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

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outline
Outline
  • Introduction to AAAI
  • Selected papers from AAAI (3)
  • Introduction to IJCAI
  • Selected papers from IJCAI (3)
  • Summary
introduction to aaai
Introduction to AAAI
  • Association for the Advancement of Artificial Intelligence conference on Artificial 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
selected papers from aaai
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 University et al., Japan)
selected papers from aaai1
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 University et al., Japan)
introduction
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 completeness 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
algorithms
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 ontology using the proposed algorithm (SyGENiA)
      • The generation of testing datasets in LUBM are hard-coded and is not exhaustive
      • Exhaustive testing datasets is proved to be impossible to generate due to exponential increase of computing time w.r.t. the scale of the ontology
      • “n-exhaustive” testing datasets can be used as an approximation to exhaustive testing 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)
results
Results
  • The results show that
    • For all 4 systems, the testing datasets generated by SyGENiA indicate more incompleteness that of LUBM
    • Provide a practical algorithms to generate testing datasets to test the completeness of query answering systems
  • For AI lab research
    • Build and test ontology for online text in social media (BI)
selected papers from aaai2
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 University et al., Japan)
introduction1
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?
algorithms1
Algorithms
  • B-TFM
    • Use reply-to relationship to build adjacent matrix of social network 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 measure 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
results1
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)
selected papers from aaai3
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 University et al., Japan)
introduction2
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?
algorithms2
Algorithms
  • The algorithm aims to estimate the weight value of each opinion by maximizing 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 threshold), 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
results2
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)
introduction to ijcai
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
selected papers from ijcai
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 Networks
      • 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 (Tsinghua University, China)
selected papers from ijcai1
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 Networks
      • 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 (Tsinghua University, China)
introduction3
Introduction
  • Introduction
    • 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 queries (preference relationships) to the algorithm?
algorithms3
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 N

Return 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)
selected papers from ijcai2
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 Networks
      • Web and Knowledge-based Information Systems
      • Masahiro Kimura, Kazumi Saito, Hiroshi Motoda (Osaka Universityet 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 (Tsinghua University, China)
introduction4
Introduction
  • Introduction
    • 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 computational 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 exactly the same influence path at time t
results3
Results
  • Advantages
    • The influence function of all nodes are estimated simultaneously
    • The number of edges in the graph are significantly reduced when propagation probability is small
  • For AI lab research
    • SIS/SIR model simulation in social media (BI/GeoPolitical)
selected papers from ijcai3
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 Networks
      • 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 (Tsinghua University, China)
introduction5
Introduction
  • Introduction
    • 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 behavior?
algorithms5
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 frequently as potential experts
  • Step 3:
    • Identify new words by their popularity among potential experts and other users
results4
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
    • [email protected]: precision at n
  • For AI lab research
    • Features selection for text mining in social media (BI)
summary
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
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