artificial intelligence for social good
TRANSCRIPT
artificial intelligence for social good
Oana Tifrea-MarciuskaNovember 28, 2017
Department of Computer Science, University of OxfordAlan Turing Institute, London
motivation
my research universe
1. Automatic Humor Recognition Bachelor thesis [2008]
2. An Adaptive Learning System Master thesis [2010-2012]
3. Personalized Search for the Social Semantic Web PhD[2012-2016]
4. A Fact Checking Tool Internship Microsoft Research [2016]
5. Semantic Parsing Postdoc Alan Turing Institute [2017]
My question How do people reason and communicate?
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1. automatic humor recognition
1. automatic humor recognition
Problem definition Given a document , is it or ?
Approach Apply supervised document classification
Algorithms Naive Bayes and SVMs
My main task 10 new features detected
Examples Ambiguity, Antonyms, Punctuation, Repetitive words
Results Improved from 60% to 66% accuracy the state of the art
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2. an adaptive learning system
2. an adaptive learning system (als) for poor comprehenders
Problem definition More than 10% of children are diagnosedwith text comprehension problems
Approach Terence EU project develops an ALS
Recommends stories to read and games to play
My main tasks
Build ontologies for Terence (e.g., story, games, common, bridge)
Reasoning automatically about temporal events
Examples of games Who ... ? What ...? When ...?
Results Improved reading comprehension for kids (Italy and UK)
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3. personalized search for the web 3.0
3. personalized search for the web 3.0
Problem definition How to combine
Social data with information about users, groups
Semantic data with precise and rich results
Area Preference modeling, representation, and reasoning
ApproachBridge a gap between Semantic Web languages and preferencerepresentation languages
Syntax and semantics of three preference frameworks
Describe top-k query answering
Understand their advantages and disadvantages
Formal properties, experiments, quality of the algorithms
Generalize preference frameworks to a group of users and thepresence of uncertainty and solve conflicts between data
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classes of languages - overview
1. Qualitative preferences
� I prefer a hotel with free parking over a hotel with paid parking.
2. Quantitative preferences
� My preferences for a hotel with free garage parking is 1.0, for ahotel with free outside parking is 0.5 and for a hotel with paidparking is 0.2.
3. Conditional preferences
� If it is winter, then I prefer a free garage parking over a freeoutside parking.
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gpp−datalog± overview
� Datalog± D + Rules offer(U,P) → ∃T,L,C prop(P,T,L,C)
Combines Datalog± with two models:1. Partial qualitative preferences ( ≻P ) for a group of users
place(London) ≻ place(Oxford)
2. Probabilistic uncertainty (e.g., reviews) ( ≻M )
place(Oxford) : 0.9 place(London) : 0.8
place(p2)
place(p3)
place(p1)
place(p4)
place(p5)
place(p6)
feature(g)
feature(p)
U1
place(p1) place(p3)
place(p2)place(p4)
place(p5) place(p6)
feature(p)
feature(g)
U2
place(p1)
place(p4)
place(p2)
place(p3)
place(p5)
place(p6)
feature(g)
feature(p)
U3
feature(p) 0.95
place(p6) 0.8
place(p5) 0.75
place(p3) 0.6
feature(g) 0.1
place(p1) 0.2
place(p4) 0.35
place(p2) 0.4
�M
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−±
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
� ( ComPrefsGen , ComPrefsPT , ComPrefsRank , ComPrefsSort )
�⊎
is the preference aggregation operator
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merging operator - a general one
� 0.35 − 0.2 > 0.4 No =⇒ keep relationplace(p1)
place(p4)
place(p2)
place(p3)
place(p5)
place(p6)
feature(g)
feature(p)
U3
feature(p) 0.95
place(p6) 0.8
place(p5) 0.75
place(p3) 0.6
feature(g) 0.1
place(p1) 0.2
place(p4) 0.35
place(p2) 0.4
�M
place(p3)
place(p5)
place(p1)
place(p4)
place(p2)
place(p6)
feature(p)
feature(g)
U ′3
� 0.75 − 0.2 > 0.4 Yes =⇒ inverse relationplace(p1)
place(p4)
place(p2)
place(p3)
place(p5)
place(p6)
feature(g)
feature(p)
U3
feature(p) 0.95
place(p6) 0.8
place(p5) 0.75
place(p3) 0.6
feature(g) 0.1
place(p1) 0.2
place(p4) 0.35
place(p2) 0.4
�M
place(p3)
place(p5)
place(p1)
place(p4)
place(p2)
place(p6)
feature(p)
feature(g)
U ′3
Back12
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.
place(p3)
place(p5)
place(p1)
place(p4)
place(p2)
place(p6)
feature(p)
feature(g)
U ′3
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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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gpp−datalog±: experiments
� Yelp dataset: 1,000 businesses, 229,907 reviews to find places toeat
� Categories in Yelp → Datalog± concepts (e.g., Italian)
� 50 users inserted their preferences (e.g., the place to eat, food)
� Compare methods
� efficiency
� quality of the results15
gpp−datalog±: some results of the experiments
� group size increase → quality decreases
� k increase → quality increases
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overall
Formalism Bipolar Pref Properties and Implem Uncertainty QueriesPP−Datalog± Yes Yes Yes DAQ
GP−Datalog± Yes No No DAQGPP−Datalog± Yes Yes Yes DAQ
OCP-nets No No No CQOCP−theories No No No CQSP−Datalog± Yes Yes No UNCQGSP−Datalog± Yes Yes No UNCQ
Figure: Summary of the ontology languages
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4. a fact checking tool
4. a fact checking tool
Problem definition Websites have incorrect facts
Approach Identify disinformation over the Web
My main tasks
Build a tool that detects the incorrect facts
Results First version of the tool was build and used internally
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5. semantic parsing
5. semantic parsing
Problem definition with examplesWhat are all the rivers in Colorado?
→ (A, (river(A), loc(A,B), const(B, stateid(colorado))))
John is an employee of Facebook.’
→ employee_of(John,Facebook)
Approach Treat it as a machine translation problem
My main tasks
Used Nematus tool for our problem
Results To be published soon
End goal Intelligent question answering
i.e., human-like natural language understanding and reasoning
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conclusion
my research universe
1. Automatic Humor Recognition Bachelor thesis [2008]
2. An Adaptive Learning System Master thesis [2010-2012]
3. Personalized Search for the Social Semantic Web PhD [2012-2016]
4. A Fact Checking Tool Internship Microsoft Research [2016]
5. Semantic Parsing Postdoc Alan Turing Institute [2017]
Future work More work on how people reason and communicate.
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special thanks to my collaborators and funding agencies
- Thomas Lukasiewicz- Rosella Gennari- Corina Forascu- Yordan Zaykov- Stefan Borgwardt- İsmail İlkan Ceylan- Tommaso Di Noia- Christian Drescher- Bettina Fazzinga- Maria Vanina Martinez- Rafael Peñaloza- Gerardo I. Simari- Akanksha Shrivastava- Pierpaolo Vittorini- Toby Walsh- Zhenghua Xu
- UAIC Iasi Scholarship- Google Anita Scholarship- FUB Bolzano Scholarship- Terence EU project- Google European Fellowship- EPSRC- Alan Turing Institute
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some of my publications
1 PP-Datalog± Preference–Based Query Answering in ProbabilisticDatalog± Ontologies In Journal on Data Semantics. Vol. 4. No.2. Pages 81–101. 20151.
2 Query Answering in Probabilistic Datalog± Ontologies underGroup Preferences. In Proc. of WI 2013. Pages 171–178.1.
3 Ontology–Based Query Answering with Group Preferences InACM Transactions on Internet Technology (TOIT). Vol. 14. No.4. Pages 25:1–25:24. 2014.1
4 Group Preferences for Query Answering in Datalog± OntologiesIn Proc. of SUM 2013‚ Vol. 8078 of LNCS. Pages 360–373. 2013.1
5 Combining Existential Rules with the Power of CP–Theories InProc. of IJCAI 2015.2
� 1Authors: T. Lukasiewicz, M.V. Martinez, G. I. Simari and O. Tifrea–Marciuska
� 2 T. Di Noia, T. Lukasiewicz, M.V. Martinez, G. I. Simari and O. Tifrea–Marciusk
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some of my publications
6 Query Answering in Ontologies Under Preference Rankings Proc.of IJCAI 2017.1
7 Ontological Query Answering under Many−Valued GroupPreferences in Datalog±. In International Journal ofApproximate Reasoning, 2017. 2
8 Preferential Query Answering over the Semantic Web withPossibilistic Networks In Proc. of IJCAI 2016.3
� 1Authors: İsmail İlkan Ceylan‚ Thomas Lukasiewicz‚ Rafael Peñaloza and Oana
Tifrea−Marciuska
� 2Bettina Fazzinga‚ Thomas Lukasiewicz‚ Maria Vanina Martinez‚ Gerardo I.
Simari and Oana Tifrea−Marciuska
� 3Stefan Borgwardt‚ Bettina Fazzinga‚ Thomas Lukasiewicz‚ Akanksha Shrivastava
and Oana Tifrea−Marciuska
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