Conclusions Proposed BELIEF databases Goal: manage, curate inconsistent data Implementation Logical foundations Relational translation Current work making it compact and fast 29

30. BACKUP 30

31. Relative overhead of relational representation 1E+4 Relative overhed (|R|/n) 1E+3 1E+2 1E+1 1E+1 1E+2 1E+3 1E+4 Distribution of belief path depths (Pr[k=x]) Number of annotations (n) 31

32. Query types and execution times 32

33. Belief Conjunctive Queries (BCQ) 33

34. Revisiting the semantics / the user (3) ? BELIEF Alice ( , eagle , ) -> Structured discourse -> Alice ASSERTS ( , eagle , ) (2) BeliefSQL BELIEF Bob BELIEF Alice ( , black feathers , ) Conflicts in belief worlds: OWA, keys, ML, DA -> Bob SUGGESTS that the ASSUMPTION ( , black feathers , ) has led Alice to her original observation (1) SQL Standard relational model 34