Conferences Review – AAAI and IJCAI Sean. 2 Outline Introduction to AAAI Selected papers from AAAI (3) Introduction to IJCAI Selected papers from IJCAI

Conferences Review– AAAI and IJCAI

Sean

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Outline

• Introduction to AAAI

• Selected papers from AAAI (3)

• Introduction to IJCAI

• Selected papers from IJCAI (3)

• Summary

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Introduction to AAAI• Association for the Advancement of Artificial Intelligence conference on Artif

icial Intelligence (AAAI)– Annual conference in summer (from 1980)– Totally 24 sessions by now– Acceptance rate: 25%~30%– No AAAI 2009

• Related tracks– AI and the Web Track (Special track)– Natural Language Processing– Knowledge-Based Information Systems– Machine Learning

• Major groups are from engineering school (algorithms and IS)– Qiang Yang et al., HKUST, Hong Kong– Changshui Zhang et al., Tsinghua University, China– Zhejiang University, China– Zhi-Hua Zhou et al. Nanjing University, China

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Selected Papers from AAAI• AAAI-10 outstanding paper awards

– How Incomplete Is Your Semantic Web Reasoner? Systematic Analysis of the Completeness of Query Answering Systems

• AI and the Web Track (Special track, AAAI-10)• Giorgos Stoilos, Bernardo Cuenca Grau, Ian Horrocks (Oxford University,

UK)

• Other selected papers– Modeling Dynamic Multi-Topic Discussions in Online Forums

• AI and the Web Track (Special track, AAAI-10)• Hao Wu, Jiajun Bu, Chun Chen, Can Wang, Guang Qiu, Lijun Zhang and

Jianfeng Shen (Zhengjiang University, China)

– Learning to Predict Opinion Share in Social Networks• AI and the Web Track (Special track, AAAI-10)• Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda (Osaka U

niversity et al., Japan)

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Introduction• Introduction

– Web Ontology Language (OWL) plays a key role in the Semantic Web Reasoner of a query answering system

• For data: describe the meaning of the data• For user: provide answers to query

– Completeness vs. efficiency• Completeness: use ontology to provide all possible answers to a query• Efficiency: ignore ontology, just use simple matching• In practical applications, incompleteness is chosen, which lies between com

pleteness and efficiency

• Research question and challenges– How to evaluate the completeness of a semantic web reasoner?– Data is not generic and exhaustive (to provide all possible answers to a

query)– Answers may not be verifiable

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Algorithms• Ontology benchmark: Lehigh University Benchmark (LUBM)

– An ontology describing an academic domain

– Including the ontology, the testing datasets and testing queries

• Proposed framework– Step 1: generate an “n-exhaustive” testing datasets based on LUBM ontolo

gy using the proposed algorithm (SyGENiA)• The generation of testing datasets in LUBM are hard-coded and is not exhaustiv

e• Exhaustive testing datasets is proved to be impossible to generate due to expon

ential increase of computing time w.r.t. the scale of the ontology• “n-exhaustive” testing datasets can be used as an approximation to exhaustive t

esting datasets, which is derived by adding some constraints to the generation of exhaustive testing datasets

– Step 2: test the proposed “n-exhaustive” testing datasets generated by SyGENiA using some query answering systems and compare the result to that of the benchmark (LUBM)

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Results

• The results show that– For all 4 systems, the testing datasets generated by SyGENiA in

dicate more incompleteness that of LUBM– Provide a practical algorithms to generate testing datasets to tes

t the completeness of query answering systems

• For AI lab research– Build and test ontology for online text in social media (BI)

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Introduction

• Introduction– Topics diffuse among online social network by self-preference

and peer-influence

– Three aspects to consider• The diffusion of generic information (B-TFM)• The diffusion of certain topics (T-TFM)• Fading of interest on topic during diffusion (TT-TFM)

• Research questions– How to model the topic diffusion considering both self-

preference and peer-influence?

– How to analyze the diffusion of specific topics at specific time?

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Algorithms

• B-TFM– Use reply-to relationship to build adjacent matrix of social networ

k for random walk (peer-influence)– Use the number of replies of each user to measure the intensity

of participation (self-preference)– Combine peer-influence and self-preference into a single measu

re called “ParticipationRank”, updated at each time point

• T-TFM– Use LDA for topic analysis of each thread– Build separate social networks for each topic, and use the topic

strength to adjust the transition probabilities in adjacent matrices

• TT-TFM– Add time lapse factor such that the transition probabilities in the

adjacent matrix of each topic social network fade with time

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Results• Dataset: Drag Racing, Honda/Acura (Honda-tech forum)

• Task: to predict if a user will participate in the discussion of a specific topic at a certain time point by ParticipationRank

• Results show that TT-TFM performs the best

• For AI lab research– Study viral marketing in social media (BI)

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Introduction

• Introduction– Multiple opinions diffusion in social network

• Voter model

• Value-weighted voter model

– Property of value-weighted voter model• Eventually one opinion will win and others will die out

– Share of opinion• The percentage of population that hold a certain opinion

• Research questions– How to predict the share of opinions at a future time point in

social networks?

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Algorithms• The algorithm aims to estimate the weight value of each opinion by ma

ximizing the log-likelihood function of the vector of weight values

• Algorithm– Step 1: initialize all weight value to 0– Step 2: calculate the first order derivative of the log-likelihood function– Step 3: if the first order derivative is sufficiently small (below a given thresh

old), terminate. Otherwise, go to step 4– Step 4: calculate the Hessian matrix (second order derivative) and update

the vector of weight values by multiplying the inverted Hessian matrix, return to step 2

• Benchmark– Naïve linear method: simple linear regression

• Datasets (social networks)– Japanese blog networks, list of people in Japanese Wikipedia

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Results

• Results show that performance of predicting opinion shares with the proposed learning method is better

• For AI lab research– Topic/information diffusion in social media (BI/GeoPolitical)

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Introduction to IJCAI• International Joint Conferences on Artificial Intelligence (IJCAI)

– Biennial conference in summer (from 1969)– Totally 20 sessions by now– Acceptance rate: 20%~25%

• Related tracks– Web and Knowledge-based Information Systems– Natural Language Processing– Machine Learning

• Major groups are from engineering school (algorithms)– Changshui Zhang et al., Tsinghua University, China– Jieping Ye et al., Arizona State University, Arizona– Qiang Yang et al., HKUST, Hong Kong– Zhengjiang University, China– Zhi-Hua Zhou et al. Nanjing University, China– University of Illinois at Chicago, Illinois

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Selected Papers from IJCAI• IJCAI-09 distinguished paper awards

– Learning Conditional Preference Networks with Queries• Uncertainty in AI• Frédéric Koriche, Bruno Zanuttini (Université de Caen Basse-Normandie,

France)

• Other selected papers– Efficient Estimation of Influence Functions for SIS Model on Social N

etworks• Web and Knowledge-based Information Systems• Masahiro Kimura, Kazumi Saito, Hiroshi Motoda (Osaka University et al.,

Japan)

– Incorporating User Behaviors in New Word Detection• Web and Knowledge-based Information Systems• Yabin Zheng, Zhiyuan Liu, Maosong Sun, Liyun Ru, Yang Zhang (Tsingh

ua University, China)

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France)



Japan)



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– Conditional Preference Networks (CP-nets)• A graph where each node (attribute) is labelled with a table describing the user’

s preference over alternative values of this node given different values of the parent nodes

– Traditional way of building CP-nets• Select possible attributes• Asking a user for the preference of each attribute• Build the CP-net by the collected information

– Challenges• A minimal set of attributes must be selected to build the CP-net• Too many irrelevant attributes will lead to low efficiency

• Research question– How to design an efficient algorithm to build CP-net by actively feeding qu

eries (preference relationships) to the algorithm?

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Conditional Preference Networks

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Algorithms

Test if a preference relationship is consistent in N (current CP-net)If false, take the counter example| If there are rules (of a node) that involve the counter example| | Find the parent nodes of the node| | Expand the conditions of the rules using parent nodes| Else| | Add the node and the rules to NReturn N

• Advantages of the proposed algorithm– Integrates the learning and preference testing together, which are

separated in traditional way– The computational complexity is proved to be linear in the size of CP-net

and logarithmic in the number of attributes

• For AI lab research– Recommendation systems in social media (BI)

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France)


etworks• Web and Knowledge-based Information Systems• Masahiro Kimura, Kazumi Saito, Hiroshi Motoda (Osaka Universityet al.,

Japan)



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– SIS (Susceptible-Infected-Susceptible) model describes the repeated diffusion of a topic in social network

– Influence function• σ(v,t): expected number of nodes infected by v at time t when v was infected at t=0

• Research question– How to estimate the influence function of each node by effective (in terms of computationa

l time) simulation?

• Layered graph method– All vertices are presented– Only edges through which topic diffused are added at time t– The graph (edges) evolve with time

• Proposed technique and algorithms– Bond percolation (BP)– Bond percolation with pruning method: retain only one node when many nodes have exact

ly the same influence path at time t

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Algorithms

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Results

• Advantages– The influence function of all nodes are estimated simultaneously– The number of edges in the graph are significantly reduced when propa

gation probability is small

• For AI lab research– SIS/SIR model simulation in social media (BI/GeoPolitical)

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France)



Japan)



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– Why study new word detection• Try to identify out-of-vocabulary words• Useful for language with no natural word boundaries (e.g. Chinese)

– Lexicons• Cell dictionary: domain specific lexicons• User dictionary: user specific lexicons

– Word features• Coverage: how many users have used a word (popularity)• Discriminability: the ratio of popularity of a word among users from a specific

domain and users outside that specific domain

• Research question– How to detect new words in domain-specific fields based on user behavi

or?

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Algorithms

• Step 1:– Identify top n representative words from every domain

using the combination of coverage and discriminability

• Step 2:– Identify users who use the representative words very f

requently as potential experts

• Step 3:– Identify new words by their popularity among potential

experts and other users

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Results• Dataset: generated from Sogou (搜狗 ) Chinese input method

• Benchmarks: Google Sets, Bayesian Sets

• Evaluation metrics (relevant documents)– Bpref: binary preference measure– MRR: mean reciprocal rank– P@n: precision at n

• For AI lab research– Features selection for text mining in social media (BI)

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Summary

• All papers focused on algorithms development

• Possible take-away for AI lab– Topic diffusion analysis in social media for

both empirical analysis and simulation

– Feature selection using collaborative filtering

Documents

Conferences Review – AAAI and IJCAI Sean. 2 Outline Introduction to AAAI Selected papers from AAAI (3) Introduction to IJCAI Selected papers from IJCAI