vldb phd workshop

PERSONALIZED SEARCH FOR THE SOCIAL

SEMANTIC WEBVLDB PHD WORKSHOP

Oana Tifrea-Marciuskasupervisor: Prof. Thomas Lukasiewicz

Department of Computer Science, University of Oxford, UK

August 31, 2015

OANA TIFREA-MARCIUSKA PERSONALIZED SEARCH FOR THE SOCIAL SEMANTIC WEB SLIDE 1 /32

MOTIVATION

PRELIMINARIESDatalog+/–

LANGUAGESStrategies to Answer k-rank Disjunctive Atomic QueriesExperiments

FUTURE WORK


WEB 3.0

Social Data Semantic data


WEB 3.0 SEARCH

Social Data

Personalized access

Semantic data

Precise and rich results


PERSONALIZED INFORMATION ACCESS

QUERY

ORDER BY user’s preferences

LIMIT k



QUERY Datalog+/– Queries

ORDER BY user’s preferences Preference Model

LIMIT k Top k


DATALOG+/–

hotelid city conn class

t1 h1 rome c et2 h2 rome w lt3 h3 rome c e

reviewid user feedback

t7 h1 b nt8 h2 b pt9 h3 j p

revieweruser age

t10 b 20t11 j 30

frienduser user

t12 b at13 j a

FIGURE : Database D.

ConstraintsDatalog like ∀X∀YΦ(X,Y)→ Ψ(X)

friend(A,B)→ friend(B,A)With existential in the head ∀X∀YΦ(X,Y)→ ∃ZΨ(X,Z)

reviewer(U,A)→ ∃Ffriend(U,F )

Negative constraints ∀XΦ(X)→ ⊥friend(D,D)→ ⊥

Equality constraints ∀XΦ(X)→ Ai = Aj

review(A,U,F1) ∧ review(A,U,F2)→ F1 = F2


WHY DATALOG+/–

Generalizes DL-Lite familyHas implementationsCan use ideas from the preference modeling in databasecommunity



QUERY Datalog+/– Queries

ORDER BY user’s preferences Preference Model

LIMIT k Top k


PREFERENCE MODELS

Syntax and SemanticsAdvantages and Disadvantages: formal properties, experimentsComplexity for answering queries: conjunctive queries (CQ) ordisjunction of atomic queries(DAQ)Solve conflict between data

Preferences of a group of usersUncertainty (reviews) vs preferences

Desire: ontology language that handles preferences of user or agroup of users and can handle uncertainty (e.g., informationintegration)


CH 1. PP-DATALOG+/–∗

Combines (4 different operators)Qualitative preferences - binary relations � ⊆ HPref ×HPref .

hotel(h1, rome, c, e) � hotel(h2, rome,w , l)Probabilistic model (e.g., reviews)

hotel(h3, rome, c, e) 0.9hotel(h1, rome, c, e) 0.8hotel(h2, rome,w , l) 0.3

Semantic propertiesComplexity of answering top-k DAQ polynomialDataset: IMDB movie database, with synthetic preferences

∗In Journal on Data Semantics. Vol. 4. No. 2. Jun, 2015.OANA TIFREA-MARCIUSKA PERSONALIZED SEARCH FOR THE SOCIAL SEMANTIC WEB SLIDE 9 /32

CH. 1. GPP-DATALOG+/–∗

Combines for a group of peopleQualitative preferences - binary relations � ⊆ HPref ×HPref .

food(f1) � food(f2)Probabilistic model (e.g., reviews)

food(m2) 0.9; food(m3) 0.8; food(m2) 0.3

Semantic propertiesComplexity of answering top-k DAQ polynomialDataset: YELP, with real preferences from 50 users

efficiencyquality of the results

∗In ACM Transactions on Internet Technology. Vol. 14. No. 4. Dec, 2014OANA TIFREA-MARCIUSKA PERSONALIZED SEARCH FOR THE SOCIAL SEMANTIC WEB SLIDE 10 /32

GROUP PREFERENCE MODEL

DEFINITIONA group preference model U =(U1, . . . ,Un) for n>1 users is a collectionof n user preference models.

dest(f1) dest(c3) dest(c2)

dest(c1) dest(b1) dest(f2)

u1

dest(c1)

dest(c3)

u2dest(f1)

dest(b1)dest(f2)

dest(c2) dest(f1)

u3dest(b1)

dest(c1)

dest(f2)dest(c3)

dest(c2)


PROBABILISTIC MODEL

A preference relation � is score-based if is defined as follows:a1 � a2 iff score(a1) > score(a2).Model assigns a probability to each atom (using e.g. Markovlogic and Bayesian networks).

PrM0.4

0.34

0.3

0.8

0.75

0.6

dest(b1)

dest(c1)

dest(f1)

dest(c2)

dest(f2)

dest(c3)


CHALLENGES OF THE GIVEN MODEL 1/2

u1



u2dest(f1)

dest(c1) dest(b1)dest(f2)

dest(c3) dest(c2) dest(f1)

u3dest(b1)

dest(c1)

dest(f2)dest(c3)

dest(c2)

0.8

0.75

0.6

dest(b1)

dest(c1)

dest(f1)

PrM0.4

0.34

0.3

dest(c2)

dest(f2)

dest(c3)

Challenge 1: user preference model and the probabilistic modelin disagreement: preference merging operators

DEFINITIONLet �U be an SPO and �M be a score-based preference relation. Apreference merging operator ⊗(�U ,�M) yields a relation �∗ such that

1 �∗ is an SPO2 if a1 �U a2 and a1 �M a2, then a1 �∗ a2.


CHALLENGES OF THE GIVEN MODEL 2/2

u1



u2dest(f1)



u3dest(b1)

dest(c1)

dest(f2)dest(c3)

dest(c2)

0.8

0.75

0.6

dest(b1)

dest(c1)

dest(f1)

PrM0.4

0.34

0.3

dest(c2)

dest(f2)

dest(c3)

Challenge 2: user preference models may be in disagreementwith each other: preference aggregation operator

DEFINITIONLet U =(U1, . . . ,Un) be a group preference model, where every Ui is anSPO. A preference aggregation operator

⊎on U yields an SPO �∗.


GPP-Datalog+/– ontology - our model

DEFINITIONA GPP-Datalog+/– ontology has the form KB =(O,U ,M,⊗,

⊎)

O is a Datalog+/– ontologyU =(U1, . . . ,Un) is a group preference model with n>1M is a probabilistic model⊗ is a preference merging operator⊎

is the preference aggregation operatorWe say that KB is a guarded iff O is guarded.


MERGING OPERATOR

0.4− 0.6 > 0.1 No =⇒ keep relation

dest(f1)


dest(c3) dest(c2)

0.8

0.75

0.6

dest(b1)

dest(c1)

dest(f1)

PrM0.4

0.34

0.3

dest(c2)

dest(f2)

dest(c3)

dest(c1) dest(b1)

dest(f1)


0.75− 0.6 > 0.1 Yes =⇒ inverse relation

dest(f1)


dest(c3) dest(c2)

0.8

0.75

0.6

dest(b1)

dest(c1)

dest(f1)

PrM0.4

0.34

0.3

dest(c2)

dest(f2)

dest(c3)

dest(c1) dest(b1)

dest(f1)



SKYLINE AND K-RANK ANSWER

Let KB be a GPP-Datalog+/– ontology, Q(X)=q1(X1) ∨ · · · ∨ qn(Xn)be a DAQ. Then, a skyline answer to Q relative to�∗=

⊎(⊗(�U1 ,�M), . . . ,⊗(�Un ,�M)) is any θqi entailed by O such

that no θ′ exists with O |= θ′qj and θ′qj �∗ θqi , where θ and θ′ areground substitutions for the variables in Q(X).A k -rank answer to Q is a sequence S = 〈θ1, . . . θk ′〉 built bysubsequently appending the skyline answers to Q, removing theseatoms from consideration, and repeating until either S = k or no moreskyline answers to Q remain.

dest(f1)

dest(b1)

dest(c1)

dest(f2)dest(c3)

dest(c2)


STRATEGIES TO ANSWER K-RANK DAQ

Collapse to single user1 Create virtual user2 Calculate k-rank from it

Voting1 Calculate k-rank for each of the users2 Vote


COLLAPSE TO SINGLE USER

no relation

dest(b1)

u1 t = 0

dest(f2)

dest(c3)

dest(c1) dest(c2)

dest(f1) u2 t = 0.1

dest(c1)

dest(f1)

dest(c2) dest(f2)

dest(b1)

dest(c3)

u3 t = 0.19

dest(b1)

dest(c1)

dest(f1)

dest(c3)

dest(c2)

dest(f2) dest(b1)

dest(c1)

dest(f2)

dest(c3)

dest(c2)

dest(f1)

1

11

1

1

1

1

3

22

11 1

2


COLLAPSE TO SINGLE USER

relation with weight 2

dest(b1)

u1 t = 0

dest(f2)

dest(c3)

dest(c1) dest(c2)

dest(f1) u2 t = 0.1

dest(c1)

dest(f1)

dest(c2) dest(f2)

dest(b1)

dest(c3)

u3 t = 0.19

dest(b1)

dest(c1)

dest(f1)

dest(c3)

dest(c2)

dest(f2) dest(b1)

dest(c1)

dest(f2)

dest(c3)

dest(c2)

dest(f1)

1

11

1

1

1

1

3

22

11 1

2


COLLAPSE TO SINGLE USER: K-RANK

Q =dest(X ), (t1, t2, t3) = (0,0.1,0.19), k = 1k -rank answer to Q〈 dest(b1) 〉 .

dest(c1)

dest(f2)

dest(f1)

dest(c2)

dest(b1) 3

1

1

dest(c3) 1

1 1

2

12

12

11

1



Q =dest(X ), (t1, t2, t3) = (0,0.1,0.19), k = 2k -rank answer to Q〈 dest(b1) , dest(c1) 〉 .

dest(c1)

dest(f2)

dest(f1)

dest(c2)

1

dest(c3) 1

1

2

12 11

1



Q =dest(X ), (t1, t2, t3) = (0,0.1,0.19), k = 3k -rank answer to Q〈 dest(b1) , dest(c1) , dest(f1) 〉 .

dest(f2)

dest(f1)

dest(c2)

1

dest(c3) 1

11

1



Q =dest(X ), (t1, t2, t3) = (0,0.1,0.19), k = 4k -rank answer to Q〈 dest(b1) , dest(c1) , dest(f1) , dest(f2) 〉 .

dest(f2)dest(c2)

dest(c3) 11



Q =dest(X ), (t1, t2, t3) = (0,0.1,0.19), k = 5k -rank answer to Q〈 dest(b1) , dest(c1) , dest(f1) , dest(f2) , dest(c2) 〉 or〈 dest(b1) , dest(c1) , dest(f1) , dest(f2) , dest(c3) 〉 .

dest(c2)

dest(c3)


VOTING - PLURALITY VOTING

Q =dest(X ), k =2, and (t1, t2, t3) = (0,0.1,0.19).

dest(f2) dest(c1) dest(c2) dest(b1) dest(c3)u1 1 1 1 1 0u2 0 1 0 1 0u3 1 0 0 1 1

Total 2 2 1 3 1

dest(b1)

u1 t = 0

dest(f2)

dest(c3)

dest(c1) dest(c2)

dest(f1)

u3 t = 0.19

dest(b1)

dest(c1)

dest(f1)

dest(c3)

dest(c2)

dest(f2)

u2 t = 0.1

dest(c1)

dest(f1)

dest(c2) dest(f2)

dest(b1)

dest(c3)

k -rank answer to Q using plurality voting is 〈dest(b1), dest(c1) 〉 or〈dest(b1), dest(f2) 〉.


EXPERIMENTS

Yelp dataset: 1,000 businesses, 229,907 reviews to find placesto eatcategories in Yelp→ Datalog+/–concepts (e.g Italian,Mediterranean)50 users inserted their preferences (e.g., the place to eat , food)

Compare methodsefficiencyquality of the results


RESULTS OF THE EXPERIMENTS

group size increase→ quality deacreasesk increase→ quality increases


CH 2. ONTOLOGICAL CP-THEORIES∗

A Datalog+/– ontology O and a set of υ : ξ � ξ′ [W ]

Given υ we prefer ξ to ξ′, irrespective of the value of W , as longas the other values are the same.

reviewer(bob,A) : hotel(I,C,wifi ,S) � hotel(I,C, cable,S)[REVIEW ]with REVIEW = {review(p), review(n)}

Complexity of answering top-k CQ

∗In Proc. of the International Joint Conference on Artificial Intelligence, Jul 2015.OANA TIFREA-MARCIUSKA PERSONALIZED SEARCH FOR THE SOCIAL SEMANTIC WEB SLIDE 25 /32

ONTOLOGICAL CP-THEORIES

> : review(I,U,p) � review(I′,U,n)[∅]



> : hotel(I,C,w,S) � hotel(I′,C, c,S′)[{R,F}]



hotel(I,C, c,S) review(I,U,n) : reviewer(j ,A)� reviewer(beate,A′)[∅]

:


SUMMARY

Chapter Formalism ∗

1 PP-Datalog+/– [1]1 GP-Datalog+/– [5]1 GPP-Datalog+/–[2,3,4]2 Ontological CP-nets [6,7]2 Ontological CP-theories [8]

PP-Datalog+/-

GPP-Datalog+/-Ontological CP-theories

Ontological CP-nets

1 2

GP-Datalog+/-

∗Complexity, Uncertainty, Properties and ImplementationOANA TIFREA-MARCIUSKA PERSONALIZED SEARCH FOR THE SOCIAL SEMANTIC WEB SLIDE 29 /32

PUBLICATIONS

1 PP-Datalog+/– Preference–Based Query Answering inProbabilistic Datalog+/– Ontologies In Journal on DataSemantics. Vol. 4. No. 2. Pages 81–101. June, 2015.∗

2 Query Answering in Probabilistic Datalog+/– Ontologies underGroup Preferences. In Proc. of WI 2013. Pages 171–178.∗

3 Query Answering in Datalog+/– Ontologies under GroupPreferences and Probabilistic Uncertainty In Proc. DMSSW 2013Vol. 8295 of LNCS. Pages 192–206.∗

4 Ontology–Based Query Answering with Group Preferences InACM Transactions on Internet Technology (TOIT). Vol. 14. No. 4.Pages 25:1–25:24. December, 2014.∗

5 Group Preferences for Query Answering in Datalog+/–Ontologies In Proc. of SUM 2013 Vol. 8078 of LNCS. Pages360–373. 2013.∗

Journal, Conference, Workshop or symposium

∗Authors: T. Lukasiewicz, M.V. Martinez, G. I. Simari and O. Tifrea–MarciuskaOANA TIFREA-MARCIUSKA PERSONALIZED SEARCH FOR THE SOCIAL SEMANTIC WEB SLIDE 30 /32

PUBLICATIONS

6 Computing k-Rank Answers with Ontological CP–Nets In Proc.SEBD 2014. Pages 276-283.∗

7 Computing k-Rank Answers with Ontological CP–Nets In Proc.PRUV 2014 Vol. 1205 of CEUR Workshop Proceedings. Pages74–87.∗

8 Combining Existential Rules with the Power of CP–Theories InProc. of IJCAI 2015.∗

Conference, Workshop or symposium

∗Authors: T. Di Noia, T. Lukasiewicz, M.V. Martinez, G. I. Simari and O.Tifrea–Marciuska


THANK YOU

Questions? [email protected]


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