Presentation: Personalised Search for the Social Semantic Web

1. PERSONALIZED SEARCH FOR THE SOCIAL
SEMANTIC WEB
VLDB PHD WORKSHOP
Oana Tifrea-Marciuska
supervisor: Prof. Thomas Lukasiewicz
Department of Computer Science, University of Oxford, UK
August 31, 2015
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2. MOTIVATION
PRELIMINARIES
Datalog+/–
LANGUAGES
Strategies to Answer k-rank Disjunctive Atomic Queries
Experiments
FUTURE WORK
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3. WEB 3.0
Social Data Semantic data
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4. WEB 3.0 SEARCH
Social Data
Personalized access
Semantic data
Precise and rich results
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5. PERSONALIZED INFORMATION ACCESS
QUERY
ORDER BY user’s preferences
LIMIT k
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6. PERSONALIZED INFORMATION ACCESS
QUERY Datalog+/– Queries
ORDER BY user’s preferences Preference Model
LIMIT k Top k
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7. DATALOG+/–
hotel
id city conn class
t1 h1 rome c e
t2 h2 rome w l
t3 h3 rome c e
review
id user feedback
t7 h1 b n
t8 h2 b p
t9 h3 j p
reviewer
user age
t10 b 20
t11 j 30
friend
user user
t12 b a
t13 j a
FIGURE : Database D.
Constraints
Datalog 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
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8. WHY DATALOG+/–
Generalizes DL-Lite family
Has implementations
Can use ideas from the preference modeling in database
community
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9. PERSONALIZED INFORMATION ACCESS
QUERY Datalog+/– Queries
ORDER BY user’s preferences Preference Model
LIMIT k Top k
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10. PREFERENCE MODELS
Syntax and Semantics
Advantages and Disadvantages: formal properties, experiments
Complexity for answering queries: conjunctive queries (CQ) or
disjunction of atomic queries(DAQ)
Solve conflict between data
Preferences of a group of users
Uncertainty (reviews) vs preferences
Desire: ontology language that handles preferences of user or a
group of users and can handle uncertainty (e.g., information
integration)
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11. 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) 0.9
hotel(h1, rome, c, e) 0.8
hotel(h2, rome, w, l) 0.3
Semantic properties
Complexity of answering top-k DAQ polynomial
Dataset: IMDB movie database, with synthetic preferences
∗In Journal on Data Semantics. Vol. 4. No. 2. Jun, 2015.
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12. CH. 1. GPP-DATALOG+/–∗
Combines for a group of people
Qualitative 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 properties
Complexity of answering top-k DAQ polynomial
Dataset: YELP, with real preferences from 50 users
efficiency
quality of the results
∗In ACM Transactions on Internet Technology. Vol. 14. No. 4. Dec, 2014
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13. GROUP PREFERENCE MODEL
DEFINITION
A group preference model U = (U1, . . . , Un) for n 1 users is a collection
of n user preference models.
dest(f1
) dest(c3
) dest(c2
)
dest(c1
) dest(b1
) dest(f2
)
u1
dest(c1
)
dest(c3
)
u2
dest(f1
)
dest(b1
)dest(f2
)
dest(c2
)
dest(f1
)
u3
dest(b1
)
dest(c1
)
dest(f2
)dest(c3
)
dest(c2
)
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14. 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. Markov
logic and Bayesian networks).
PrM
0.4
0.34
0.3
0.8
0.75
0.6
dest(b1
)
dest(c1
)
dest(f1
)
dest(c2
)
dest(f2
)
dest(c3
)
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15. CHALLENGES OF THE GIVEN MODEL 1/2
u1
dest(f1
) dest(c3
) dest(c2
)
dest(c1
) dest(b1
) dest(f2
)
u2
dest(f1
)
dest(c1
) dest(b1
)dest(f2
)
dest(c3
) dest(c2
)
dest(f1
)
u3
dest(b1
)
dest(c1
)
dest(f2
)dest(c3
)
dest(c2
)
0.8
0.75
0.6
dest(b1
)
dest(c1
)
dest(f1
)
PrM
0.4
0.34
0.3
dest(c2
)
dest(f2
)
dest(c3
)
Challenge 1: user preference model and the probabilistic model
in disagreement: preference merging operators
DEFINITION
Let U be an SPO and M be a score-based preference relation. A
preference merging operator ⊗( U, M ) yields a relation ∗
such that
1 ∗
is an SPO
2 if a1 U a2 and a1 M a2, then a1
∗
a2.
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16. CHALLENGES OF THE GIVEN MODEL 2/2
u1
dest(f1
) dest(c3
) dest(c2
)
dest(c1
) dest(b1
) dest(f2
)
u2
dest(f1
)
dest(c1
) dest(b1
)dest(f2
)
dest(c3
) dest(c2
)
dest(f1
)
u3
dest(b1
)
dest(c1
)
dest(f2
)dest(c3
)
dest(c2
)
0.8
0.75
0.6
dest(b1
)
dest(c1
)
dest(f1
)
PrM
0.4
0.34
0.3
dest(c2
)
dest(f2
)
dest(c3
)
Challenge 2: user preference models may be in disagreement
with each other: preference aggregation operator
DEFINITION
Let U = (U1, . . . , Un) be a group preference model, where every Ui is an
SPO. A preference aggregation operator on U yields an SPO ∗
.
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17. GPP-Datalog+/– ontology - our model
DEFINITION
A GPP-Datalog+/– ontology has the form KB = (O, U, M, ⊗, )
O is a Datalog+/– ontology
U = (U1, . . . , Un) is a group preference model with n 1
M is a probabilistic model
⊗ is a preference merging operator
is the preference aggregation operator
We say that KB is a guarded iff O is guarded.
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18. MERGING OPERATOR
0.4 − 0.6 > 0.1 No =⇒ keep relation
dest(f1
)
dest(c1
) dest(b1
)dest(f2
)
dest(c3
) dest(c2
)
0.8
0.75
0.6
dest(b1
)
dest(c1
)
dest(f1
)
PrM
0.4
0.34
0.3
dest(c2
)
dest(f2
)
dest(c3
)
dest(c1
) dest(b1
)
dest(f1
)
dest(c3
) dest(c2
) dest(f2
)
0.75 − 0.6 > 0.1 Yes =⇒ inverse relation
dest(f1
)
dest(c1
) dest(b1
)dest(f2
)
dest(c3
) dest(c2
)
0.8
0.75
0.6
dest(b1
)
dest(c1
)
dest(f1
)
PrM
0.4
0.34
0.3
dest(c2
)
dest(f2
)
dest(c3
)
dest(c1
) dest(b1
)
dest(f1
)
dest(c3
) dest(c2
) dest(f2
)
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19. 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 are
ground substitutions for the variables in Q(X).
A k-rank answer to Q is a sequence S = θ1, . . . θk built by
subsequently appending the skyline answers to Q, removing these
atoms from consideration, and repeating until either S = k or no more
skyline answers to Q remain.
dest(f1
)
dest(b1
)
dest(c1
)
dest(f2
)dest(c3
)
dest(c2
)
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20. STRATEGIES TO ANSWER K-RANK DAQ
Collapse to single user
1 Create virtual user
2 Calculate k-rank from it
Voting
1 Calculate k-rank for each of the users
2 Vote
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21. 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
1
1
1
1
1
1
3
2
2
11 1
2
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22. 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
1
1
1
1
1
1
3
2
2
11 1
2
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23. COLLAPSE TO SINGLE USER: K-RANK
Q = dest(X), (t1, t2, t3) = (0, 0.1, 0.19), k = 1
k-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
1
2
1
2
11
1
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24. COLLAPSE TO SINGLE USER: K-RANK
Q = dest(X), (t1, t2, t3) = (0, 0.1, 0.19), k = 2
k-rank answer to Q
dest(b1) , dest(c1) .
dest(c1
)
dest(f2
)
dest(f1
)
dest(c2
)
1
dest(c3
) 1
1
2
1
2 11
1
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25. COLLAPSE TO SINGLE USER: K-RANK
Q = dest(X), (t1, t2, t3) = (0, 0.1, 0.19), k = 3
k-rank answer to Q
dest(b1) , dest(c1) , dest(f1) .
dest(f2
)
dest(f1
)
dest(c2
)
1
dest(c3
) 1
11
1
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26. COLLAPSE TO SINGLE USER: K-RANK
Q = dest(X), (t1, t2, t3) = (0, 0.1, 0.19), k = 4
k-rank answer to Q
dest(b1) , dest(c1) , dest(f1) , dest(f2) .
dest(f2
)dest(c2
)
dest(c3
) 1
1
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27. COLLAPSE TO SINGLE USER: K-RANK
Q = dest(X), (t1, t2, t3) = (0, 0.1, 0.19), k = 5
k-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
)
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28. 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 0
u2 0 1 0 1 0
u3 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) .
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29. EXPERIMENTS
Yelp dataset: 1,000 businesses, 229,907 reviews to find places
to eat
categories in Yelp → Datalog+/–concepts (e.g Italian,
Mediterranean)
50 users inserted their preferences (e.g., the place to eat , food)
Compare methods
efficiency
quality of the results
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30. RESULTS OF THE EXPERIMENTS
group size increase → quality deacreases
k increase → quality increases
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31. 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 long
as 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.
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32. ONTOLOGICAL CP-THEORIES
: review(I, U, p) review(I , U, n)[∅]
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33. ONTOLOGICAL CP-THEORIES
: hotel(I, C, w, S) hotel(I , C, c, S )[{R, F}]
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34. ONTOLOGICAL CP-THEORIES
hotel(I, C, c, S) review(I, U, n): reviewer(j, A) reviewer(beate, A )[∅]
:
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35. 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 Implementation
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36. PUBLICATIONS
1 PP-Datalog+/– Preference–Based Query Answering in
Probabilistic Datalog+/– Ontologies In Journal on Data
Semantics. Vol. 4. No. 2. Pages 81–101. June, 2015.∗
2 Query Answering in Probabilistic Datalog+/– Ontologies under
Group Preferences. In Proc. of WI 2013. Pages 171–178.∗
3 Query Answering in Datalog+/– Ontologies under Group
Preferences and Probabilistic Uncertainty In Proc. DMSSW 2013
Vol. 8295 of LNCS. Pages 192–206.∗
4 Ontology–Based Query Answering with Group Preferences In
ACM 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. Pages
360–373. 2013.∗
Journal, Conference, Workshop or symposium
∗Authors: T. Lukasiewicz, M.V. Martinez, G. I. Simari and O. Tifrea–Marciuska
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37. 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. Pages
74–87.∗
8 Combining Existential Rules with the Power of CP–Theories In
Proc. of IJCAI 2015.∗
Conference, Workshop or symposium
∗Authors: T. Di Noia, T. Lukasiewicz, M.V. Martinez, G. I. Simari and O.
Tifrea–Marciuska
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38. THANK YOU
Questions ?oana.tifrea@cs.ox.ac.uk
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